Access Sentinel-1 in Native Mode¶

xarray-eopf is a Python package that extends xarray with a custom backend called "eopf-zarr". This backend enables seamless access to ESA EOPF data products stored in the Zarr format, presenting them as analysis-ready data structures.

In this notebook, we demonstrate how to use the xarray-eopf backend to access Sentinel-1 EOPF Zarr products in native mode. All data access is lazy, meaning that data is only loaded when required—for example, during plotting or when writing to storage.

For a general introduction to the xarray EOPF backend, see the introduction notebook.

• 🐙 GitHub: EOPF Sample Service – xarray-eopf
• ❗ Issue Tracker: Submit or view issues
• 📘 Documentation: xarray-eopf Docs

---

Import Modules¶

The xarray-eopf backend is implemented as a plugin for xarray. Once installed, it registers automatically and requires no additional import. You can simply import xarray as usual:

In [1]:

import matplotlib.pyplot as plt
import pystac_client
import xarray as xr

import matplotlib.pyplot as plt import pystac_client import xarray as xr

---

Open a Sentinel-1 GRD¶

We begin with an example that accesses a Sentinel-1 GRD product in native mode.

---

Find a Sentinel-1 GRD Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-1 GRD tile.

In [2]:

%%time
catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-1-l1-grd"],
        bbox=[16, 40, 17, 41],
        datetime=["2026-03-16", "2026-03-16"],
    ).items()
)
items

%%time catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-1-l1-grd"], bbox=[16, 40, 17, 41], datetime=["2026-03-16", "2026-03-16"], ).items() ) items

CPU times: user 22.5 ms, sys: 3.07 ms, total: 25.6 ms
Wall time: 4.1 s

Out[2]:

[<Item id=S1C_IW_GRDH_1SDV_20260316T164819_20260316T164844_006792_00DBA6_AE14>,
 <Item id=S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0>,
 <Item id=S1A_IW_GRDH_1SDV_20260316T045550_20260316T045615_063648_080022_7C56>]

Next, we can inspect the item’s contents, including the additional field xarray:open_datatree_kwargs, which provides the arguments needed to open the product using Xarray’s eopf-zarr engine.

In [3]:

item = items[1]
item

item = items[1] item

Out[3]:

<Item id=S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 7 items

  • 0 "https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"

  • 1 "https://stac-extensions.github.io/sat/v1.0.0/schema.json"

  • 2 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"

  • 3 "https://stac-extensions.github.io/product/v0.1.0/schema.json"

  • 4 "https://stac-extensions.github.io/sar/v1.3.0/schema.json"

  • 5 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"

  • 6 "https://stac-extensions.github.io/version/v1.2.0/schema.json"
• id "S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 5 items

      • 0[] 2 items

        • 0 15.261218

        • 1 40.715069

      • 1[] 2 items

        • 0 18.376741

        • 1 41.126934

      • 2[] 2 items

        • 0 18.688345

        • 1 39.626194

      • 3[] 2 items

        • 0 15.640784

        • 1 39.213028

      • 4[] 2 items

        • 0 15.261218

        • 1 40.715069
• bbox[] 4 items

  • 0 15.261218

  • 1 39.213028

  • 2 18.688345

  • 3 41.126934
• properties

  • created "2026-03-16T18:51:20.575510Z"
  • mission "Sentinel-1"
  • updated "2026-03-16T18:51:20.575510Z"
  • datetime "2026-03-16T16:48:07.261108Z"
  • platform "sentinel-1c"
  • providers[] 3 items

    • 0

      • url "https://commission.europa.eu/"
      • name "European Commission"
      • roles[] 1 items

        • 0 "licensor"

    • 1

      • url "https://sentinel.esa.int/web/sentinel/missions/sentinel-1"
      • name "ESA"
      • roles[] 2 items

        • 0 "producer"

        • 1 "processor"

    • 2

      • url "https://zarr.eopf.copernicus.eu/"
      • name "EOPF Sentinel Zarr Samples Service"
      • roles[] 2 items

        • 0 "host"

        • 1 "processor"
  • published "2026-03-16T18:51:20.575510Z"
  • deprecated False
  • instruments[] 1 items

    • 0 "sar"
  • end_datetime "2026-03-16T16:48:19.760381Z"
  • product:type "S01SIWGRD"
  • constellation "sentinel-1"
  • start_datetime "2026-03-16T16:47:54.761834Z"
  • sat:orbit_state "ascending"
  • eopf:datatake_id "56230"
  • sar:product_type "GRD"
  • sat:anx_datetime "2026-03-16T16:37:16.668595Z"
  • sar:polarizations[] 2 items

    • 0 "VV"

    • 1 "VH"
  • processing:lineage "systematic"
  • processing:version "004.03"
  • product:timeliness "PT3H"
  • sar:frequency_band "C"
  • sat:absolute_orbit 6792
  • sat:relative_orbit 146
  • processing:software

    • EOPF-CPM "2.6.2"
    • Sentinel-1 IPF "004.03"
  • sar:instrument_mode "IW"
  • eopf:instrument_mode "IW"
  • sar:center_frequency 5.405
  • product:timeliness_category "NRT-3h"
  • sat:platform_international_designator "2024-235A"
• links[] 5 items

  • 0

    • rel "collection"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-1-l1-grd"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-1-l1-grd"
    • type "application/json"

  • 2

    • rel "root"
    • href "https://stac.core.eopf.eodc.eu"
    • type "application/json"
    • title "EOPF Sentinel Zarr Samples Service STAC API"

  • 3

    • rel "self"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-1-l1-grd/items/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0"
    • type "application/geo+json"

  • 4

    • rel "license"
    • href "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
    • type "application/pdf"
    • title "Legal notice on the use of Copernicus Sentinel Data and Service Information"
• assets

  • vh

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH/measurements"
    • type "application/vnd+zarr"
    • title "VH Data"
    • description "VH polarization backscattering coefficient, 16-bit DN."
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • vv

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV/measurements"
    • type "application/vnd+zarr"
    • title "VV Data"
    • description "VV polarization backscattering coefficient, 16-bit DN."
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • product

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr"
    • type "application/vnd+zarr"
    • title "EOPF Product"
    • description "The full Zarr store of the EOPF product"
    • xarray:open_datatree_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "metadata"
  • noise-vh

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH/quality/noise"
    • type "application/vnd+zarr"
    • title "VH Noise"
    • description "Estimated thermal noise look-up tables"
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "metadata"

      • 1 "dataset"
  • noise-vv

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV/quality/noise"
    • type "application/vnd+zarr"
    • title "VV Noise"
    • description "Estimated thermal noise look-up tables"
    • xarray:open_datatree_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "metadata"

      • 1 "dataset"
  • calibration-vh

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH/quality/calibration"
    • type "application/vnd+zarr"
    • title "VH Calibration"
    • description "Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration."
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "metadata"

      • 1 "dataset"
  • calibration-vv

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV/quality/calibration"
    • type "application/vnd+zarr"
    • title "VV Calibration"
    • description "Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration."
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "metadata"

      • 1 "dataset"
  • zipped_product

    • href "https://download.user.eopf.eodc.eu/zip/collections/sentinel-1-l1-grd/items/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zip"
    • type "application/zip"
    • title "Zipped EOPF Product"
    • description "The full EOPF Zarr store as zip archive"
    • roles[] 3 items

      • 0 "data"

      • 1 "metadata"

      • 2 "archive"
  • product_metadata

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s01siwgrh-eu/16/products/cpm_v262/S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr/.zmetadata"
    • type "application/json"
    • title "Consolidated Metadata"
    • description "Consolidated metadata of the EOPF product"
    • roles[] 1 items

      • 0 "metadata"
• collection "sentinel-1-l1-grd"

Open Sentinel-1 GRD in native mode as DataTree¶

We can use the "product" asset to obtain the href and xarray:open_datatree_kwargs from the STAC item, and open the product as an xarray.DataTree as shown below:

In [4]:

%%time
dt = xr.open_datatree(
    item.assets["product"].href,
    **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"]
)
dt

%%time dt = xr.open_datatree( item.assets["product"].href, **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"] ) dt

CPU times: user 718 ms, sys: 110 ms, total: 828 ms
Wall time: 5.17 s

Out[4]:

<xarray.DatasetView> Size: 0B
Dimensions:  ()
Data variables:
    *empty*
Attributes: (2)

xarray.DataTree

• Groups: (2)
  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*
  Attributes: (3)

  S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH

  • Groups: (3)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*

    conditions

    • Groups: (5)
      <xarray.DatasetView> Size: 322kB
      Dimensions:              (azimuth_time: 27, count: 730)
      Coordinates:
        * azimuth_time         (azimuth_time) datetime64[ns] 216B 2026-03-16T16:47:...
        * count                (count) int64 6kB 0 1 2 3 4 5 ... 725 726 727 728 729
      Data variables:
          elevation_angle      (azimuth_time, count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
          incidence_angle      (azimuth_time, count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
          roll                 (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
          slant_range_time_ap  (azimuth_time, count) float64 158kB dask.array<chunksize=(27, 730), meta=np.ndarray>
          swath                (azimuth_time) <U3 324B dask.array<chunksize=(27,), meta=np.ndarray>
          terrain_height       (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>

      antenna_pattern

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 27
        • count: 730
      • Coordinates: (2)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:55.425896 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time of antenna pattern measurement

          array(['2026-03-16T16:47:55.425896000', '2026-03-16T16:47:56.276897000',
                 '2026-03-16T16:47:57.222452000', '2026-03-16T16:47:58.182397000',
                 '2026-03-16T16:47:59.031342000', '2026-03-16T16:47:59.983064000',
                 '2026-03-16T16:48:00.938898000', '2026-03-16T16:48:01.791954000',
                 '2026-03-16T16:48:02.741621000', '2026-03-16T16:48:03.695399000',
                 '2026-03-16T16:48:04.548455000', '2026-03-16T16:48:05.500177000',
                 '2026-03-16T16:48:06.453956000', '2026-03-16T16:48:07.307012000',
                 '2026-03-16T16:48:08.256678000', '2026-03-16T16:48:09.216623000',
                 '2026-03-16T16:48:10.063513000', '2026-03-16T16:48:11.011124000',
                 '2026-03-16T16:48:11.966958000', '2026-03-16T16:48:12.822069000',
                 '2026-03-16T16:48:13.765569000', '2026-03-16T16:48:14.725514000',
                 '2026-03-16T16:48:15.578570000', '2026-03-16T16:48:16.526181000',
                 '2026-03-16T16:48:17.488182000', '2026-03-16T16:48:18.337127000',
                 '2026-03-16T16:48:19.280627000'], dtype='datetime64[ns]')

        • count
          (count)
          int64
          0 1 2 3 4 5 ... 725 726 727 728 729

          array([  0,   1,   2, ..., 727, 728, 729], shape=(730,))

      • Data variables: (6)
        • elevation_angle
          (azimuth_time, count)
          float32
          dask.array<chunksize=(27, 730), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'elevation angle to grid point', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          elevation angle to grid point

          units :

          degrees

          Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • incidence_angle
          (azimuth_time, count)
          float32
          dask.array<chunksize=(27, 730), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'incidence angle to grid point', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          incidence angle to grid point

          units :

          degrees

          Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • roll
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'estimated roll angle for this antenna pattern', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          estimated roll angle for this antenna pattern

          units :

          degrees

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • slant_range_time_ap
          (azimuth_time, count)
          float64
          dask.array<chunksize=(27, 730), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f8', 'long_name': 'two-way slant range time to sample', 'units': 's'}

          dtype :

          <f8

          long_name :

          two-way slant range time to sample

          units :

          s

          Array Chunk Bytes 153.98 kiB 153.98 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • swath
          (azimuth_time)
          <U3
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': 'U3', 'long_name': 'Swath'}

          dtype :

          U3

          long_name :

          Swath

          Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type

        • terrain_height
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time', 'units': 'm'}

          dtype :

          <f8

          long_name :

          mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time

          units :

          m

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 2kB
      Dimensions:       (azimuth_time: 25)
      Coordinates:
        * azimuth_time  (azimuth_time) datetime64[ns] 200B 2026-03-16T16:47:55.0000...
      Data variables:
          pitch         (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          q0            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          q1            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          q2            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          q3            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          roll          (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          wx            (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          wy            (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          wz            (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          yaw           (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>

      attitude

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 25
      • Coordinates: (1)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:55.000002 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          time stamp at which orbit state vectors apply

          array(['2026-03-16T16:47:55.000002000', '2026-03-16T16:47:55.999998000',
                 '2026-03-16T16:47:57.000002000', '2026-03-16T16:47:57.999996000',
                 '2026-03-16T16:47:59.000000000', '2026-03-16T16:47:59.999995000',
                 '2026-03-16T16:48:01.000000000', '2026-03-16T16:48:01.999995000',
                 '2026-03-16T16:48:03.000000000', '2026-03-16T16:48:04.000003000',
                 '2026-03-16T16:48:04.999998000', '2026-03-16T16:48:06.000003000',
                 '2026-03-16T16:48:06.999997000', '2026-03-16T16:48:08.000002000',
                 '2026-03-16T16:48:08.999996000', '2026-03-16T16:48:10.000001000',
                 '2026-03-16T16:48:10.999996000', '2026-03-16T16:48:12.000000000',
                 '2026-03-16T16:48:12.999995000', '2026-03-16T16:48:14.000000000',
                 '2026-03-16T16:48:14.999994000', '2026-03-16T16:48:16.000000000',
                 '2026-03-16T16:48:17.000004000', '2026-03-16T16:48:17.999998000',
                 '2026-03-16T16:48:19.000004000'], dtype='datetime64[ns]')

      • Data variables: (10)
        • pitch
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform pitch angle calculated from ancillary attitude data', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          platform pitch angle calculated from ancillary attitude data

          units :

          degrees

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • q0
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q0 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q0 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • q1
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q1 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q1 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • q2
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q2 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q2 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • q3
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q3 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q3 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • roll
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform roll angle calculated from ancillary attitude data', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          platform roll angle calculated from ancillary attitude data

          units :

          degrees

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wx
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'x component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

          dtype :

          <f8

          long_name :

          x component of angular velocity vector as extracted from ancillary attitude data

          units :

          degrees/s

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wy
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'y component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

          dtype :

          <f8

          long_name :

          y component of angular velocity vector as extracted from ancillary attitude data

          units :

          degrees/s

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wz
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'z component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

          dtype :

          <f8

          long_name :

          z component of angular velocity vector as extracted from ancillary attitude data

          units :

          degrees/s

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • yaw
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform yaw angle calculated from ancillary attitude data', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          platform yaw angle calculated from ancillary attitude data

          units :

          degrees

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 1kB
      Dimensions:                            (azimuth_time: 27, degree: 3)
      Coordinates:
        * azimuth_time                       (azimuth_time) datetime64[ns] 216B 202...
        * degree                             (degree) int64 24B 2 1 0
      Data variables:
          data_dc_polynomial                 (azimuth_time, degree) float32 324B dask.array<chunksize=(27, 3), meta=np.ndarray>
          data_dc_rms_error                  (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
          data_dc_rms_error_above_threshold  (azimuth_time) bool 27B dask.array<chunksize=(27,), meta=np.ndarray>
          fine_dce_azimuth_start_time        (azimuth_time) bool 27B dask.array<chunksize=(27,), meta=np.ndarray>
          fine_dce_azimuth_stop_time         (azimuth_time) bool 27B dask.array<chunksize=(27,), meta=np.ndarray>
          geometry_dc_polynomial             (azimuth_time, degree) float32 324B dask.array<chunksize=(27, 3), meta=np.ndarray>
          t0                                 (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>

      doppler_centroid

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 27
        • degree: 3
      • Coordinates: (2)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:56.021426 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time of terrain height measurement

          array(['2026-03-16T16:47:56.021426000', '2026-03-16T16:47:56.022979000',
                 '2026-03-16T16:47:56.035054000', '2026-03-16T16:47:58.779703000',
                 '2026-03-16T16:47:58.781256000', '2026-03-16T16:47:58.793331000',
                 '2026-03-16T16:48:01.537980000', '2026-03-16T16:48:01.539533000',
                 '2026-03-16T16:48:01.551608000', '2026-03-16T16:48:04.296257000',
                 '2026-03-16T16:48:04.297810000', '2026-03-16T16:48:04.309885000',
                 '2026-03-16T16:48:07.054533000', '2026-03-16T16:48:07.056086000',
                 '2026-03-16T16:48:07.068161000', '2026-03-16T16:48:09.812810000',
                 '2026-03-16T16:48:09.814363000', '2026-03-16T16:48:09.826438000',
                 '2026-03-16T16:48:12.571087000', '2026-03-16T16:48:12.572640000',
                 '2026-03-16T16:48:12.584715000', '2026-03-16T16:48:15.329364000',
                 '2026-03-16T16:48:15.330917000', '2026-03-16T16:48:15.342992000',
                 '2026-03-16T16:48:18.087641000', '2026-03-16T16:48:18.089194000',
                 '2026-03-16T16:48:18.101269000'], dtype='datetime64[ns]')

        • degree
          (degree)
          int64
          2 1 0

          array([2, 1, 0])

      • Data variables: (7)
        • data_dc_polynomial
          (azimuth_time, degree)
          float32
          dask.array<chunksize=(27, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

          dtype :

          <f4

          long_name :

          doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

          Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • data_dc_rms_error
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)'}

          dtype :

          <f8

          long_name :

          RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • data_dc_rms_error_above_threshold
          (azimuth_time)
          bool
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

          dtype :

          <b1

          long_name :

          false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

          Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • fine_dce_azimuth_start_time
          (azimuth_time)
          bool
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

          dtype :

          <b1

          long_name :

          false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

          Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • fine_dce_azimuth_stop_time
          (azimuth_time)
          bool
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

          dtype :

          <b1

          long_name :

          false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

          Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • geometry_dc_polynomial
          (azimuth_time, degree)
          float32
          dask.array<chunksize=(27, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

          dtype :

          <f4

          long_name :

          doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

          Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • t0
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'two-way slant range time origin for doppler centroid estimate', 'units': 's'}

          dtype :

          <f8

          long_name :

          two-way slant range time origin for doppler centroid estimate

          units :

          s

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 12kB
      Dimensions:               (azimuth_time: 10, ground_range: 21)
      Coordinates:
        * azimuth_time          (azimuth_time) datetime64[ns] 80B 2026-03-16T16:47:...
        * ground_range          (ground_range) float64 168B 0.0 ... 2.664e+05
          line                  (azimuth_time) uint32 40B dask.array<chunksize=(10,), meta=np.ndarray>
          pixel                 (ground_range) uint32 84B dask.array<chunksize=(21,), meta=np.ndarray>
      Data variables:
          azimuth_time_gcp      (azimuth_time, ground_range) datetime64[ns] 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          elevation_angle       (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          height                (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          incidence_angle       (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          latitude              (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          longitude             (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          slant_range_time_gcp  (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>

      gcp

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 10
        • ground_range: 21
      • Coordinates: (4)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:54.761834 ... 2...

          array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:57.762318000',
                 '2026-03-16T16:48:00.762803000', '2026-03-16T16:48:03.763288000',
                 '2026-03-16T16:48:06.763773000', '2026-03-16T16:48:09.764257000',
                 '2026-03-16T16:48:12.764742000', '2026-03-16T16:48:15.765227000',
                 '2026-03-16T16:48:18.765712000', '2026-03-16T16:48:19.760381000'],
                dtype='datetime64[ns]')

        • ground_range
          (ground_range)
          float64
          0.0 1.333e+04 ... 2.664e+05

          array([     0.,  13330.,  26660.,  39990.,  53320.,  66650.,  79980.,  93310.,
                 106640., 119970., 133300., 146630., 159960., 173290., 186620., 199950.,
                 213280., 226610., 239940., 253270., 266410.])

        • line
          (azimuth_time)
          uint32
          dask.array<chunksize=(10,), meta=np.ndarray>

          Array Chunk Bytes 40 B 40 B Shape (10,) (10,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

        • pixel
          (ground_range)
          uint32
          dask.array<chunksize=(21,), meta=np.ndarray>

          Array Chunk Bytes 84 B 84 B Shape (21,) (21,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

      • Data variables: (7)
        • azimuth_time_gcp
          (azimuth_time, ground_range)
          datetime64[ns]
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

        • elevation_angle
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • height
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • incidence_angle
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • latitude
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • longitude
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • slant_range_time_gcp
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 964B
      Dimensions:       (azimuth_time: 17, axis: 3)
      Coordinates:
        * axis          (axis) <U1 12B 'x' 'y' 'z'
        * azimuth_time  (azimuth_time) datetime64[ns] 136B 2026-03-16T16:46:46.6816...
      Data variables:
          position      (azimuth_time, axis) float64 408B dask.array<chunksize=(17, 3), meta=np.ndarray>
          velocity      (azimuth_time, axis) float64 408B dask.array<chunksize=(17, 3), meta=np.ndarray>

      orbit

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 17
        • axis: 3
      • Coordinates: (2)
        • axis
          (axis)
          <U1
          'x' 'y' 'z'

          array(['x', 'y', 'z'], dtype='<U1')

        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:46:46.681692 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          time stamp at which orbit state vectors apply

          array(['2026-03-16T16:46:46.681692000', '2026-03-16T16:46:56.681692000',
                 '2026-03-16T16:47:06.681692000', '2026-03-16T16:47:16.681692000',
                 '2026-03-16T16:47:26.681692000', '2026-03-16T16:47:36.681693000',
                 '2026-03-16T16:47:46.681693000', '2026-03-16T16:47:56.681692000',
                 '2026-03-16T16:48:06.681692000', '2026-03-16T16:48:16.681692000',
                 '2026-03-16T16:48:26.681692000', '2026-03-16T16:48:36.681692000',
                 '2026-03-16T16:48:46.681692000', '2026-03-16T16:48:56.681693000',
                 '2026-03-16T16:49:06.681693000', '2026-03-16T16:49:16.681692000',
                 '2026-03-16T16:49:26.681692000'], dtype='datetime64[ns]')

      • Data variables: (2)
        • position
          (azimuth_time, axis)
          float64
          dask.array<chunksize=(17, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'position vector', 'units': 'm'}

          dtype :

          <f8

          long_name :

          position vector

          units :

          m

          Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • velocity
          (azimuth_time, axis)
          float64
          dask.array<chunksize=(17, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'velocity vector', 'units': 'm/s'}

          dtype :

          <f8

          long_name :

          velocity vector

          units :

          m/s

          Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (0)

    <xarray.DatasetView> Size: 890MB
    Dimensions:       (azimuth_time: 16689, ground_range: 26642)
    Coordinates:
      * azimuth_time  (azimuth_time) datetime64[ns] 134kB 2026-03-16T16:47:54.761...
      * ground_range  (ground_range) float64 213kB 0.0 10.0 ... 2.664e+05 2.664e+05
        line          (azimuth_time) int64 134kB dask.array<chunksize=(2048,), meta=np.ndarray>
        pixel         (ground_range) int64 213kB dask.array<chunksize=(4096,), meta=np.ndarray>
    Data variables:
        grd           (azimuth_time, ground_range) uint16 889MB dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    measurements

    • Groups: (0)
    • Dimensions:
      • azimuth_time: 16689
      • ground_range: 26642
    • Coordinates: (4)
      • azimuth_time
        (azimuth_time)
        datetime64[ns]
        2026-03-16T16:47:54.761834 ... 2...

        array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
               '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
               '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
              shape=(16689,), dtype='datetime64[ns]')

      • ground_range
        (ground_range)
        float64
        0.0 10.0 ... 2.664e+05 2.664e+05

        array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
               2.6641e+05], shape=(26642,))

      • line
        (azimuth_time)
        int64
        dask.array<chunksize=(2048,), meta=np.ndarray>

        Array Chunk Bytes 130.38 kiB 16.00 kiB Shape (16689,) (2048,) Dask graph 9 chunks in 2 graph layers Data type int64 numpy.ndarray

      • pixel
        (ground_range)
        int64
        dask.array<chunksize=(4096,), meta=np.ndarray>

        Array Chunk Bytes 208.14 kiB 32.00 kiB Shape (26642,) (4096,) Dask graph 7 chunks in 2 graph layers Data type int64 numpy.ndarray

    • Data variables: (1)
      • grd
        (azimuth_time, ground_range)
        uint16
        dask.array<chunksize=(2048, 4096), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

        dtype :

        <u2

        long_name :

        measurement data set for GRD IW

        Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*

    quality

    • Groups: (3)
      <xarray.DatasetView> Size: 297kB
      Dimensions:       (azimuth_time: 27, ground_range: 668)
      Coordinates:
        * azimuth_time  (azimuth_time) datetime64[ns] 216B 2026-03-16T16:47:54.7618...
        * ground_range  (ground_range) float64 5kB 0.0 6.675e+06 ... 4.446e+09
          line          (azimuth_time) uint32 108B dask.array<chunksize=(27,), meta=np.ndarray>
          pixel         (ground_range) uint32 3kB dask.array<chunksize=(668,), meta=np.ndarray>
      Data variables:
          beta_nought   (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
          dn            (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
          gamma         (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
          sigma_nought  (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>

      calibration

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 27
        • ground_range: 668
      • Coordinates: (4)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:54.761834 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time at which calibration vector applies

          array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.761834000',
                 '2026-03-16T16:47:56.761834000', '2026-03-16T16:47:57.761834000',
                 '2026-03-16T16:47:58.761834000', '2026-03-16T16:47:59.761834000',
                 '2026-03-16T16:48:00.761834000', '2026-03-16T16:48:01.761834000',
                 '2026-03-16T16:48:02.761834000', '2026-03-16T16:48:03.761834000',
                 '2026-03-16T16:48:04.761834000', '2026-03-16T16:48:05.761834000',
                 '2026-03-16T16:48:06.761834000', '2026-03-16T16:48:07.761834000',
                 '2026-03-16T16:48:08.761834000', '2026-03-16T16:48:09.761834000',
                 '2026-03-16T16:48:10.761834000', '2026-03-16T16:48:11.761834000',
                 '2026-03-16T16:48:12.761834000', '2026-03-16T16:48:13.761834000',
                 '2026-03-16T16:48:14.761834000', '2026-03-16T16:48:15.761834000',
                 '2026-03-16T16:48:16.761834000', '2026-03-16T16:48:17.761834000',
                 '2026-03-16T16:48:18.761834000', '2026-03-16T16:48:19.761834000',
                 '2026-03-16T16:48:20.761834000'], dtype='datetime64[ns]')

        • ground_range
          (ground_range)
          float64
          0.0 6.675e+06 ... 4.446e+09

          dimensions :

          ['ground_range']

          dtype :

          <f8

          array([0.000000e+00, 6.675200e+06, 1.335040e+07, ..., 4.439008e+09,
                 4.445683e+09, 4.445850e+09], shape=(668,))

        • line
          (azimuth_time)
          uint32
          dask.array<chunksize=(27,), meta=np.ndarray>

          dimensions :

          ['azimuth_time']

          dtype :

          <u4

          long_name :

          image line at which the calibration vector applies

          Array Chunk Bytes 108 B 108 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

        • pixel
          (ground_range)
          uint32
          dask.array<chunksize=(668,), meta=np.ndarray>

          dimensions :

          ['ground_range']

          dtype :

          <u4

          long_name :

          image pixel at which the calibration vector applies (this array contains the count attribute number of integer values (i.e. one value per point in the noise vector); the maximum length of this array is one value for every pixel in an image line, however in general the vector is subsampled)

          Array Chunk Bytes 2.61 kiB 2.61 kiB Shape (668,) (668,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

      • Data variables: (4)
        • beta_nought
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • dn
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • gamma
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • sigma_nought
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 41kB
      Dimensions:             (swath: 3, azimuth_time: 1688)
      Coordinates:
          first_azimuth_time  (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          first_range_sample  (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          last_azimuth_time   (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          last_range_sample   (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          line                (swath, azimuth_time) int32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>
        * swath               (swath) <U3 36B 'IW1' 'IW2' 'IW3'
      Dimensions without coordinates: azimuth_time
      Data variables:
          noise_azimuth_lut   (swath, azimuth_time) float32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>

      noise_azimuth

      • Groups: (0)
      • Dimensions:
        • swath: 3
        • azimuth_time: 1688
      • Coordinates: (6)
        • first_azimuth_time
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          first azimuth line azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • first_range_sample
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          first range sample azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • last_azimuth_time
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          last azimuth line azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • last_range_sample
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          last range sample azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • line
          (swath, azimuth_time)
          int32
          dask.array<chunksize=(3, 1688), meta=np.ndarray>

          dimensions :

          ['swath', 'azimuth_time']

          dtype :

          <i4

          long_name :

          line

          Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • swath
          (swath)
          <U3
          'IW1' 'IW2' 'IW3'

          dimensions :

          ['swath']

          dtype :

          U3

          long_name :

          Swath

          array(['IW1', 'IW2', 'IW3'], dtype='<U3')

      • Data variables: (1)
        • noise_azimuth_lut
          (swath, azimuth_time)
          float32
          dask.array<chunksize=(3, 1688), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['line', 'swath', 'first_azimuth_time', 'last_azimuth_time', 'first_range_sample', 'last_range_sample'], 'dimensions': ['swath', 'azimuth_time'], 'dtype': '<f4', 'long_name': 'azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

          dtype :

          <f4

          long_name :

          azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)

          Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 279kB
      Dimensions:          (azimuth_time: 26, ground_range: 671)
      Coordinates:
          ground_range     (azimuth_time, ground_range) float64 140kB dask.array<chunksize=(26, 671), meta=np.ndarray>
        * azimuth_time     (azimuth_time) datetime64[ns] 208B 2026-03-16T16:47:54.7...
          line             (azimuth_time) int32 104B dask.array<chunksize=(26,), meta=np.ndarray>
          pixel            (azimuth_time, ground_range) int32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>
      Data variables:
          noise_range_lut  (azimuth_time, ground_range) float32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>

      noise_range

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 26
        • ground_range: 671
      • Coordinates: (4)
        • ground_range
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(26, 671), meta=np.ndarray>

          Array Chunk Bytes 136.30 kiB 136.30 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:54.761834 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time at which noise vector applies

          array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.762495000',
                 '2026-03-16T16:47:56.763156000', '2026-03-16T16:47:57.763817000',
                 '2026-03-16T16:47:58.764478000', '2026-03-16T16:47:59.765139000',
                 '2026-03-16T16:48:00.765800000', '2026-03-16T16:48:01.766461000',
                 '2026-03-16T16:48:02.767122000', '2026-03-16T16:48:03.767783000',
                 '2026-03-16T16:48:04.768444000', '2026-03-16T16:48:05.769104000',
                 '2026-03-16T16:48:06.769765000', '2026-03-16T16:48:07.770426000',
                 '2026-03-16T16:48:08.771087000', '2026-03-16T16:48:09.771748000',
                 '2026-03-16T16:48:10.772409000', '2026-03-16T16:48:11.773070000',
                 '2026-03-16T16:48:12.773731000', '2026-03-16T16:48:13.774392000',
                 '2026-03-16T16:48:14.775053000', '2026-03-16T16:48:15.775714000',
                 '2026-03-16T16:48:16.776375000', '2026-03-16T16:48:17.777036000',
                 '2026-03-16T16:48:18.777697000', '2026-03-16T16:48:19.760382000'],
                dtype='datetime64[ns]')

        • line
          (azimuth_time)
          int32
          dask.array<chunksize=(26,), meta=np.ndarray>

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          line

          Array Chunk Bytes 104 B 104 B Shape (26,) (26,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • pixel
          (azimuth_time, ground_range)
          int32
          dask.array<chunksize=(26, 671), meta=np.ndarray>

          Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

      • Data variables: (1)
        • noise_range_lut
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(26, 671), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'range thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

          dtype :

          <f4

          long_name :

          range thermal noise correction vector power values (this array contains the count attribute number of floating point values)

          Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (0)

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (3)

    other_metadata :

    {'azimuth_steering_rate': 1.590368784, 'downlink_information': {'azimuth_time': '2026-03-16T16:47:50.757728', 'baq_block_length': 256, 'decimation_filter_bandwidth': 56590000.0, 'ecc_number': 8, 'filter_length': 36, 'first_line_sensing_time': '2026-03-16T16:42:01.593452', 'instrument_config_id': 5, 'last_line_sensing_time': '2026-03-16T16:42:32.143255', 'mean_bit_rate': 0.2662843699455962, 'num_err_azimuth_beam_address': 0, 'num_err_baq_block_length': 0, 'num_err_baq_mode': 0, 'num_err_cal_mode': 0, 'num_err_cal_type': 0, 'num_err_calibration_beam_address': 0, 'num_err_data_take_id': 0, 'num_err_ecc_number': 0, 'num_err_elevation_beam_address': 0, 'num_err_instrument_config_id': 0, 'num_err_number_of_quads': 0, 'num_err_packet_count': 0, 'num_err_polarisation': 0, 'num_err_pri': 0, 'num_err_pri_count': 0, 'num_err_range_decimation': 0, 'num_err_rank': 0, 'num_err_rx_channel_id': 0, 'num_err_rx_gain': 0, 'num_err_sas_test_mode': 0, 'num_err_signal_type': 0, 'num_err_ssb_error_flag': 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'std_dev_pg_product_phase': 0.07609531536913908}, 'image_quality': {'image_statistics': {'output_data_mean': {'im': 0.0, 're': 64.73268}, 'output_data_std_dev': {'im': 0.0, 're': 53.807}}, 'output_data_mean_outside_nominal_range_flag': True, 'output_data_st_dev_outside_nominal_range_flag': True}, 'raw_data_analysis_quality': {'i_bias': 0.1028990969061852, 'i_bias_significance_flag': True, 'iq_gain_imbalance': 0.9886056780815125, 'iq_gain_significance_flag': True, 'iq_quadrature_departure': 0.09657777845859528, 'iq_quadrature_departure_significance_flag': True, 'q_bias': 0.1909915953874588, 'q_bias_significance_flag': True}}]}, 'range_sampling_rate': 64345238.12571428, 'raw_data_analysis': {'azimuth_time': '2026-03-16T16:47:50.757728', 'i_bias': 0.1028990969061852, 'i_bias_lower_bound': -0.007533131167292595, 'i_bias_upper_bound': 0.007533131167292595, 'i_bias_used_for_correction': 0.01635888963937759, 'iq_gain_imbalance': 0.9886056780815125, 'iq_gain_imbalance_used_for_correction': 1.007580041885376, 'iq_gain_lower_bound': 0.9988974928855896, 'iq_gain_upper_bound': 1.001101970672607, 'iq_quadrature_departure': 0.09657777845859528, 'iq_quadrature_departure_lower_bound': -0.8294360041618347, 'iq_quadrature_departure_upper_bound': 1.022565960884094, 'iq_quadrature_departure_used_for_correction': 0.05972424894571304, 'q_bias': 0.1909915953874588, 'q_bias_lower_bound': -0.007619955111294985, 'q_bias_upper_bound': 0.007619955111294985, 'q_bias_used_for_correction': 0.09456537663936615}, 'reference_replica': {'azimuth_time': '2026-03-16T16:47:51.903759', 'chirp_source': 'Nominal', 'pg_source': 'Extracted', 'time_delay': 4.291048e-07}, 'rfi': {'radio_frequency_interference': {'rfi_burst_report': {'azimuth_time': ['2026-03-16T16:47:50.757728', '2026-03-16T16:47:53.518340', '2026-03-16T16:47:56.276897', '2026-03-16T16:47:59.031342', '2026-03-16T16:48:01.791954', '2026-03-16T16:48:04.548455', '2026-03-16T16:48:07.307012', '2026-03-16T16:48:10.063513', '2026-03-16T16:48:12.822069', '2026-03-16T16:48:15.578570', '2026-03-16T16:48:18.337127', '2026-03-16T16:47:51.705339', '2026-03-16T16:47:54.463896', '2026-03-16T16:47:57.222452', '2026-03-16T16:47:59.983064', '2026-03-16T16:48:02.741621', '2026-03-16T16:48:05.500177', '2026-03-16T16:48:08.256678', '2026-03-16T16:48:11.011124', '2026-03-16T16:48:13.765569', '2026-03-16T16:48:16.526181', '2026-03-16T16:48:19.280627', '2026-03-16T16:47:52.669395', '2026-03-16T16:47:55.425896', '2026-03-16T16:47:58.182397', '2026-03-16T16:48:00.938898', '2026-03-16T16:48:03.695399', '2026-03-16T16:48:06.453956', '2026-03-16T16:48:09.216623', '2026-03-16T16:48:11.966958', '2026-03-16T16:48:14.725514', '2026-03-16T16:48:17.488182', '2026-03-16T16:48:20.242627'], 'frequency_domain': {'max_percentage_affected_bw': [1.779343, 1.556925, 1.556925, 1.556925, 1.556925, 2.001761, 1.556925, 1.334507, 1.334507, 1.112089, 1.334507, 1.765761, 5.738722, 4.419001, 1.549639, 6.400883, 9.049524, 1.765761, 1.545041, 1.324321, 1.545041, 1.765761, 2.127193, 2.565789, 2.560307, 1.716009, 2.143641, 2.357456, 1.929825, 1.502193, 2.143641, 3.201754, 2.560307], 'max_percentage_bw_affected_persistent_rfi': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'num_sub_blocks': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], 'percentage_affected_lines': [48.47839, 54.44114, 50.395, 38.0306, 38.16402, 41.27047, 33.98818, 23.24022, 22.43002, 21.13437, 19.9457, 35.06824, 32.37559, 30.55374, 38.38581, 25.57141, 34.89888, 42.50509, 41.70071, 33.23726, 31.23523, 34.38243, 62.29629, 66.96047, 47.63647, 39.87685, 39.87372, 24.71303, 12.85819, 16.95723, 32.61474, 72.37092, 92.04929], 'percentage_blocks_persistent_rfi': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'sub_block_size': [537, 537, 537, 537, 537, 537, 537, 537, 537, 537, 537, 583, 583, 583, 583, 583, 583, 583, 583, 583, 583, 583, 537, 537, 537, 537, 537, 537, 537, 537, 537, 537, 537]}, 'in_band_out_band_power_ratio': [6.040483, 5.885845, 5.770032, 5.33534, 5.739498, 6.480892, 6.939073, 6.906009, 7.050421, 6.340687, 5.752688, 3.835001, 3.319923, 2.069375, 1.427945, 1.739582, 2.307185, 4.2729, 5.899227, 5.842138, 5.231568, 4.665253, 1.280412, 1.344367, 2.986798, 4.159164, 4.051703, 4.874695, 5.00666, 4.071457, 2.378007, 1.351661, 1.04332], 'time_domain': {'avg_percentage_affected_samples': [0.03072282, 0.02541306, 0.02900477, 0.02216469, 0.02230422, 0.0327036, 0.02128726, 0.0310719, 0.03200946, 0.02720804, 0.03276924, 0.02132807, 0.03892438, 0.02845037, 0.02325107, 0.1596922, 0.374319, 0.02000132, 0.01872421, 0.03915716, 0.02229234, 0.03514876, 0.03153514, 0.02917108, 0.03309658, 0.0319356, 0.02673698, 0.04061222, 0.02720815, 0.04185823, 0.06881663, 0.02263948, 0.02056231], 'max_percentage_affected_samples': [0.4939305, 1.025534, 0.9418166, 0.4897447, 0.5776476, 1.075764, 0.2637087, 0.1632482, 0.3223106, 0.1339473, 0.2469653, 0.5792304, 3.814646, 0.7405875, 0.2606537, 3.971866, 12.42036, 0.3764998, 0.2482416, 0.3020273, 0.3227141, 0.8895324, 0.6812677, 1.175817, 1.221235, 0.6055713, 0.6711748, 1.145539, 0.6308034, 3.174202, 4.784013, 0.2422285, 0.3381106], 'percentage_affected_lines': [22.35628, 40.88006, 53.15826, 36.55075, 43.86089, 34.13769, 10.78779, 1.84528, 2.413059, 1.84528, 2.484031, 56.26615, 66.02068, 72.15762, 41.79586, 16.21447, 21.44703, 21.83463, 10.07752, 1.808785, 8.65633, 14.21189, 71.20567, 65.60284, 77.0922, 66.52482, 55.17731, 34.18439, 23.5461, 40.92199, 56.80851, 15.46099, 9.503547]}}, 'rfi_detection_from_noise_report': {'max_fisher_z': [29.50416, 4.590335, 27.0361, 9.515205, 6.524429, 8.818636, 4.148534, 3.308747, 3.465118, 10.62549, 5.714411, 14.31397, 3.102887, 3.137011, 4.052643, 5.175268, 17.6979, 2.843202], 'max_kl_divergence': [259975.0, 4.807519, 279972.0, 19.79907, 9.58111, 17.15115, 3.910321, 2.868378, 2.873281, 10023.04, 7.236189, 149995.0, 2.383656, 2.435508, 3.756703, 6.133147, 289971.0, 1.960016], 'max_rfi_psd': [42.35976, 0.0, 32.62078, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 21.42714, 0.0, 0.0, 0.0, 0.0, 27.95378, 0.0], 'noise_sensing_time': ['2026-03-16T16:47:52.722175', '2026-03-16T16:47:58.238731', '2026-03-16T16:48:03.755271', '2026-03-16T16:48:09.271827', '2026-03-16T16:48:14.788383', '2026-03-16T16:48:20.304939', '2026-03-16T16:47:53.799354', '2026-03-16T16:47:59.315910', '2026-03-16T16:48:04.832466', '2026-03-16T16:48:10.349021', '2026-03-16T16:48:15.865562', '2026-03-16T16:48:21.382118', '2026-03-16T16:47:54.648155', '2026-03-16T16:48:00.164710', '2026-03-16T16:48:05.681266', '2026-03-16T16:48:11.197822', '2026-03-16T16:48:16.714378', '2026-03-16T16:48:22.230934'], 'rfi_detected': ['true', 'false', 'true', 'false', 'false', 'false', 'false', 'false', 'false', 'false', 'false', 'true', 'false', 'false', 'false', 'false', 'true', 'false']}}}, 'swath_merging': {'swath_merge_list': [{'swath': 'IW1', 'swath_bounds_list': [{'azimuth_time': '2026-03-16T16:47:54.761834', 'first_azimuth_line': 0, 'first_range_sample': 0, 'last_azimuth_line': 16688, 'last_range_sample': 8871}]}, {'swath': 'IW2', 'swath_bounds_list': [{'azimuth_time': '2026-03-16T16:47:54.761834', 'first_azimuth_line': 0, 'first_range_sample': 8872, 'last_azimuth_line': 16688, 'last_range_sample': 17677}]}, {'swath': 'IW3', 'swath_bounds_list': [{'azimuth_time': '2026-03-16T16:47:54.761834', 'first_azimuth_line': 0, 'first_range_sample': 17678, 'last_azimuth_line': 16688, 'last_range_sample': 26641}]}]}, 'swath_timing': {'burst_list': {}, 'lines_per_burst': 0, 'samples_per_burst': 0}, 'timeliness_category': 'NRT-3h', 'title': 'S01SIWGRD'}

    processing_history :

    {'Level-0 Product': [{'facility': 'Unknown', 'inputs': [], 'outputs': ['Downlinked Stream'], 'processor': 'Unknown', 'time': '2026-03-16T16:44:59.039521Z', 'version': 'Unknown'}, {'facility': 'Unknown', 'inputs': [], 'outputs': ['Downlinked Stream'], 'processor': 'Unknown', 'time': '2026-03-16T16:45:26.324421Z', 'version': 'Unknown'}, {'facility': 'Unknown', 'inputs': ['S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Downlinked Stream'], 'outputs': ['Unknown'], 'processor': 'Unknown', 'time': '2026-03-16T17:07:12.306670Z', 'version': 'Unknown'}, {'facility': 'Unknown', 'inputs': ['S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Downlinked Stream'], 'outputs': ['Unknown'], 'processor': 'Unknown', 'time': '2026-03-16T17:12:02.055507Z', 'version': 'Unknown'}, {'facility': 'ESA-S1C-PS', 'inputs': ['S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Unknown', 'Unknown'], 'outputs': ['/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0SDV_20260316T164751_20260316T164823_006792_00DBA6_DA02.SAFE'], 'processor': '', 'time': '2026-03-16T17:15:28.685180Z', 'version': ''}], 'Level-1 Intermediate SLC Product': [{'adfs': ['/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_AUX_PP1_V20250427T000000_G20260203T152141.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_AUX_CAL_V20250427T000000_G20260212T132118.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_AUX_INS_V20250224T000000_G20251015T082332.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_OPER_AUX_PREORB_OPOD_20260316T170705_V20260316T163717_20260316T231217.EOF'], 'facility': 'ESA-S1C-PS', 'inputs': ['/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0ADV_20260316T164201_20260316T165701_006792_00DBA6_2189.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0CDV_20260316T164201_20260316T165701_006792_00DBA6_9E95.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0NDV_20260316T164201_20260316T165701_006792_00DBA6_97E5.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0SDV_20260316T164751_20260316T164823_006792_00DBA6_DA02.SAFE', 'Sentinel-1 Product Definition (S1-RS-MDA-52-7440) release 2/7', 'Sentinel-1 Product Specification (S1-RS-MDA-52-7441) release 3/14'], 'outputs': ['S1C_IW_SL1__1_DV_20260316T164751_20260316T164824_006792_00DBA6_1763.SAFE'], 'processor': 'Sentinel-1 IPF', 'time': '2026-03-16T17:55:39.000000Z', 'version': '004.03'}], 'Level-1 Product': [{'facility': 'ESA-S1C-PS', 'inputs': ['S1C_IW_SL1__1_DV_20260316T164751_20260316T164824_006792_00DBA6_1763.SAFE'], 'outputs': ['S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.SAFE'], 'processor': 'Sentinel-1 IPF', 'time': '2026-03-16T17:57:14.000000Z', 'version': '004.03'}, {'facility': 'CS-SopraSteria', 'inputs': ['S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.SAFE'], 'outputs': ['S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr.zarr'], 'processor': 'EOPF-CPM', 'time': '2026-03-16T18:51:01.944012Z', 'version': '2.6.2'}]}

    stac_discovery :

    {'assets': {}, 'bbox': [18.688345, 39.213028, 15.261218, 41.126934], 'detectionPerformed': 'true', 'geometry': {'coordinates': [[[15.261218, 40.715069], [18.376741, 41.126934], [18.688345, 39.626194], [15.640784, 39.213028], [15.261218, 40.715069]]], 'type': 'Polygon'}, 'id': 'S01SIWGRD_20260316T164754_0024_C146_TF31', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'constellation': 'sentinel-1', 'created': '2026-03-16T17:57:14.000000Z', 'datetime': 'None', 'end_datetime': '2026-03-16T16:48:19.760381Z', 'instruments': ['sar'], 'platform': 'sentinel-1c', 'processing:software': {'Sentinel-1 IPF': '004.03'}, 'processing:version': '004.00', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT-3h', 'product:type': 'S01SIWGRD', 'sar:center_frequency': 5405000454.33435, 'sar:frequency_band': 'C', 'sar:instrument_mode': 'IW', 'sar:pixel_spacing_azimuth': 10.0, 'sar:pixel_spacing_range': 10.0, 'sat:absolute_orbit': 6792, 'sat:anx_datetime': '2026-03-16T16:37:16.668595', 'sat:orbit_state': 'ascending', 'sat:platform_international_designator': '2024-235A', 'sat:relative_orbit': 146, 'start_datetime': '2026-03-16T16:47:54.761834Z', 'view:incidence_angle': 39.13511618906348}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/sar/v1.2.0/schema.json'], 'stac_version': '1.1.0', 'thermalNoiseCorrectionPerformed': 'false', 'type': 'Feature'}

  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*
  Attributes: (3)

  S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV

  • Groups: (3)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*

    conditions

    • Groups: (5)
      <xarray.DatasetView> Size: 322kB
      Dimensions:              (azimuth_time: 27, count: 730)
      Coordinates:
        * azimuth_time         (azimuth_time) datetime64[ns] 216B 2026-03-16T16:47:...
        * count                (count) int64 6kB 0 1 2 3 4 5 ... 725 726 727 728 729
      Data variables:
          elevation_angle      (azimuth_time, count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
          incidence_angle      (azimuth_time, count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
          roll                 (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
          slant_range_time_ap  (azimuth_time, count) float64 158kB dask.array<chunksize=(27, 730), meta=np.ndarray>
          swath                (azimuth_time) <U3 324B dask.array<chunksize=(27,), meta=np.ndarray>
          terrain_height       (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>

      antenna_pattern

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 27
        • count: 730
      • Coordinates: (2)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:55.425896 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time of antenna pattern measurement

          array(['2026-03-16T16:47:55.425896000', '2026-03-16T16:47:56.276897000',
                 '2026-03-16T16:47:57.222452000', '2026-03-16T16:47:58.182397000',
                 '2026-03-16T16:47:59.031342000', '2026-03-16T16:47:59.983064000',
                 '2026-03-16T16:48:00.938898000', '2026-03-16T16:48:01.791954000',
                 '2026-03-16T16:48:02.741621000', '2026-03-16T16:48:03.695399000',
                 '2026-03-16T16:48:04.548455000', '2026-03-16T16:48:05.500177000',
                 '2026-03-16T16:48:06.453956000', '2026-03-16T16:48:07.307012000',
                 '2026-03-16T16:48:08.256678000', '2026-03-16T16:48:09.216623000',
                 '2026-03-16T16:48:10.063513000', '2026-03-16T16:48:11.011124000',
                 '2026-03-16T16:48:11.966958000', '2026-03-16T16:48:12.822069000',
                 '2026-03-16T16:48:13.765569000', '2026-03-16T16:48:14.725514000',
                 '2026-03-16T16:48:15.578570000', '2026-03-16T16:48:16.526181000',
                 '2026-03-16T16:48:17.488182000', '2026-03-16T16:48:18.337127000',
                 '2026-03-16T16:48:19.280627000'], dtype='datetime64[ns]')

        • count
          (count)
          int64
          0 1 2 3 4 5 ... 725 726 727 728 729

          array([  0,   1,   2, ..., 727, 728, 729], shape=(730,))

      • Data variables: (6)
        • elevation_angle
          (azimuth_time, count)
          float32
          dask.array<chunksize=(27, 730), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'elevation angle to grid point', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          elevation angle to grid point

          units :

          degrees

          Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • incidence_angle
          (azimuth_time, count)
          float32
          dask.array<chunksize=(27, 730), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'incidence angle to grid point', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          incidence angle to grid point

          units :

          degrees

          Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • roll
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'estimated roll angle for this antenna pattern', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          estimated roll angle for this antenna pattern

          units :

          degrees

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • slant_range_time_ap
          (azimuth_time, count)
          float64
          dask.array<chunksize=(27, 730), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f8', 'long_name': 'two-way slant range time to sample', 'units': 's'}

          dtype :

          <f8

          long_name :

          two-way slant range time to sample

          units :

          s

          Array Chunk Bytes 153.98 kiB 153.98 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • swath
          (azimuth_time)
          <U3
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': 'U3', 'long_name': 'Swath'}

          dtype :

          U3

          long_name :

          Swath

          Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type

        • terrain_height
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time', 'units': 'm'}

          dtype :

          <f8

          long_name :

          mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time

          units :

          m

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 2kB
      Dimensions:       (azimuth_time: 25)
      Coordinates:
        * azimuth_time  (azimuth_time) datetime64[ns] 200B 2026-03-16T16:47:55.0000...
      Data variables:
          pitch         (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          q0            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          q1            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          q2            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          q3            (azimuth_time) float32 100B dask.array<chunksize=(25,), meta=np.ndarray>
          roll          (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          wx            (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          wy            (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          wz            (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>
          yaw           (azimuth_time) float64 200B dask.array<chunksize=(25,), meta=np.ndarray>

      attitude

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 25
      • Coordinates: (1)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:55.000002 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          time stamp at which orbit state vectors apply

          array(['2026-03-16T16:47:55.000002000', '2026-03-16T16:47:55.999998000',
                 '2026-03-16T16:47:57.000002000', '2026-03-16T16:47:57.999996000',
                 '2026-03-16T16:47:59.000000000', '2026-03-16T16:47:59.999995000',
                 '2026-03-16T16:48:01.000000000', '2026-03-16T16:48:01.999995000',
                 '2026-03-16T16:48:03.000000000', '2026-03-16T16:48:04.000003000',
                 '2026-03-16T16:48:04.999998000', '2026-03-16T16:48:06.000003000',
                 '2026-03-16T16:48:06.999997000', '2026-03-16T16:48:08.000002000',
                 '2026-03-16T16:48:08.999996000', '2026-03-16T16:48:10.000001000',
                 '2026-03-16T16:48:10.999996000', '2026-03-16T16:48:12.000000000',
                 '2026-03-16T16:48:12.999995000', '2026-03-16T16:48:14.000000000',
                 '2026-03-16T16:48:14.999994000', '2026-03-16T16:48:16.000000000',
                 '2026-03-16T16:48:17.000004000', '2026-03-16T16:48:17.999998000',
                 '2026-03-16T16:48:19.000004000'], dtype='datetime64[ns]')

      • Data variables: (10)
        • pitch
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform pitch angle calculated from ancillary attitude data', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          platform pitch angle calculated from ancillary attitude data

          units :

          degrees

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • q0
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q0 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q0 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • q1
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q1 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q1 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • q2
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q2 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q2 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • q3
          (azimuth_time)
          float32
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q3 attitude quaternion as extracted from ancillary attitude data'}

          dtype :

          <f4

          long_name :

          q3 attitude quaternion as extracted from ancillary attitude data

          Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • roll
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform roll angle calculated from ancillary attitude data', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          platform roll angle calculated from ancillary attitude data

          units :

          degrees

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wx
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'x component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

          dtype :

          <f8

          long_name :

          x component of angular velocity vector as extracted from ancillary attitude data

          units :

          degrees/s

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wy
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'y component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

          dtype :

          <f8

          long_name :

          y component of angular velocity vector as extracted from ancillary attitude data

          units :

          degrees/s

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wz
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'z component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

          dtype :

          <f8

          long_name :

          z component of angular velocity vector as extracted from ancillary attitude data

          units :

          degrees/s

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • yaw
          (azimuth_time)
          float64
          dask.array<chunksize=(25,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform yaw angle calculated from ancillary attitude data', 'units': 'degrees'}

          dtype :

          <f8

          long_name :

          platform yaw angle calculated from ancillary attitude data

          units :

          degrees

          Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 1kB
      Dimensions:                            (azimuth_time: 27, degree: 3)
      Coordinates:
        * azimuth_time                       (azimuth_time) datetime64[ns] 216B 202...
        * degree                             (degree) int64 24B 2 1 0
      Data variables:
          data_dc_polynomial                 (azimuth_time, degree) float32 324B dask.array<chunksize=(27, 3), meta=np.ndarray>
          data_dc_rms_error                  (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
          data_dc_rms_error_above_threshold  (azimuth_time) bool 27B dask.array<chunksize=(27,), meta=np.ndarray>
          fine_dce_azimuth_start_time        (azimuth_time) bool 27B dask.array<chunksize=(27,), meta=np.ndarray>
          fine_dce_azimuth_stop_time         (azimuth_time) bool 27B dask.array<chunksize=(27,), meta=np.ndarray>
          geometry_dc_polynomial             (azimuth_time, degree) float32 324B dask.array<chunksize=(27, 3), meta=np.ndarray>
          t0                                 (azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>

      doppler_centroid

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 27
        • degree: 3
      • Coordinates: (2)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:56.021426 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time of terrain height measurement

          array(['2026-03-16T16:47:56.021426000', '2026-03-16T16:47:56.022979000',
                 '2026-03-16T16:47:56.035054000', '2026-03-16T16:47:58.779703000',
                 '2026-03-16T16:47:58.781256000', '2026-03-16T16:47:58.793331000',
                 '2026-03-16T16:48:01.537980000', '2026-03-16T16:48:01.539533000',
                 '2026-03-16T16:48:01.551608000', '2026-03-16T16:48:04.296257000',
                 '2026-03-16T16:48:04.297810000', '2026-03-16T16:48:04.309885000',
                 '2026-03-16T16:48:07.054533000', '2026-03-16T16:48:07.056086000',
                 '2026-03-16T16:48:07.068161000', '2026-03-16T16:48:09.812810000',
                 '2026-03-16T16:48:09.814363000', '2026-03-16T16:48:09.826438000',
                 '2026-03-16T16:48:12.571087000', '2026-03-16T16:48:12.572640000',
                 '2026-03-16T16:48:12.584715000', '2026-03-16T16:48:15.329364000',
                 '2026-03-16T16:48:15.330917000', '2026-03-16T16:48:15.342992000',
                 '2026-03-16T16:48:18.087641000', '2026-03-16T16:48:18.089194000',
                 '2026-03-16T16:48:18.101269000'], dtype='datetime64[ns]')

        • degree
          (degree)
          int64
          2 1 0

          array([2, 1, 0])

      • Data variables: (7)
        • data_dc_polynomial
          (azimuth_time, degree)
          float32
          dask.array<chunksize=(27, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

          dtype :

          <f4

          long_name :

          doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

          Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • data_dc_rms_error
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)'}

          dtype :

          <f8

          long_name :

          RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • data_dc_rms_error_above_threshold
          (azimuth_time)
          bool
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

          dtype :

          <b1

          long_name :

          false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

          Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • fine_dce_azimuth_start_time
          (azimuth_time)
          bool
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

          dtype :

          <b1

          long_name :

          false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

          Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • fine_dce_azimuth_stop_time
          (azimuth_time)
          bool
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

          dtype :

          <b1

          long_name :

          false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

          Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • geometry_dc_polynomial
          (azimuth_time, degree)
          float32
          dask.array<chunksize=(27, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

          dtype :

          <f4

          long_name :

          doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

          Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • t0
          (azimuth_time)
          float64
          dask.array<chunksize=(27,), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'two-way slant range time origin for doppler centroid estimate', 'units': 's'}

          dtype :

          <f8

          long_name :

          two-way slant range time origin for doppler centroid estimate

          units :

          s

          Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 12kB
      Dimensions:               (azimuth_time: 10, ground_range: 21)
      Coordinates:
        * azimuth_time          (azimuth_time) datetime64[ns] 80B 2026-03-16T16:47:...
        * ground_range          (ground_range) float64 168B 0.0 ... 2.664e+05
          line                  (azimuth_time) uint32 40B dask.array<chunksize=(10,), meta=np.ndarray>
          pixel                 (ground_range) uint32 84B dask.array<chunksize=(21,), meta=np.ndarray>
      Data variables:
          azimuth_time_gcp      (azimuth_time, ground_range) datetime64[ns] 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          elevation_angle       (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          height                (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          incidence_angle       (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          latitude              (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          longitude             (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>
          slant_range_time_gcp  (azimuth_time, ground_range) float64 2kB dask.array<chunksize=(10, 21), meta=np.ndarray>

      gcp

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 10
        • ground_range: 21
      • Coordinates: (4)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:54.761834 ... 2...

          array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:57.762318000',
                 '2026-03-16T16:48:00.762803000', '2026-03-16T16:48:03.763288000',
                 '2026-03-16T16:48:06.763773000', '2026-03-16T16:48:09.764257000',
                 '2026-03-16T16:48:12.764742000', '2026-03-16T16:48:15.765227000',
                 '2026-03-16T16:48:18.765712000', '2026-03-16T16:48:19.760381000'],
                dtype='datetime64[ns]')

        • ground_range
          (ground_range)
          float64
          0.0 1.333e+04 ... 2.664e+05

          array([     0.,  13330.,  26660.,  39990.,  53320.,  66650.,  79980.,  93310.,
                 106640., 119970., 133300., 146630., 159960., 173290., 186620., 199950.,
                 213280., 226610., 239940., 253270., 266410.])

        • line
          (azimuth_time)
          uint32
          dask.array<chunksize=(10,), meta=np.ndarray>

          Array Chunk Bytes 40 B 40 B Shape (10,) (10,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

        • pixel
          (ground_range)
          uint32
          dask.array<chunksize=(21,), meta=np.ndarray>

          Array Chunk Bytes 84 B 84 B Shape (21,) (21,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

      • Data variables: (7)
        • azimuth_time_gcp
          (azimuth_time, ground_range)
          datetime64[ns]
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

        • elevation_angle
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • height
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • incidence_angle
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • latitude
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • longitude
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • slant_range_time_gcp
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(10, 21), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

          Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 964B
      Dimensions:       (azimuth_time: 17, axis: 3)
      Coordinates:
        * axis          (axis) <U1 12B 'x' 'y' 'z'
        * azimuth_time  (azimuth_time) datetime64[ns] 136B 2026-03-16T16:46:46.6816...
      Data variables:
          position      (azimuth_time, axis) float64 408B dask.array<chunksize=(17, 3), meta=np.ndarray>
          velocity      (azimuth_time, axis) float64 408B dask.array<chunksize=(17, 3), meta=np.ndarray>

      orbit

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 17
        • axis: 3
      • Coordinates: (2)
        • axis
          (axis)
          <U1
          'x' 'y' 'z'

          array(['x', 'y', 'z'], dtype='<U1')

        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:46:46.681692 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          time stamp at which orbit state vectors apply

          array(['2026-03-16T16:46:46.681692000', '2026-03-16T16:46:56.681692000',
                 '2026-03-16T16:47:06.681692000', '2026-03-16T16:47:16.681692000',
                 '2026-03-16T16:47:26.681692000', '2026-03-16T16:47:36.681693000',
                 '2026-03-16T16:47:46.681693000', '2026-03-16T16:47:56.681692000',
                 '2026-03-16T16:48:06.681692000', '2026-03-16T16:48:16.681692000',
                 '2026-03-16T16:48:26.681692000', '2026-03-16T16:48:36.681692000',
                 '2026-03-16T16:48:46.681692000', '2026-03-16T16:48:56.681693000',
                 '2026-03-16T16:49:06.681693000', '2026-03-16T16:49:16.681692000',
                 '2026-03-16T16:49:26.681692000'], dtype='datetime64[ns]')

      • Data variables: (2)
        • position
          (azimuth_time, axis)
          float64
          dask.array<chunksize=(17, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'position vector', 'units': 'm'}

          dtype :

          <f8

          long_name :

          position vector

          units :

          m

          Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • velocity
          (azimuth_time, axis)
          float64
          dask.array<chunksize=(17, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'velocity vector', 'units': 'm/s'}

          dtype :

          <f8

          long_name :

          velocity vector

          units :

          m/s

          Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (0)

    <xarray.DatasetView> Size: 890MB
    Dimensions:       (azimuth_time: 16689, ground_range: 26642)
    Coordinates:
      * azimuth_time  (azimuth_time) datetime64[ns] 134kB 2026-03-16T16:47:54.761...
      * ground_range  (ground_range) float64 213kB 0.0 10.0 ... 2.664e+05 2.664e+05
        line          (azimuth_time) int64 134kB dask.array<chunksize=(2048,), meta=np.ndarray>
        pixel         (ground_range) int64 213kB dask.array<chunksize=(4096,), meta=np.ndarray>
    Data variables:
        grd           (azimuth_time, ground_range) uint16 889MB dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    measurements

    • Groups: (0)
    • Dimensions:
      • azimuth_time: 16689
      • ground_range: 26642
    • Coordinates: (4)
      • azimuth_time
        (azimuth_time)
        datetime64[ns]
        2026-03-16T16:47:54.761834 ... 2...

        array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
               '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
               '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
              shape=(16689,), dtype='datetime64[ns]')

      • ground_range
        (ground_range)
        float64
        0.0 10.0 ... 2.664e+05 2.664e+05

        array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
               2.6641e+05], shape=(26642,))

      • line
        (azimuth_time)
        int64
        dask.array<chunksize=(2048,), meta=np.ndarray>

        Array Chunk Bytes 130.38 kiB 16.00 kiB Shape (16689,) (2048,) Dask graph 9 chunks in 2 graph layers Data type int64 numpy.ndarray

      • pixel
        (ground_range)
        int64
        dask.array<chunksize=(4096,), meta=np.ndarray>

        Array Chunk Bytes 208.14 kiB 32.00 kiB Shape (26642,) (4096,) Dask graph 7 chunks in 2 graph layers Data type int64 numpy.ndarray

    • Data variables: (1)
      • grd
        (azimuth_time, ground_range)
        uint16
        dask.array<chunksize=(2048, 4096), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

        dtype :

        <u2

        long_name :

        measurement data set for GRD IW

        Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*

    quality

    • Groups: (3)
      <xarray.DatasetView> Size: 297kB
      Dimensions:       (azimuth_time: 27, ground_range: 668)
      Coordinates:
        * azimuth_time  (azimuth_time) datetime64[ns] 216B 2026-03-16T16:47:54.7618...
        * ground_range  (ground_range) float64 5kB 0.0 6.675e+06 ... 4.446e+09
          line          (azimuth_time) uint32 108B dask.array<chunksize=(27,), meta=np.ndarray>
          pixel         (ground_range) uint32 3kB dask.array<chunksize=(668,), meta=np.ndarray>
      Data variables:
          beta_nought   (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
          dn            (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
          gamma         (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
          sigma_nought  (azimuth_time, ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>

      calibration

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 27
        • ground_range: 668
      • Coordinates: (4)
        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:54.761834 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time at which calibration vector applies

          array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.761834000',
                 '2026-03-16T16:47:56.761834000', '2026-03-16T16:47:57.761834000',
                 '2026-03-16T16:47:58.761834000', '2026-03-16T16:47:59.761834000',
                 '2026-03-16T16:48:00.761834000', '2026-03-16T16:48:01.761834000',
                 '2026-03-16T16:48:02.761834000', '2026-03-16T16:48:03.761834000',
                 '2026-03-16T16:48:04.761834000', '2026-03-16T16:48:05.761834000',
                 '2026-03-16T16:48:06.761834000', '2026-03-16T16:48:07.761834000',
                 '2026-03-16T16:48:08.761834000', '2026-03-16T16:48:09.761834000',
                 '2026-03-16T16:48:10.761834000', '2026-03-16T16:48:11.761834000',
                 '2026-03-16T16:48:12.761834000', '2026-03-16T16:48:13.761834000',
                 '2026-03-16T16:48:14.761834000', '2026-03-16T16:48:15.761834000',
                 '2026-03-16T16:48:16.761834000', '2026-03-16T16:48:17.761834000',
                 '2026-03-16T16:48:18.761834000', '2026-03-16T16:48:19.761834000',
                 '2026-03-16T16:48:20.761834000'], dtype='datetime64[ns]')

        • ground_range
          (ground_range)
          float64
          0.0 6.675e+06 ... 4.446e+09

          dimensions :

          ['ground_range']

          dtype :

          <f8

          array([0.000000e+00, 6.675200e+06, 1.335040e+07, ..., 4.439008e+09,
                 4.445683e+09, 4.445850e+09], shape=(668,))

        • line
          (azimuth_time)
          uint32
          dask.array<chunksize=(27,), meta=np.ndarray>

          dimensions :

          ['azimuth_time']

          dtype :

          <u4

          long_name :

          image line at which the calibration vector applies

          Array Chunk Bytes 108 B 108 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

        • pixel
          (ground_range)
          uint32
          dask.array<chunksize=(668,), meta=np.ndarray>

          dimensions :

          ['ground_range']

          dtype :

          <u4

          long_name :

          image pixel at which the calibration vector applies (this array contains the count attribute number of integer values (i.e. one value per point in the noise vector); the maximum length of this array is one value for every pixel in an image line, however in general the vector is subsampled)

          Array Chunk Bytes 2.61 kiB 2.61 kiB Shape (668,) (668,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

      • Data variables: (4)
        • beta_nought
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • dn
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • gamma
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • sigma_nought
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(27, 668), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

          dtype :

          <f4

          long_name :

          sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

          Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 41kB
      Dimensions:             (swath: 3, azimuth_time: 1688)
      Coordinates:
          first_azimuth_time  (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          first_range_sample  (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          last_azimuth_time   (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          last_range_sample   (swath) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>
          line                (swath, azimuth_time) int32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>
        * swath               (swath) <U3 36B 'IW1' 'IW2' 'IW3'
      Dimensions without coordinates: azimuth_time
      Data variables:
          noise_azimuth_lut   (swath, azimuth_time) float32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>

      noise_azimuth

      • Groups: (0)
      • Dimensions:
        • swath: 3
        • azimuth_time: 1688
      • Coordinates: (6)
        • first_azimuth_time
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          first azimuth line azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • first_range_sample
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          first range sample azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • last_azimuth_time
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          last azimuth line azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • last_range_sample
          (swath)
          int32
          dask.array<chunksize=(3,), meta=np.ndarray>

          dtype :

          <i4

          long_name :

          last range sample azimuth thermal noise correction

          Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • line
          (swath, azimuth_time)
          int32
          dask.array<chunksize=(3, 1688), meta=np.ndarray>

          dimensions :

          ['swath', 'azimuth_time']

          dtype :

          <i4

          long_name :

          line

          Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • swath
          (swath)
          <U3
          'IW1' 'IW2' 'IW3'

          dimensions :

          ['swath']

          dtype :

          U3

          long_name :

          Swath

          array(['IW1', 'IW2', 'IW3'], dtype='<U3')

      • Data variables: (1)
        • noise_azimuth_lut
          (swath, azimuth_time)
          float32
          dask.array<chunksize=(3, 1688), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['line', 'swath', 'first_azimuth_time', 'last_azimuth_time', 'first_range_sample', 'last_range_sample'], 'dimensions': ['swath', 'azimuth_time'], 'dtype': '<f4', 'long_name': 'azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

          dtype :

          <f4

          long_name :

          azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)

          Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 279kB
      Dimensions:          (azimuth_time: 26, ground_range: 671)
      Coordinates:
          ground_range     (azimuth_time, ground_range) float64 140kB dask.array<chunksize=(26, 671), meta=np.ndarray>
        * azimuth_time     (azimuth_time) datetime64[ns] 208B 2026-03-16T16:47:54.7...
          line             (azimuth_time) int32 104B dask.array<chunksize=(26,), meta=np.ndarray>
          pixel            (azimuth_time, ground_range) int32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>
      Data variables:
          noise_range_lut  (azimuth_time, ground_range) float32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>

      noise_range

      • Groups: (0)
      • Dimensions:
        • azimuth_time: 26
        • ground_range: 671
      • Coordinates: (4)
        • ground_range
          (azimuth_time, ground_range)
          float64
          dask.array<chunksize=(26, 671), meta=np.ndarray>

          Array Chunk Bytes 136.30 kiB 136.30 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • azimuth_time
          (azimuth_time)
          datetime64[ns]
          2026-03-16T16:47:54.761834 ... 2...

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          zero doppler azimuth time at which noise vector applies

          array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.762495000',
                 '2026-03-16T16:47:56.763156000', '2026-03-16T16:47:57.763817000',
                 '2026-03-16T16:47:58.764478000', '2026-03-16T16:47:59.765139000',
                 '2026-03-16T16:48:00.765800000', '2026-03-16T16:48:01.766461000',
                 '2026-03-16T16:48:02.767122000', '2026-03-16T16:48:03.767783000',
                 '2026-03-16T16:48:04.768444000', '2026-03-16T16:48:05.769104000',
                 '2026-03-16T16:48:06.769765000', '2026-03-16T16:48:07.770426000',
                 '2026-03-16T16:48:08.771087000', '2026-03-16T16:48:09.771748000',
                 '2026-03-16T16:48:10.772409000', '2026-03-16T16:48:11.773070000',
                 '2026-03-16T16:48:12.773731000', '2026-03-16T16:48:13.774392000',
                 '2026-03-16T16:48:14.775053000', '2026-03-16T16:48:15.775714000',
                 '2026-03-16T16:48:16.776375000', '2026-03-16T16:48:17.777036000',
                 '2026-03-16T16:48:18.777697000', '2026-03-16T16:48:19.760382000'],
                dtype='datetime64[ns]')

        • line
          (azimuth_time)
          int32
          dask.array<chunksize=(26,), meta=np.ndarray>

          dimensions :

          ['azimuth_time']

          dtype :

          <M8[ns]

          long_name :

          line

          Array Chunk Bytes 104 B 104 B Shape (26,) (26,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

        • pixel
          (azimuth_time, ground_range)
          int32
          dask.array<chunksize=(26, 671), meta=np.ndarray>

          Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

      • Data variables: (1)
        • noise_range_lut
          (azimuth_time, ground_range)
          float32
          dask.array<chunksize=(26, 671), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'range thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

          dtype :

          <f4

          long_name :

          range thermal noise correction vector power values (this array contains the count attribute number of floating point values)

          Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (0)

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (3)

    other_metadata :

    {'azimuth_steering_rate': 1.590368784, 'downlink_information': {'azimuth_time': '2026-03-16T16:47:50.757728', 'baq_block_length': 256, 'decimation_filter_bandwidth': 56590000.0, 'ecc_number': 8, 'filter_length': 36, 'first_line_sensing_time': '2026-03-16T16:42:01.593452', 'instrument_config_id': 5, 'last_line_sensing_time': '2026-03-16T16:42:32.143255', 'mean_bit_rate': 1.700959009296019, 'num_err_azimuth_beam_address': 0, 'num_err_baq_block_length': 0, 'num_err_baq_mode': 0, 'num_err_cal_mode': 0, 'num_err_cal_type': 0, 'num_err_calibration_beam_address': 0, 'num_err_data_take_id': 0, 'num_err_ecc_number': 0, 'num_err_elevation_beam_address': 0, 'num_err_instrument_config_id': 0, 'num_err_number_of_quads': 0, 'num_err_packet_count': 0, 'num_err_polarisation': 0, 'num_err_pri': 0, 'num_err_pri_count': 0, 'num_err_range_decimation': 0, 'num_err_rank': 0, 'num_err_rx_channel_id': 0, 'num_err_rx_gain': 0, 'num_err_sas_test_mode': 0, 'num_err_signal_type': 0, 'num_err_ssb_error_flag': 0, 'num_err_swap_flag': 0, 'num_err_swath_number': 0, 'num_err_swl': 0, 'num_err_swst': 0, 'num_err_sync_marker': 0, 'num_err_temp_comp': 0, 'num_err_test_mode': 0, 'num_err_tx_pulse_number': 0, 'num_err_tx_pulse_start_frequency': 0, 'num_err_tx_ramp_rate': 0, 'num_isp_header_errors': 0, 'pointing_status': {'azimuth_time': '2026-03-16T16:42:01.112039'}, 'prf': 1717.128973878037, 'pri': 0.0005823674372819869, 'rank': 9, 'rx_channel_id': 0, 'rx_gain': -4.0, 'sampling_frequency_after_decimation': 64345238.12571429, 'swath': 'IW1', 'swath_number': 10, 'swl_azimuth_time': ['2026-03-16T16:47:51.903772', '2026-03-16T16:48:00.178598', '2026-03-16T16:47:52.723896', '2026-03-16T16:48:00.998722', '2026-03-16T16:47:53.800376', '2026-03-16T16:48:02.075218'], 'swl_value': ['3.725084179549267e-04', '3.722952819698287e-04', '4.438024049701879e-04', '4.435359849888155e-04', '4.234479183933346e-04', '4.231282144156876e-04'], 'swst_azimuth_time': ['2026-03-16T16:47:51.903772', '2026-03-16T16:48:05.695154', '2026-03-16T16:47:52.723896', '2026-03-16T16:48:06.515278', '2026-03-16T16:47:53.800376', '2026-03-16T16:48:07.591758'], 'swst_value': ['1.021987048544628e-04', '1.035574467594622e-04', '1.416555040957191e-04', '1.428810360100323e-04', '9.095578164054638e-05', '9.204810356417333e-05'], 'tx_pulse_length': 5.240481033595628e-05, 'tx_pulse_ramp_rate': 1078230321255.894, 'tx_pulse_start_frequency': -28251534.19637256}, 'eopf_category': 'eoproduct', 'general_annotation': {'attitude_list': [{'frame': 'GM2000', 'pitch': -55.17261231188552, 'q_0': -0.4113218, 'q_1': 0.3290825, 'q_2': -0.1024825, 'q_3': 0.8438106, 'roll': 38.80744740949537, 'time': '2026-03-16T16:47:55.000002', 'wx': 7.1674570790492e-05, 'wy': -0.0009378587710671127, 'wz': -0.0005000680102966726, 'yaw': -34.70487602857855}, {'frame': 'GM2000', 'pitch': -55.12904739969731, 'q_0': -0.4108475, 'q_1': 0.3291059, 'q_2': -0.102416, 'q_3': 0.8440407, 'roll': 38.76535946512109, 'time': '2026-03-16T16:47:55.999998', 'wx': 7.168311276473105e-05, 'wy': -0.0009378853137604892, 'wz': -0.0004887541872449219, 'yaw': -34.64946319619379}, {'frame': 'GM2000', 'pitch': -55.08594605711506, 'q_0': -0.4103732, 'q_1': 0.3291317, 'q_2': -0.1023494, 'q_3': 0.8442693, 'roll': 38.72317140066681, 'time': '2026-03-16T16:47:57.000002', 'wx': 6.368310278048739e-05, 'wy': -0.0009378857212141156, 'wz': -0.000488748075440526, 'yaw': -34.59426333934903}, {'frame': 'GM2000', 'pitch': -55.04273445617216, 'q_0': -0.4098973, 'q_1': 0.3291572, 'q_2': -0.1022826, 'q_3': 0.8444987, 'roll': 38.68081439071234, 'time': '2026-03-16T16:47:57.999996', 'wx': 6.367456808220595e-05, 'wy': -0.0009378591785207391, 'wz': -0.0005000618984922767, 'yaw': -34.53894608955266}, {'frame': 'GM2000', 'pitch': -54.99988781279179, 'q_0': -0.4094216, 'q_1': 0.3291841, 'q_2': -0.1022174, 'q_3': 0.8447268, 'roll': 38.63831562982691, 'time': '2026-03-16T16:47:59.000000', 'wx': 6.367913738358766e-05, 'wy': -0.0009435293031856418, 'wz': -0.0004944183747284114, 'yaw': -34.48398500887792}, {'frame': 'GM2000', 'pitch': -54.95712657807638, 'q_0': -0.408945, 'q_1': 0.3292113, 'q_2': -0.1021531, 'q_3': 0.8449548, 'roll': 38.59564165317258, 'time': '2026-03-16T16:47:59.999995', 'wx': 6.367913738358766e-05, 'wy': -0.0009435293031856418, 'wz': -0.0004944183747284114, 'yaw': -34.42914639927536}, {'frame': 'GM2000', 'pitch': -54.91444463565411, 'q_0': -0.4084688, 'q_1': 0.3292379, 'q_2': -0.1020892, 'q_3': 0.8451825, 'roll': 38.55298661989919, 'time': '2026-03-16T16:48:01.000000', 'wx': 6.36876720818691e-05, 'wy': -0.0009435559040866792, 'wz': -0.0004831046098843217, 'yaw': -34.37447856993219}, {'frame': 'GM2000', 'pitch': -54.87215781892897, 'q_0': -0.4079936, 'q_1': 0.3292663, 'q_2': -0.1020237, 'q_3': 0.8454088, 'roll': 38.51041495865938, 'time': '2026-03-16T16:48:01.999995', 'wx': 5.568311098613776e-05, 'wy': -0.0009378861868754029, 'wz': -0.0004887420800514519, 'yaw': -34.31988675394627}, {'frame': 'GM2000', 'pitch': -54.82971499083168, 'q_0': -0.4075179, 'q_1': 0.3292933, 'q_2': -0.1019582, 'q_3': 0.8456357, 'roll': 38.46782473458212, 'time': '2026-03-16T16:48:03.000000', 'wx': 5.568311098613776e-05, 'wy': -0.0009378861868754029, 'wz': -0.0004887420800514519, 'yaw': -34.26525516932718}, {'frame': 'GM2000', 'pitch': -54.78734746764446, 'q_0': -0.4070441, 'q_1': 0.3293189, 'q_2': -0.1018923, 'q_3': 0.8458618, 'roll': 38.42545578769001, 'time': '2026-03-16T16:48:04.000003', 'wx': 6.368310278048739e-05, 'wy': -0.0009378857212141156, 'wz': -0.000488748075440526, 'yaw': -34.21081497631647}, {'frame': 'GM2000', 'pitch': -54.74498225673515, 'q_0': -0.4065683, 'q_1': 0.3293447, 'q_2': -0.1018277, 'q_3': 0.8460883, 'roll': 38.38280373226142, 'time': '2026-03-16T16:48:04.999998', 'wx': 6.368370668496937e-05, 'wy': -0.0009491994860582054, 'wz': -0.0004887749091722071, 'yaw': -34.1564257913761}, {'frame': 'GM2000', 'pitch': -54.70261615592364, 'q_0': -0.406094, 'q_1': 0.3293693, 'q_2': -0.1017615, 'q_3': 0.8463145, 'roll': 38.34039119959236, 'time': '2026-03-16T16:48:06.000003', 'wx': 6.368310278048739e-05, 'wy': -0.0009378857212141156, 'wz': -0.000488748075440526, 'yaw': -34.10203582562131}, {'frame': 'GM2000', 'pitch': -54.66006022527766, 'q_0': -0.4056189, 'q_1': 0.3293924, 'q_2': -0.1016947, 'q_3': 0.8465413, 'roll': 38.29797808532778, 'time': '2026-03-16T16:48:06.999997', 'wx': 7.168311276473105e-05, 'wy': -0.0009378853137604892, 'wz': -0.0004887541872449219, 'yaw': -34.04751721158922}, {'frame': 'GM2000', 'pitch': -54.61735686948578, 'q_0': -0.4051441, 'q_1': 0.3294137, 'q_2': -0.101627, 'q_3': 0.8467685, 'roll': 38.25567857528648, 'time': '2026-03-16T16:48:08.000002', 'wx': 7.967914280015975e-05, 'wy': -0.0009435283718630672, 'wz': -0.0004944304237142205, 'yaw': -33.99293639196655}, {'frame': 'GM2000', 'pitch': -54.57470577315505, 'q_0': -0.4046705, 'q_1': 0.329434, 'q_2': -0.1015586, 'q_3': 0.8469952, 'roll': 38.21355210897909, 'time': '2026-03-16T16:48:08.999996', 'wx': 7.168311276473105e-05, 'wy': -0.0009378853137604892, 'wz': -0.0004887541872449219, 'yaw': -33.93844508504616}, {'frame': 'GM2000', 'pitch': -54.5318431800682, 'q_0': -0.4041955, 'q_1': 0.3294532, 'q_2': -0.1014895, 'q_3': 0.8472228, 'roll': 38.17133558867301, 'time': '2026-03-16T16:48:10.000001', 'wx': 7.967914280015975e-05, 'wy': -0.0009435283718630672, 'wz': -0.0004944304237142205, 'yaw': -33.88376120183212}, {'frame': 'GM2000', 'pitch': -54.48903603461851, 'q_0': -0.4037198, 'q_1': 0.3294725, 'q_2': -0.1014201, 'q_3': 0.8474504, 'roll': 38.12905195820072, 'time': '2026-03-16T16:48:10.999996', 'wx': 7.967853161972016e-05, 'wy': -0.0009322147234342992, 'wz': -0.0004944037063978612, 'yaw': -33.82908217391361}, {'frame': 'GM2000', 'pitch': -54.44633227461761, 'q_0': -0.4032434, 'q_1': 0.3294923, 'q_2': -0.101351, 'q_3': 0.8476777, 'roll': 38.08662565750637, 'time': '2026-03-16T16:48:12.000000', 'wx': 7.967914280015975e-05, 'wy': 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'GM2000', 'pitch': -54.27747503916703, 'q_0': -0.4013363, 'q_1': 0.3295783, 'q_2': -0.1010792, 'q_3': 0.8485813, 'roll': 37.91603133169262, 'time': '2026-03-16T16:48:16.000000', 'wx': 6.368310278048739e-05, 'wy': -0.0009378857212141156, 'wz': -0.000488748075440526, 'yaw': -33.55763864019311}, {'frame': 'GM2000', 'pitch': -54.23571079303117, 'q_0': -0.4008584, 'q_1': 0.3296016, 'q_2': -0.1010129, 'q_3': 0.848806, 'roll': 37.87305790435265, 'time': '2026-03-16T16:48:17.000004', 'wx': 5.568311098613776e-05, 'wy': -0.0009378861868754029, 'wz': -0.0004887420800514519, 'yaw': -33.50381773023206}, {'frame': 'GM2000', 'pitch': -54.19431118739229, 'q_0': -0.4003805, 'q_1': 0.3296265, 'q_2': -0.1009481, 'q_3': 0.8490296, 'roll': 37.82991593737108, 'time': '2026-03-16T16:48:17.999998', 'wx': 5.567914922721684e-05, 'wy': -0.00094352982705459, 'wz': -0.0004944123211316764, 'yaw': -33.45032223981444}, {'frame': 'GM2000', 'pitch': -54.15311721487835, 'q_0': -0.399903, 'q_1': 0.3296523, 'q_2': -0.1008816, 'q_3': 0.8492525, 'roll': 37.78683361923807, 'time': '2026-03-16T16:48:19.000004', 'wx': 5.567914922721684e-05, 'wy': -0.00094352982705459, 'wz': -0.0004944123211316764, 'yaw': -33.39680577404658}], 'azimuth_fm_rate_list': [{'azimuth_fm_rate_polynomial': {'$': '-2.329507759513437e+03 4.513155220334518e+05 -7.875722824990876e+07'}, 'azimuth_time': '2026-03-16T16:47:52.314037', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329425220531431e+03 4.513337537013095e+05 -7.876601537209529e+07'}, 'azimuth_time': '2026-03-16T16:47:55.072314', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329288456843449e+03 4.513521067110632e+05 -7.878660809204991e+07'}, 'azimuth_time': '2026-03-16T16:47:57.830591', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329170524177974e+03 4.513688863169953e+05 -7.879955400439282e+07'}, 'azimuth_time': '2026-03-16T16:48:00.588868', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329152683803014e+03 4.513898117818113e+05 -7.880297494477469e+07'}, 'azimuth_time': '2026-03-16T16:48:03.347145', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329090254228201e+03 4.514097867327967e+05 -7.881377438486762e+07'}, 'azimuth_time': '2026-03-16T16:48:06.105422', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329046750246828e+03 4.514299174061207e+05 -7.882034795674761e+07'}, 'azimuth_time': '2026-03-16T16:48:08.863699', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.329003315349202e+03 4.514494069880208e+05 -7.882712276024127e+07'}, 'azimuth_time': '2026-03-16T16:48:11.621976', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.328934447571472e+03 4.514685709684306e+05 -7.883624526838507e+07'}, 'azimuth_time': '2026-03-16T16:48:14.380253', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.328841910896722e+03 4.514879998793321e+05 -7.885308166200055e+07'}, 'azimuth_time': '2026-03-16T16:48:17.138530', 't_0': 0.005330565783569756}, {'azimuth_fm_rate_polynomial': {'$': '-2.328759304172387e+03 4.515057441557362e+05 -7.886099677908592e+07'}, 'azimuth_time': '2026-03-16T16:48:19.896807', 't_0': 0.005330565783569756}], 'downlink_information_list': [{'azimuth_time': '2026-03-16T16:47:50.757728', 'bit_error_count': {'num_err_azimuth_beam_address': 0, 'num_err_baq_block_length': 0, 'num_err_baq_mode': 0, 'num_err_cal_mode': 0, 'num_err_cal_type': 0, 'num_err_calibration_beam_address': 0, 'num_err_data_take_id': 0, 'num_err_ecc_number': 0, 'num_err_elevation_beam_address': 0, 'num_err_instrument_config_id': 0, 'num_err_number_of_quads': 0, 'num_err_packet_count': 0, 'num_err_polarisation': 0, 'num_err_pri': 0, 'num_err_pri_count': 0, 'num_err_range_decimation': 0, 'num_err_rank': 0, 'num_err_rx_channel_id': 0, 'num_err_rx_gain': 0, 'num_err_sas_test_mode': 0, 'num_err_signal_type': 0, 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'total_bandwidth': 327.0, 'window_coefficient': 0.7, 'window_type': ['Hamming', 'Hamming', 'Hamming']}, 'input_dimensions': {'azimuth_time': '2026-03-16T16:47:51.894617', 'number_of_input_lines': 16500, 'number_of_input_samples': 23882}, 'processor_scaling_factor': 101690000000000.0, 'range_processing': {'look_bandwidth': 14100000.0, 'look_overlap': 3502589.347412674, 'number_of_looks': 5, 'processing_bandwidth': 56500000.0, 'total_bandwidth': 56504455.48389234, 'window_coefficient': 0.7, 'window_type': ['Hamming', 'Hamming', 'Hamming']}}, 'product_quality': {'azimuth_time': '2026-03-16T16:47:54.761834', 'doppler_centroid_quality': {'dc_method': 'Data Analysis', 'doppler_centroid_uncertain_flag': True}, 'downlink_quality': {'chirp_source_used': 'Nominal', 'downlink_gaps_in_input_data_significant_flag': True, 'downlink_missing_lines_significant_flag': True, 'i_input_data_mean': 0.01635888963937759, 'i_input_data_std_dev': 12.90546035766602, 'input_data_mean_outside_nominal_range_flag': 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25.12475, 3.992737], 'max_kl_divergence': [179988.4, 7.047721, 259974.0, 13.58421, 17.17076, 99996.08, 3.668005, 9.935549, 2.093246, 8.637241, 9.066045, 12.11973, 1.929122, 2.642891, 19.77823, 2.044533, 219979.3, 3.642163], 'max_rfi_psd': [22.79415, 0.0, 20.73992, 0.0, 0.0, 21.51024, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 32.18208, 0.0], 'noise_sensing_time': ['2026-03-16T16:47:52.722175', '2026-03-16T16:47:58.238731', '2026-03-16T16:48:03.755271', '2026-03-16T16:48:09.271827', '2026-03-16T16:48:14.788383', '2026-03-16T16:48:20.304939', '2026-03-16T16:47:53.799354', '2026-03-16T16:47:59.315910', '2026-03-16T16:48:04.832466', '2026-03-16T16:48:10.349021', '2026-03-16T16:48:15.865562', '2026-03-16T16:48:21.382118', '2026-03-16T16:47:54.648155', '2026-03-16T16:48:00.164710', '2026-03-16T16:48:05.681266', '2026-03-16T16:48:11.197822', '2026-03-16T16:48:16.714378', '2026-03-16T16:48:22.230934'], 'rfi_detected': ['true', 'false', 'true', 'false', 'false', 'true', 'false', 'false', 'false', 'false', 'false', 'false', 'false', 'false', 'false', 'false', 'true', 'false']}}}, 'swath_merging': {'swath_merge_list': [{'swath': 'IW1', 'swath_bounds_list': [{'azimuth_time': '2026-03-16T16:47:54.761834', 'first_azimuth_line': 0, 'first_range_sample': 0, 'last_azimuth_line': 16688, 'last_range_sample': 8871}]}, {'swath': 'IW2', 'swath_bounds_list': [{'azimuth_time': '2026-03-16T16:47:54.761834', 'first_azimuth_line': 0, 'first_range_sample': 8872, 'last_azimuth_line': 16688, 'last_range_sample': 17677}]}, {'swath': 'IW3', 'swath_bounds_list': [{'azimuth_time': '2026-03-16T16:47:54.761834', 'first_azimuth_line': 0, 'first_range_sample': 17678, 'last_azimuth_line': 16688, 'last_range_sample': 26641}]}]}, 'swath_timing': {'burst_list': {}, 'lines_per_burst': 0, 'samples_per_burst': 0}, 'timeliness_category': 'NRT-3h', 'title': 'S01SIWGRD'}

    processing_history :

    {'Level-0 Product': [{'facility': 'Unknown', 'inputs': [], 'outputs': ['Downlinked Stream'], 'processor': 'Unknown', 'time': '2026-03-16T16:44:59.039521Z', 'version': 'Unknown'}, {'facility': 'Unknown', 'inputs': [], 'outputs': ['Downlinked Stream'], 'processor': 'Unknown', 'time': '2026-03-16T16:45:26.324421Z', 'version': 'Unknown'}, {'facility': 'Unknown', 'inputs': ['S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Downlinked Stream'], 'outputs': ['Unknown'], 'processor': 'Unknown', 'time': '2026-03-16T17:07:12.306670Z', 'version': 'Unknown'}, {'facility': 'Unknown', 'inputs': ['S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Downlinked Stream'], 'outputs': ['Unknown'], 'processor': 'Unknown', 'time': '2026-03-16T17:12:02.055507Z', 'version': 'Unknown'}, {'facility': 'ESA-S1C-PS', 'inputs': ['S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Unknown', 'Unknown'], 'outputs': ['/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0SDV_20260316T164751_20260316T164823_006792_00DBA6_DA02.SAFE'], 'processor': '', 'time': '2026-03-16T17:15:28.685180Z', 'version': ''}], 'Level-1 Intermediate SLC Product': [{'adfs': ['/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_AUX_PP1_V20250427T000000_G20260203T152141.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_AUX_CAL_V20250427T000000_G20260212T132118.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_AUX_INS_V20250224T000000_G20251015T082332.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_OPER_AUX_PREORB_OPOD_20260316T170705_V20260316T163717_20260316T231217.EOF'], 'facility': 'ESA-S1C-PS', 'inputs': ['/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0ADV_20260316T164201_20260316T165701_006792_00DBA6_2189.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0CDV_20260316T164201_20260316T165701_006792_00DBA6_9E95.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0NDV_20260316T164201_20260316T165701_006792_00DBA6_97E5.SAFE', '/data_pwa/cps/wd/eops-cps-pnode-s1-l1-0/IW_RAW__0_GRDH_1_4_03_IW_RAW__0_GRDH_1_4_03_301675188/S1C_IW_RAW__0SDV_20260316T164751_20260316T164823_006792_00DBA6_DA02.SAFE', 'Sentinel-1 Product Definition (S1-RS-MDA-52-7440) release 2/7', 'Sentinel-1 Product Specification (S1-RS-MDA-52-7441) release 3/14'], 'outputs': ['S1C_IW_SL1__1_DV_20260316T164751_20260316T164824_006792_00DBA6_1763.SAFE'], 'processor': 'Sentinel-1 IPF', 'time': '2026-03-16T17:55:39.000000Z', 'version': '004.03'}], 'Level-1 Product': [{'facility': 'ESA-S1C-PS', 'inputs': ['S1C_IW_SL1__1_DV_20260316T164751_20260316T164824_006792_00DBA6_1763.SAFE'], 'outputs': ['S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.SAFE'], 'processor': 'Sentinel-1 IPF', 'time': '2026-03-16T17:57:14.000000Z', 'version': '004.03'}, {'facility': 'CS-SopraSteria', 'inputs': ['S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.SAFE'], 'outputs': ['S1C_IW_GRDH_1SDV_20260316T164754_20260316T164819_006792_00DBA6_8EA0.zarr.zarr'], 'processor': 'EOPF-CPM', 'time': '2026-03-16T18:51:01.944012Z', 'version': '2.6.2'}]}

    stac_discovery :

    {'assets': {}, 'bbox': [18.688345, 39.213028, 15.261218, 41.126934], 'detectionPerformed': 'true', 'geometry': {'coordinates': [[[15.261218, 40.715069], [18.376741, 41.126934], [18.688345, 39.626194], [15.640784, 39.213028], [15.261218, 40.715069]]], 'type': 'Polygon'}, 'id': 'S01SIWGRD_20260316T164754_0024_C146_T1CE', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'constellation': 'sentinel-1', 'created': '2026-03-16T17:57:14.000000Z', 'datetime': 'None', 'end_datetime': '2026-03-16T16:48:19.760381Z', 'instruments': ['sar'], 'platform': 'sentinel-1c', 'processing:software': {'Sentinel-1 IPF': '004.03'}, 'processing:version': '004.00', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT-3h', 'product:type': 'S01SIWGRD', 'sar:center_frequency': 5405000454.33435, 'sar:frequency_band': 'C', 'sar:instrument_mode': 'IW', 'sar:pixel_spacing_azimuth': 10.0, 'sar:pixel_spacing_range': 10.0, 'sat:absolute_orbit': 6792, 'sat:anx_datetime': '2026-03-16T16:37:16.668595', 'sat:orbit_state': 'ascending', 'sat:platform_international_designator': '2024-235A', 'sat:relative_orbit': 146, 'start_datetime': '2026-03-16T16:47:54.761834Z', 'view:incidence_angle': 39.13511618906348}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/sar/v1.2.0/schema.json'], 'stac_version': '1.1.0', 'thermalNoiseCorrectionPerformed': 'false', 'type': 'Feature'}

• Dimensions:
• Coordinates: (0)
• Inherited coordinates: (0)
• Data variables: (0)
• Attributes: (2)

  other_metadata :

  {'eopf_category': 'eocontainer', 'title': 'S01SIWGRD'}

  stac_discovery :

  {'assets': {'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH': {'href': 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH'}, 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV': {'href': 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV'}}, 'bbox': [18.688345, 39.213028, 15.261218, 41.126934], 'geometry': {'coordinates': [[[15.261218, 40.715069], [18.376741, 41.126934], [18.688345, 39.626194], [15.640784, 39.213028], [15.261218, 40.715069]]], 'type': 'Polygon'}, 'id': 'SAR Standard L1 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'constellation': 'sentinel-1', 'created': '2026-03-16T17:57:14.000000Z', 'datetime': '2026-03-16T16:48:07.261108Z', 'end_datetime': '2026-03-16T16:48:19.760381Z', 'eopf:datatake_id': '56230', 'eopf:instrument_mode': 'IW', 'instruments': ['sar'], 'platform': 'sentinel-1c', 'processing:lineage': 'systematic', 'processing:software': {'Sentinel-1 IPF': '004.03'}, 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT-3h', 'product:type': 'S01SIWGRD', 'provider': [{'name': 'S1C-PS', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sar:frequency_band': 'C', 'sar:instrument_mode': 'IW', 'sar:polarizations': ['VV', 'VH'], 'sar:product_type': 'GRD', 'sat:absolute_orbit': 6792, 'sat:anx_datetime': '2026-03-16T16:37:16.668595', 'sat:orbit_state': 'ascending', 'sat:platform_international_designator': '2024-235A', 'sat:relative_orbit': 146, 'start_datetime': '2026-03-16T16:47:54.761834Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/sar/v1.2.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

We can extract the "VV" band as shown below:

In [5]:

ds = dt.S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH.measurements.to_dataset()
ds

ds = dt.S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH.measurements.to_dataset() ds

Out[5]:

<xarray.Dataset> Size: 890MB
Dimensions:       (azimuth_time: 16689, ground_range: 26642)
Coordinates:
  * azimuth_time  (azimuth_time) datetime64[ns] 134kB 2026-03-16T16:47:54.761...
  * ground_range  (ground_range) float64 213kB 0.0 10.0 ... 2.664e+05 2.664e+05
    line          (azimuth_time) int64 134kB dask.array<chunksize=(2048,), meta=np.ndarray>
    pixel         (ground_range) int64 213kB dask.array<chunksize=(4096,), meta=np.ndarray>
Data variables:
    grd           (azimuth_time, ground_range) uint16 889MB dask.array<chunksize=(2048, 4096), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • azimuth_time: 16689
  • ground_range: 26642
• Coordinates: (4)
  • azimuth_time
    (azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
           '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
           '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
          shape=(16689,), dtype='datetime64[ns]')

  • ground_range
    (ground_range)
    float64
    0.0 10.0 ... 2.664e+05 2.664e+05

    array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
           2.6641e+05], shape=(26642,))

  • line
    (azimuth_time)
    int64
    dask.array<chunksize=(2048,), meta=np.ndarray>

    Array Chunk Bytes 130.38 kiB 16.00 kiB Shape (16689,) (2048,) Dask graph 9 chunks in 2 graph layers Data type int64 numpy.ndarray

  • pixel
    (ground_range)
    int64
    dask.array<chunksize=(4096,), meta=np.ndarray>

    Array Chunk Bytes 208.14 kiB 32.00 kiB Shape (26642,) (4096,) Dask graph 7 chunks in 2 graph layers Data type int64 numpy.ndarray

• Data variables: (1)
  • grd
    (azimuth_time, ground_range)
    uint16
    dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

    dtype :

    <u2

    long_name :

    measurement data set for GRD IW

    Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

• Indexes: (2)
  • azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex([   '2026-03-16 16:47:54.761834',
                   '2026-03-16 16:47:54.763331995',
                   '2026-03-16 16:47:54.764829990',
                   '2026-03-16 16:47:54.766327986',
                   '2026-03-16 16:47:54.767825981',
                   '2026-03-16 16:47:54.769323976',
                   '2026-03-16 16:47:54.770821972',
                   '2026-03-16 16:47:54.772319967',
                   '2026-03-16 16:47:54.773817963',
                   '2026-03-16 16:47:54.775315958',
                   ...
                   '2026-03-16 16:48:19.746899041',
                   '2026-03-16 16:48:19.748397036',
                   '2026-03-16 16:48:19.749895032',
                   '2026-03-16 16:48:19.751393027',
                   '2026-03-16 16:48:19.752891023',
                   '2026-03-16 16:48:19.754389018',
                   '2026-03-16 16:48:19.755887013',
                   '2026-03-16 16:48:19.757385009',
                   '2026-03-16 16:48:19.758883004',
                      '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='azimuth_time', length=16689, freq=None))

  • ground_range
    PandasIndex

    PandasIndex(Index([     0.0,     10.0,     20.0,     30.0,     40.0,     50.0,     60.0,
               70.0,     80.0,     90.0,
           ...
           266320.0, 266330.0, 266340.0, 266350.0, 266360.0, 266370.0, 266380.0,
           266390.0, 266400.0, 266410.0],
          dtype='float64', name='ground_range', length=26642))

• Attributes: (0)

As an example, we plot the raw GRD array.

In [6]:

ds.grd[::10, ::10].plot(vmax=200, figsize=(13, 8))

ds.grd[::10, ::10].plot(vmax=200, figsize=(13, 8))

Out[6]:

<matplotlib.collections.QuadMesh at 0x72da94314830>

Open Sentinel-1 GRD VH group in native mode as Dataset¶

Similarily, we can open the individual polarisation bands (assets "vh" and "vv") to open the individual groups as an xarray.Dataset as shown below:

In [7]:

%%time
ds = xr.open_dataset(
    item.assets["vh"].href,
    **item.assets["vh"].extra_fields["xarray:open_dataset_kwargs"]
)
ds

%%time ds = xr.open_dataset( item.assets["vh"].href, **item.assets["vh"].extra_fields["xarray:open_dataset_kwargs"] ) ds

CPU times: user 22.3 ms, sys: 3.92 ms, total: 26.2 ms
Wall time: 350 ms

Out[7]:

<xarray.Dataset> Size: 890MB
Dimensions:       (azimuth_time: 16689, ground_range: 26642)
Coordinates:
  * azimuth_time  (azimuth_time) datetime64[ns] 134kB 2026-03-16T16:47:54.761...
  * ground_range  (ground_range) float64 213kB 0.0 10.0 ... 2.664e+05 2.664e+05
    line          (azimuth_time) int64 134kB dask.array<chunksize=(2048,), meta=np.ndarray>
    pixel         (ground_range) int64 213kB dask.array<chunksize=(4096,), meta=np.ndarray>
Data variables:
    grd           (azimuth_time, ground_range) uint16 889MB dask.array<chunksize=(2048, 4096), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • azimuth_time: 16689
  • ground_range: 26642
• Coordinates: (4)
  • azimuth_time
    (azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
           '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
           '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
          shape=(16689,), dtype='datetime64[ns]')

  • ground_range
    (ground_range)
    float64
    0.0 10.0 ... 2.664e+05 2.664e+05

    array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
           2.6641e+05], shape=(26642,))

  • line
    (azimuth_time)
    int64
    dask.array<chunksize=(2048,), meta=np.ndarray>

    Array Chunk Bytes 130.38 kiB 16.00 kiB Shape (16689,) (2048,) Dask graph 9 chunks in 2 graph layers Data type int64 numpy.ndarray

  • pixel
    (ground_range)
    int64
    dask.array<chunksize=(4096,), meta=np.ndarray>

    Array Chunk Bytes 208.14 kiB 32.00 kiB Shape (26642,) (4096,) Dask graph 7 chunks in 2 graph layers Data type int64 numpy.ndarray

• Data variables: (1)
  • grd
    (azimuth_time, ground_range)
    uint16
    dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

    dtype :

    <u2

    long_name :

    measurement data set for GRD IW

    Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

• Indexes: (2)
  • azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex([   '2026-03-16 16:47:54.761834',
                   '2026-03-16 16:47:54.763331995',
                   '2026-03-16 16:47:54.764829990',
                   '2026-03-16 16:47:54.766327986',
                   '2026-03-16 16:47:54.767825981',
                   '2026-03-16 16:47:54.769323976',
                   '2026-03-16 16:47:54.770821972',
                   '2026-03-16 16:47:54.772319967',
                   '2026-03-16 16:47:54.773817963',
                   '2026-03-16 16:47:54.775315958',
                   ...
                   '2026-03-16 16:48:19.746899041',
                   '2026-03-16 16:48:19.748397036',
                   '2026-03-16 16:48:19.749895032',
                   '2026-03-16 16:48:19.751393027',
                   '2026-03-16 16:48:19.752891023',
                   '2026-03-16 16:48:19.754389018',
                   '2026-03-16 16:48:19.755887013',
                   '2026-03-16 16:48:19.757385009',
                   '2026-03-16 16:48:19.758883004',
                      '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='azimuth_time', length=16689, freq=None))

  • ground_range
    PandasIndex

    PandasIndex(Index([     0.0,     10.0,     20.0,     30.0,     40.0,     50.0,     60.0,
               70.0,     80.0,     90.0,
           ...
           266320.0, 266330.0, 266340.0, 266350.0, 266360.0, 266370.0, 266380.0,
           266390.0, 266400.0, 266410.0],
          dtype='float64', name='ground_range', length=26642))

• Attributes: (0)

Open Sentinel-1 GRD as Dataset¶

The xarray.DataTree model was introduced in xarray v2024.10.0 (October 2024). To maintain compatibility with workflows based on xr.Dataset, the function xarray.open_dataset(..., engine="eopf-zarr", op_mode="native") is provided, which flattens the DataTree into a single dataset.

During this process, hierarchical groups in the Zarr product are merged, and variable as well as dimension names are prefixed with their group paths (using _ by default) to ensure uniqueness. For example, S01SIWGRD_20260316T045550_0025_A364_7C56_080022_VH.measurements becomes S01SIWGRD_20260316T045550_0025_A364_7C56_080022_VH_measurements.

In [8]:

%%time
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    op_mode="native",
    chunks={},
)
ds

%%time ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", op_mode="native", chunks={}, ) ds

CPU times: user 219 ms, sys: 10.8 ms, total: 230 ms
Wall time: 2.26 s

Out[8]:

<xarray.Dataset> Size: 2GB
Dimensions:                                                           (
                                                                       VH_conditions_antenna_pattern_azimuth_time: 27,
                                                                       VH_conditions_antenna_pattern_count: 730,
                                                                       VH_conditions_attitude_azimuth_time: 25,
                                                                       VH_conditions_doppler_centroid_azimuth_time: 27,
                                                                       VH_conditions_doppler_centroid_degree: 3,
                                                                       ...
                                                                       VV_quality_calibration_azimuth_time: 27,
                                                                       VV_quality_calibration_ground_range: 668,
                                                                       VV_quality_azimuth_swath: 3,
                                                                       VV_quality_azimuth_azimuth_time: 1688,
                                                                       VV_quality_range_azimuth_time: 26,
                                                                       VV_quality_range_ground_range: 671)
Coordinates: (12/58)
  * VH_conditions_antenna_pattern_azimuth_time                        (VH_conditions_antenna_pattern_azimuth_time) datetime64[ns] 216B ...
  * VH_conditions_antenna_pattern_count                               (VH_conditions_antenna_pattern_count) int64 6kB ...
  * VH_conditions_attitude_azimuth_time                               (VH_conditions_attitude_azimuth_time) datetime64[ns] 200B ...
  * VH_conditions_doppler_centroid_azimuth_time                       (VH_conditions_doppler_centroid_azimuth_time) datetime64[ns] 216B ...
  * VH_conditions_doppler_centroid_degree                             (VH_conditions_doppler_centroid_degree) int64 24B ...
  * VH_conditions_gcp_azimuth_time                                    (VH_conditions_gcp_azimuth_time) datetime64[ns] 80B ...
    ...                                                                ...
    VV_quality_azimuth_line                                           (VV_quality_azimuth_swath, VV_quality_azimuth_azimuth_time) int32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>
  * VV_quality_azimuth_swath                                          (VV_quality_azimuth_swath) <U3 36B ...
    VV_quality_range_ground_range                                     (VV_quality_range_azimuth_time, VV_quality_range_ground_range) float64 140kB dask.array<chunksize=(26, 671), meta=np.ndarray>
  * VV_quality_range_azimuth_time                                     (VV_quality_range_azimuth_time) datetime64[ns] 208B ...
    VV_quality_range_line                                             (VV_quality_range_azimuth_time) int32 104B dask.array<chunksize=(26,), meta=np.ndarray>
    VV_quality_range_pixel                                            (VV_quality_range_azimuth_time, VV_quality_range_ground_range) int32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>
Dimensions without coordinates: VH_quality_azimuth_azimuth_time,
                                VV_quality_azimuth_azimuth_time
Data variables: (12/78)
    VH_conditions_antenna_pattern_elevation_angle                     (VH_conditions_antenna_pattern_azimuth_time, VH_conditions_antenna_pattern_count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
    VH_conditions_antenna_pattern_incidence_angle                     (VH_conditions_antenna_pattern_azimuth_time, VH_conditions_antenna_pattern_count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
    VH_conditions_antenna_pattern_roll                                (VH_conditions_antenna_pattern_azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
    VH_conditions_antenna_pattern_slant_range_time_ap                 (VH_conditions_antenna_pattern_azimuth_time, VH_conditions_antenna_pattern_count) float64 158kB dask.array<chunksize=(27, 730), meta=np.ndarray>
    VH_conditions_antenna_pattern_swath                               (VH_conditions_antenna_pattern_azimuth_time) <U3 324B dask.array<chunksize=(27,), meta=np.ndarray>
    VH_conditions_antenna_pattern_terrain_height                      (VH_conditions_antenna_pattern_azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
    ...                                                                ...
    VV_quality_calibration_beta_nought                                (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VV_quality_calibration_dn                                         (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VV_quality_calibration_gamma                                      (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VV_quality_calibration_sigma_nought                               (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VV_quality_azimuth_noise_azimuth_lut                              (VV_quality_azimuth_swath, VV_quality_azimuth_azimuth_time) float32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>
    VV_quality_range_noise_range_lut                                  (VV_quality_range_azimuth_time, VV_quality_range_ground_range) float32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>
Attributes: (2)

xarray.Dataset

• Dimensions:
  • VH_conditions_antenna_pattern_azimuth_time: 27
  • VH_conditions_antenna_pattern_count: 730
  • VH_conditions_attitude_azimuth_time: 25
  • VH_conditions_doppler_centroid_azimuth_time: 27
  • VH_conditions_doppler_centroid_degree: 3
  • VH_conditions_gcp_azimuth_time: 10
  • VH_conditions_gcp_ground_range: 21
  • VH_conditions_orbit_azimuth_time: 17
  • VH_conditions_orbit_axis: 3
  • VH_measurements_azimuth_time: 16689
  • VH_measurements_ground_range: 26642
  • VH_quality_calibration_azimuth_time: 27
  • VH_quality_calibration_ground_range: 668
  • VH_quality_azimuth_swath: 3
  • VH_quality_azimuth_azimuth_time: 1688
  • VH_quality_range_azimuth_time: 26
  • VH_quality_range_ground_range: 671
  • VV_conditions_antenna_pattern_azimuth_time: 27
  • VV_conditions_antenna_pattern_count: 730
  • VV_conditions_attitude_azimuth_time: 25
  • VV_conditions_doppler_centroid_azimuth_time: 27
  • VV_conditions_doppler_centroid_degree: 3
  • VV_conditions_gcp_azimuth_time: 10
  • VV_conditions_gcp_ground_range: 21
  • VV_conditions_orbit_azimuth_time: 17
  • VV_conditions_orbit_axis: 3
  • VV_measurements_azimuth_time: 16689
  • VV_measurements_ground_range: 26642
  • VV_quality_calibration_azimuth_time: 27
  • VV_quality_calibration_ground_range: 668
  • VV_quality_azimuth_swath: 3
  • VV_quality_azimuth_azimuth_time: 1688
  • VV_quality_range_azimuth_time: 26
  • VV_quality_range_ground_range: 671
• Coordinates: (58)
  • VH_conditions_antenna_pattern_azimuth_time
    (VH_conditions_antenna_pattern_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:55.425896 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time of antenna pattern measurement

    array(['2026-03-16T16:47:55.425896000', '2026-03-16T16:47:56.276897000',
           '2026-03-16T16:47:57.222452000', '2026-03-16T16:47:58.182397000',
           '2026-03-16T16:47:59.031342000', '2026-03-16T16:47:59.983064000',
           '2026-03-16T16:48:00.938898000', '2026-03-16T16:48:01.791954000',
           '2026-03-16T16:48:02.741621000', '2026-03-16T16:48:03.695399000',
           '2026-03-16T16:48:04.548455000', '2026-03-16T16:48:05.500177000',
           '2026-03-16T16:48:06.453956000', '2026-03-16T16:48:07.307012000',
           '2026-03-16T16:48:08.256678000', '2026-03-16T16:48:09.216623000',
           '2026-03-16T16:48:10.063513000', '2026-03-16T16:48:11.011124000',
           '2026-03-16T16:48:11.966958000', '2026-03-16T16:48:12.822069000',
           '2026-03-16T16:48:13.765569000', '2026-03-16T16:48:14.725514000',
           '2026-03-16T16:48:15.578570000', '2026-03-16T16:48:16.526181000',
           '2026-03-16T16:48:17.488182000', '2026-03-16T16:48:18.337127000',
           '2026-03-16T16:48:19.280627000'], dtype='datetime64[ns]')

  • VH_conditions_antenna_pattern_count
    (VH_conditions_antenna_pattern_count)
    int64
    0 1 2 3 4 5 ... 725 726 727 728 729

    array([  0,   1,   2, ..., 727, 728, 729], shape=(730,))

  • VH_conditions_attitude_azimuth_time
    (VH_conditions_attitude_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:55.000002 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    time stamp at which orbit state vectors apply

    array(['2026-03-16T16:47:55.000002000', '2026-03-16T16:47:55.999998000',
           '2026-03-16T16:47:57.000002000', '2026-03-16T16:47:57.999996000',
           '2026-03-16T16:47:59.000000000', '2026-03-16T16:47:59.999995000',
           '2026-03-16T16:48:01.000000000', '2026-03-16T16:48:01.999995000',
           '2026-03-16T16:48:03.000000000', '2026-03-16T16:48:04.000003000',
           '2026-03-16T16:48:04.999998000', '2026-03-16T16:48:06.000003000',
           '2026-03-16T16:48:06.999997000', '2026-03-16T16:48:08.000002000',
           '2026-03-16T16:48:08.999996000', '2026-03-16T16:48:10.000001000',
           '2026-03-16T16:48:10.999996000', '2026-03-16T16:48:12.000000000',
           '2026-03-16T16:48:12.999995000', '2026-03-16T16:48:14.000000000',
           '2026-03-16T16:48:14.999994000', '2026-03-16T16:48:16.000000000',
           '2026-03-16T16:48:17.000004000', '2026-03-16T16:48:17.999998000',
           '2026-03-16T16:48:19.000004000'], dtype='datetime64[ns]')

  • VH_conditions_doppler_centroid_azimuth_time
    (VH_conditions_doppler_centroid_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:56.021426 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time of terrain height measurement

    array(['2026-03-16T16:47:56.021426000', '2026-03-16T16:47:56.022979000',
           '2026-03-16T16:47:56.035054000', '2026-03-16T16:47:58.779703000',
           '2026-03-16T16:47:58.781256000', '2026-03-16T16:47:58.793331000',
           '2026-03-16T16:48:01.537980000', '2026-03-16T16:48:01.539533000',
           '2026-03-16T16:48:01.551608000', '2026-03-16T16:48:04.296257000',
           '2026-03-16T16:48:04.297810000', '2026-03-16T16:48:04.309885000',
           '2026-03-16T16:48:07.054533000', '2026-03-16T16:48:07.056086000',
           '2026-03-16T16:48:07.068161000', '2026-03-16T16:48:09.812810000',
           '2026-03-16T16:48:09.814363000', '2026-03-16T16:48:09.826438000',
           '2026-03-16T16:48:12.571087000', '2026-03-16T16:48:12.572640000',
           '2026-03-16T16:48:12.584715000', '2026-03-16T16:48:15.329364000',
           '2026-03-16T16:48:15.330917000', '2026-03-16T16:48:15.342992000',
           '2026-03-16T16:48:18.087641000', '2026-03-16T16:48:18.089194000',
           '2026-03-16T16:48:18.101269000'], dtype='datetime64[ns]')

  • VH_conditions_doppler_centroid_degree
    (VH_conditions_doppler_centroid_degree)
    int64
    2 1 0

    array([2, 1, 0])

  • VH_conditions_gcp_azimuth_time
    (VH_conditions_gcp_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:57.762318000',
           '2026-03-16T16:48:00.762803000', '2026-03-16T16:48:03.763288000',
           '2026-03-16T16:48:06.763773000', '2026-03-16T16:48:09.764257000',
           '2026-03-16T16:48:12.764742000', '2026-03-16T16:48:15.765227000',
           '2026-03-16T16:48:18.765712000', '2026-03-16T16:48:19.760381000'],
          dtype='datetime64[ns]')

  • VH_conditions_gcp_ground_range
    (VH_conditions_gcp_ground_range)
    float64
    0.0 1.333e+04 ... 2.664e+05

    array([     0.,  13330.,  26660.,  39990.,  53320.,  66650.,  79980.,  93310.,
           106640., 119970., 133300., 146630., 159960., 173290., 186620., 199950.,
           213280., 226610., 239940., 253270., 266410.])

  • VH_conditions_gcp_line
    (VH_conditions_gcp_azimuth_time)
    uint32
    dask.array<chunksize=(10,), meta=np.ndarray>

    Array Chunk Bytes 40 B 40 B Shape (10,) (10,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VH_conditions_gcp_pixel
    (VH_conditions_gcp_ground_range)
    uint32
    dask.array<chunksize=(21,), meta=np.ndarray>

    Array Chunk Bytes 84 B 84 B Shape (21,) (21,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VH_conditions_orbit_axis
    (VH_conditions_orbit_axis)
    <U1
    'x' 'y' 'z'

    array(['x', 'y', 'z'], dtype='<U1')

  • VH_conditions_orbit_azimuth_time
    (VH_conditions_orbit_azimuth_time)
    datetime64[ns]
    2026-03-16T16:46:46.681692 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    time stamp at which orbit state vectors apply

    array(['2026-03-16T16:46:46.681692000', '2026-03-16T16:46:56.681692000',
           '2026-03-16T16:47:06.681692000', '2026-03-16T16:47:16.681692000',
           '2026-03-16T16:47:26.681692000', '2026-03-16T16:47:36.681693000',
           '2026-03-16T16:47:46.681693000', '2026-03-16T16:47:56.681692000',
           '2026-03-16T16:48:06.681692000', '2026-03-16T16:48:16.681692000',
           '2026-03-16T16:48:26.681692000', '2026-03-16T16:48:36.681692000',
           '2026-03-16T16:48:46.681692000', '2026-03-16T16:48:56.681693000',
           '2026-03-16T16:49:06.681693000', '2026-03-16T16:49:16.681692000',
           '2026-03-16T16:49:26.681692000'], dtype='datetime64[ns]')

  • VH_measurements_azimuth_time
    (VH_measurements_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
           '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
           '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
          shape=(16689,), dtype='datetime64[ns]')

  • VH_measurements_ground_range
    (VH_measurements_ground_range)
    float64
    0.0 10.0 ... 2.664e+05 2.664e+05

    array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
           2.6641e+05], shape=(26642,))

  • VH_measurements_line
    (VH_measurements_azimuth_time)
    int64
    dask.array<chunksize=(2048,), meta=np.ndarray>

    Array Chunk Bytes 130.38 kiB 16.00 kiB Shape (16689,) (2048,) Dask graph 9 chunks in 2 graph layers Data type int64 numpy.ndarray

  • VH_measurements_pixel
    (VH_measurements_ground_range)
    int64
    dask.array<chunksize=(4096,), meta=np.ndarray>

    Array Chunk Bytes 208.14 kiB 32.00 kiB Shape (26642,) (4096,) Dask graph 7 chunks in 2 graph layers Data type int64 numpy.ndarray

  • VH_quality_calibration_azimuth_time
    (VH_quality_calibration_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time at which calibration vector applies

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.761834000',
           '2026-03-16T16:47:56.761834000', '2026-03-16T16:47:57.761834000',
           '2026-03-16T16:47:58.761834000', '2026-03-16T16:47:59.761834000',
           '2026-03-16T16:48:00.761834000', '2026-03-16T16:48:01.761834000',
           '2026-03-16T16:48:02.761834000', '2026-03-16T16:48:03.761834000',
           '2026-03-16T16:48:04.761834000', '2026-03-16T16:48:05.761834000',
           '2026-03-16T16:48:06.761834000', '2026-03-16T16:48:07.761834000',
           '2026-03-16T16:48:08.761834000', '2026-03-16T16:48:09.761834000',
           '2026-03-16T16:48:10.761834000', '2026-03-16T16:48:11.761834000',
           '2026-03-16T16:48:12.761834000', '2026-03-16T16:48:13.761834000',
           '2026-03-16T16:48:14.761834000', '2026-03-16T16:48:15.761834000',
           '2026-03-16T16:48:16.761834000', '2026-03-16T16:48:17.761834000',
           '2026-03-16T16:48:18.761834000', '2026-03-16T16:48:19.761834000',
           '2026-03-16T16:48:20.761834000'], dtype='datetime64[ns]')

  • VH_quality_calibration_ground_range
    (VH_quality_calibration_ground_range)
    float64
    0.0 6.675e+06 ... 4.446e+09

    dimensions :

    ['ground_range']

    dtype :

    <f8

    array([0.000000e+00, 6.675200e+06, 1.335040e+07, ..., 4.439008e+09,
           4.445683e+09, 4.445850e+09], shape=(668,))

  • VH_quality_calibration_line
    (VH_quality_calibration_azimuth_time)
    uint32
    dask.array<chunksize=(27,), meta=np.ndarray>

    dimensions :

    ['azimuth_time']

    dtype :

    <u4

    long_name :

    image line at which the calibration vector applies

    Array Chunk Bytes 108 B 108 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VH_quality_calibration_pixel
    (VH_quality_calibration_ground_range)
    uint32
    dask.array<chunksize=(668,), meta=np.ndarray>

    dimensions :

    ['ground_range']

    dtype :

    <u4

    long_name :

    image pixel at which the calibration vector applies (this array contains the count attribute number of integer values (i.e. one value per point in the noise vector); the maximum length of this array is one value for every pixel in an image line, however in general the vector is subsampled)

    Array Chunk Bytes 2.61 kiB 2.61 kiB Shape (668,) (668,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VH_quality_azimuth_first_azimuth_time
    (VH_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    first azimuth line azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VH_quality_azimuth_first_range_sample
    (VH_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    first range sample azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VH_quality_azimuth_last_azimuth_time
    (VH_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    last azimuth line azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VH_quality_azimuth_last_range_sample
    (VH_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    last range sample azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VH_quality_azimuth_line
    (VH_quality_azimuth_swath, VH_quality_azimuth_azimuth_time)
    int32
    dask.array<chunksize=(3, 1688), meta=np.ndarray>

    dimensions :

    ['swath', 'azimuth_time']

    dtype :

    <i4

    long_name :

    line

    Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VH_quality_azimuth_swath
    (VH_quality_azimuth_swath)
    <U3
    'IW1' 'IW2' 'IW3'

    dimensions :

    ['swath']

    dtype :

    U3

    long_name :

    Swath

    array(['IW1', 'IW2', 'IW3'], dtype='<U3')

  • VH_quality_range_ground_range
    (VH_quality_range_azimuth_time, VH_quality_range_ground_range)
    float64
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    Array Chunk Bytes 136.30 kiB 136.30 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_quality_range_azimuth_time
    (VH_quality_range_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time at which noise vector applies

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.762495000',
           '2026-03-16T16:47:56.763156000', '2026-03-16T16:47:57.763817000',
           '2026-03-16T16:47:58.764478000', '2026-03-16T16:47:59.765139000',
           '2026-03-16T16:48:00.765800000', '2026-03-16T16:48:01.766461000',
           '2026-03-16T16:48:02.767122000', '2026-03-16T16:48:03.767783000',
           '2026-03-16T16:48:04.768444000', '2026-03-16T16:48:05.769104000',
           '2026-03-16T16:48:06.769765000', '2026-03-16T16:48:07.770426000',
           '2026-03-16T16:48:08.771087000', '2026-03-16T16:48:09.771748000',
           '2026-03-16T16:48:10.772409000', '2026-03-16T16:48:11.773070000',
           '2026-03-16T16:48:12.773731000', '2026-03-16T16:48:13.774392000',
           '2026-03-16T16:48:14.775053000', '2026-03-16T16:48:15.775714000',
           '2026-03-16T16:48:16.776375000', '2026-03-16T16:48:17.777036000',
           '2026-03-16T16:48:18.777697000', '2026-03-16T16:48:19.760382000'],
          dtype='datetime64[ns]')

  • VH_quality_range_line
    (VH_quality_range_azimuth_time)
    int32
    dask.array<chunksize=(26,), meta=np.ndarray>

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    line

    Array Chunk Bytes 104 B 104 B Shape (26,) (26,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VH_quality_range_pixel
    (VH_quality_range_azimuth_time, VH_quality_range_ground_range)
    int32
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_conditions_antenna_pattern_azimuth_time
    (VV_conditions_antenna_pattern_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:55.425896 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time of antenna pattern measurement

    array(['2026-03-16T16:47:55.425896000', '2026-03-16T16:47:56.276897000',
           '2026-03-16T16:47:57.222452000', '2026-03-16T16:47:58.182397000',
           '2026-03-16T16:47:59.031342000', '2026-03-16T16:47:59.983064000',
           '2026-03-16T16:48:00.938898000', '2026-03-16T16:48:01.791954000',
           '2026-03-16T16:48:02.741621000', '2026-03-16T16:48:03.695399000',
           '2026-03-16T16:48:04.548455000', '2026-03-16T16:48:05.500177000',
           '2026-03-16T16:48:06.453956000', '2026-03-16T16:48:07.307012000',
           '2026-03-16T16:48:08.256678000', '2026-03-16T16:48:09.216623000',
           '2026-03-16T16:48:10.063513000', '2026-03-16T16:48:11.011124000',
           '2026-03-16T16:48:11.966958000', '2026-03-16T16:48:12.822069000',
           '2026-03-16T16:48:13.765569000', '2026-03-16T16:48:14.725514000',
           '2026-03-16T16:48:15.578570000', '2026-03-16T16:48:16.526181000',
           '2026-03-16T16:48:17.488182000', '2026-03-16T16:48:18.337127000',
           '2026-03-16T16:48:19.280627000'], dtype='datetime64[ns]')

  • VV_conditions_antenna_pattern_count
    (VV_conditions_antenna_pattern_count)
    int64
    0 1 2 3 4 5 ... 725 726 727 728 729

    array([  0,   1,   2, ..., 727, 728, 729], shape=(730,))

  • VV_conditions_attitude_azimuth_time
    (VV_conditions_attitude_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:55.000002 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    time stamp at which orbit state vectors apply

    array(['2026-03-16T16:47:55.000002000', '2026-03-16T16:47:55.999998000',
           '2026-03-16T16:47:57.000002000', '2026-03-16T16:47:57.999996000',
           '2026-03-16T16:47:59.000000000', '2026-03-16T16:47:59.999995000',
           '2026-03-16T16:48:01.000000000', '2026-03-16T16:48:01.999995000',
           '2026-03-16T16:48:03.000000000', '2026-03-16T16:48:04.000003000',
           '2026-03-16T16:48:04.999998000', '2026-03-16T16:48:06.000003000',
           '2026-03-16T16:48:06.999997000', '2026-03-16T16:48:08.000002000',
           '2026-03-16T16:48:08.999996000', '2026-03-16T16:48:10.000001000',
           '2026-03-16T16:48:10.999996000', '2026-03-16T16:48:12.000000000',
           '2026-03-16T16:48:12.999995000', '2026-03-16T16:48:14.000000000',
           '2026-03-16T16:48:14.999994000', '2026-03-16T16:48:16.000000000',
           '2026-03-16T16:48:17.000004000', '2026-03-16T16:48:17.999998000',
           '2026-03-16T16:48:19.000004000'], dtype='datetime64[ns]')

  • VV_conditions_doppler_centroid_azimuth_time
    (VV_conditions_doppler_centroid_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:56.021426 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time of terrain height measurement

    array(['2026-03-16T16:47:56.021426000', '2026-03-16T16:47:56.022979000',
           '2026-03-16T16:47:56.035054000', '2026-03-16T16:47:58.779703000',
           '2026-03-16T16:47:58.781256000', '2026-03-16T16:47:58.793331000',
           '2026-03-16T16:48:01.537980000', '2026-03-16T16:48:01.539533000',
           '2026-03-16T16:48:01.551608000', '2026-03-16T16:48:04.296257000',
           '2026-03-16T16:48:04.297810000', '2026-03-16T16:48:04.309885000',
           '2026-03-16T16:48:07.054533000', '2026-03-16T16:48:07.056086000',
           '2026-03-16T16:48:07.068161000', '2026-03-16T16:48:09.812810000',
           '2026-03-16T16:48:09.814363000', '2026-03-16T16:48:09.826438000',
           '2026-03-16T16:48:12.571087000', '2026-03-16T16:48:12.572640000',
           '2026-03-16T16:48:12.584715000', '2026-03-16T16:48:15.329364000',
           '2026-03-16T16:48:15.330917000', '2026-03-16T16:48:15.342992000',
           '2026-03-16T16:48:18.087641000', '2026-03-16T16:48:18.089194000',
           '2026-03-16T16:48:18.101269000'], dtype='datetime64[ns]')

  • VV_conditions_doppler_centroid_degree
    (VV_conditions_doppler_centroid_degree)
    int64
    2 1 0

    array([2, 1, 0])

  • VV_conditions_gcp_azimuth_time
    (VV_conditions_gcp_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:57.762318000',
           '2026-03-16T16:48:00.762803000', '2026-03-16T16:48:03.763288000',
           '2026-03-16T16:48:06.763773000', '2026-03-16T16:48:09.764257000',
           '2026-03-16T16:48:12.764742000', '2026-03-16T16:48:15.765227000',
           '2026-03-16T16:48:18.765712000', '2026-03-16T16:48:19.760381000'],
          dtype='datetime64[ns]')

  • VV_conditions_gcp_ground_range
    (VV_conditions_gcp_ground_range)
    float64
    0.0 1.333e+04 ... 2.664e+05

    array([     0.,  13330.,  26660.,  39990.,  53320.,  66650.,  79980.,  93310.,
           106640., 119970., 133300., 146630., 159960., 173290., 186620., 199950.,
           213280., 226610., 239940., 253270., 266410.])

  • VV_conditions_gcp_line
    (VV_conditions_gcp_azimuth_time)
    uint32
    dask.array<chunksize=(10,), meta=np.ndarray>

    Array Chunk Bytes 40 B 40 B Shape (10,) (10,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VV_conditions_gcp_pixel
    (VV_conditions_gcp_ground_range)
    uint32
    dask.array<chunksize=(21,), meta=np.ndarray>

    Array Chunk Bytes 84 B 84 B Shape (21,) (21,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VV_conditions_orbit_axis
    (VV_conditions_orbit_axis)
    <U1
    'x' 'y' 'z'

    array(['x', 'y', 'z'], dtype='<U1')

  • VV_conditions_orbit_azimuth_time
    (VV_conditions_orbit_azimuth_time)
    datetime64[ns]
    2026-03-16T16:46:46.681692 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    time stamp at which orbit state vectors apply

    array(['2026-03-16T16:46:46.681692000', '2026-03-16T16:46:56.681692000',
           '2026-03-16T16:47:06.681692000', '2026-03-16T16:47:16.681692000',
           '2026-03-16T16:47:26.681692000', '2026-03-16T16:47:36.681693000',
           '2026-03-16T16:47:46.681693000', '2026-03-16T16:47:56.681692000',
           '2026-03-16T16:48:06.681692000', '2026-03-16T16:48:16.681692000',
           '2026-03-16T16:48:26.681692000', '2026-03-16T16:48:36.681692000',
           '2026-03-16T16:48:46.681692000', '2026-03-16T16:48:56.681693000',
           '2026-03-16T16:49:06.681693000', '2026-03-16T16:49:16.681692000',
           '2026-03-16T16:49:26.681692000'], dtype='datetime64[ns]')

  • VV_measurements_azimuth_time
    (VV_measurements_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
           '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
           '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
          shape=(16689,), dtype='datetime64[ns]')

  • VV_measurements_ground_range
    (VV_measurements_ground_range)
    float64
    0.0 10.0 ... 2.664e+05 2.664e+05

    array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
           2.6641e+05], shape=(26642,))

  • VV_measurements_line
    (VV_measurements_azimuth_time)
    int64
    dask.array<chunksize=(2048,), meta=np.ndarray>

    Array Chunk Bytes 130.38 kiB 16.00 kiB Shape (16689,) (2048,) Dask graph 9 chunks in 2 graph layers Data type int64 numpy.ndarray

  • VV_measurements_pixel
    (VV_measurements_ground_range)
    int64
    dask.array<chunksize=(4096,), meta=np.ndarray>

    Array Chunk Bytes 208.14 kiB 32.00 kiB Shape (26642,) (4096,) Dask graph 7 chunks in 2 graph layers Data type int64 numpy.ndarray

  • VV_quality_calibration_azimuth_time
    (VV_quality_calibration_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time at which calibration vector applies

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.761834000',
           '2026-03-16T16:47:56.761834000', '2026-03-16T16:47:57.761834000',
           '2026-03-16T16:47:58.761834000', '2026-03-16T16:47:59.761834000',
           '2026-03-16T16:48:00.761834000', '2026-03-16T16:48:01.761834000',
           '2026-03-16T16:48:02.761834000', '2026-03-16T16:48:03.761834000',
           '2026-03-16T16:48:04.761834000', '2026-03-16T16:48:05.761834000',
           '2026-03-16T16:48:06.761834000', '2026-03-16T16:48:07.761834000',
           '2026-03-16T16:48:08.761834000', '2026-03-16T16:48:09.761834000',
           '2026-03-16T16:48:10.761834000', '2026-03-16T16:48:11.761834000',
           '2026-03-16T16:48:12.761834000', '2026-03-16T16:48:13.761834000',
           '2026-03-16T16:48:14.761834000', '2026-03-16T16:48:15.761834000',
           '2026-03-16T16:48:16.761834000', '2026-03-16T16:48:17.761834000',
           '2026-03-16T16:48:18.761834000', '2026-03-16T16:48:19.761834000',
           '2026-03-16T16:48:20.761834000'], dtype='datetime64[ns]')

  • VV_quality_calibration_ground_range
    (VV_quality_calibration_ground_range)
    float64
    0.0 6.675e+06 ... 4.446e+09

    dimensions :

    ['ground_range']

    dtype :

    <f8

    array([0.000000e+00, 6.675200e+06, 1.335040e+07, ..., 4.439008e+09,
           4.445683e+09, 4.445850e+09], shape=(668,))

  • VV_quality_calibration_line
    (VV_quality_calibration_azimuth_time)
    uint32
    dask.array<chunksize=(27,), meta=np.ndarray>

    dimensions :

    ['azimuth_time']

    dtype :

    <u4

    long_name :

    image line at which the calibration vector applies

    Array Chunk Bytes 108 B 108 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VV_quality_calibration_pixel
    (VV_quality_calibration_ground_range)
    uint32
    dask.array<chunksize=(668,), meta=np.ndarray>

    dimensions :

    ['ground_range']

    dtype :

    <u4

    long_name :

    image pixel at which the calibration vector applies (this array contains the count attribute number of integer values (i.e. one value per point in the noise vector); the maximum length of this array is one value for every pixel in an image line, however in general the vector is subsampled)

    Array Chunk Bytes 2.61 kiB 2.61 kiB Shape (668,) (668,) Dask graph 1 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • VV_quality_azimuth_first_azimuth_time
    (VV_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    first azimuth line azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_quality_azimuth_first_range_sample
    (VV_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    first range sample azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_quality_azimuth_last_azimuth_time
    (VV_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    last azimuth line azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_quality_azimuth_last_range_sample
    (VV_quality_azimuth_swath)
    int32
    dask.array<chunksize=(3,), meta=np.ndarray>

    dtype :

    <i4

    long_name :

    last range sample azimuth thermal noise correction

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_quality_azimuth_line
    (VV_quality_azimuth_swath, VV_quality_azimuth_azimuth_time)
    int32
    dask.array<chunksize=(3, 1688), meta=np.ndarray>

    dimensions :

    ['swath', 'azimuth_time']

    dtype :

    <i4

    long_name :

    line

    Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_quality_azimuth_swath
    (VV_quality_azimuth_swath)
    <U3
    'IW1' 'IW2' 'IW3'

    dimensions :

    ['swath']

    dtype :

    U3

    long_name :

    Swath

    array(['IW1', 'IW2', 'IW3'], dtype='<U3')

  • VV_quality_range_ground_range
    (VV_quality_range_azimuth_time, VV_quality_range_ground_range)
    float64
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    Array Chunk Bytes 136.30 kiB 136.30 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_quality_range_azimuth_time
    (VV_quality_range_azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time at which noise vector applies

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.762495000',
           '2026-03-16T16:47:56.763156000', '2026-03-16T16:47:57.763817000',
           '2026-03-16T16:47:58.764478000', '2026-03-16T16:47:59.765139000',
           '2026-03-16T16:48:00.765800000', '2026-03-16T16:48:01.766461000',
           '2026-03-16T16:48:02.767122000', '2026-03-16T16:48:03.767783000',
           '2026-03-16T16:48:04.768444000', '2026-03-16T16:48:05.769104000',
           '2026-03-16T16:48:06.769765000', '2026-03-16T16:48:07.770426000',
           '2026-03-16T16:48:08.771087000', '2026-03-16T16:48:09.771748000',
           '2026-03-16T16:48:10.772409000', '2026-03-16T16:48:11.773070000',
           '2026-03-16T16:48:12.773731000', '2026-03-16T16:48:13.774392000',
           '2026-03-16T16:48:14.775053000', '2026-03-16T16:48:15.775714000',
           '2026-03-16T16:48:16.776375000', '2026-03-16T16:48:17.777036000',
           '2026-03-16T16:48:18.777697000', '2026-03-16T16:48:19.760382000'],
          dtype='datetime64[ns]')

  • VV_quality_range_line
    (VV_quality_range_azimuth_time)
    int32
    dask.array<chunksize=(26,), meta=np.ndarray>

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    line

    Array Chunk Bytes 104 B 104 B Shape (26,) (26,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • VV_quality_range_pixel
    (VV_quality_range_azimuth_time, VV_quality_range_ground_range)
    int32
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

• Data variables: (78)
  • VH_conditions_antenna_pattern_elevation_angle
    (VH_conditions_antenna_pattern_azimuth_time, VH_conditions_antenna_pattern_count)
    float32
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'elevation angle to grid point', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    elevation angle to grid point

    units :

    degrees

    Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_antenna_pattern_incidence_angle
    (VH_conditions_antenna_pattern_azimuth_time, VH_conditions_antenna_pattern_count)
    float32
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'incidence angle to grid point', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    incidence angle to grid point

    units :

    degrees

    Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_antenna_pattern_roll
    (VH_conditions_antenna_pattern_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'estimated roll angle for this antenna pattern', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    estimated roll angle for this antenna pattern

    units :

    degrees

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_antenna_pattern_slant_range_time_ap
    (VH_conditions_antenna_pattern_azimuth_time, VH_conditions_antenna_pattern_count)
    float64
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f8', 'long_name': 'two-way slant range time to sample', 'units': 's'}

    dtype :

    <f8

    long_name :

    two-way slant range time to sample

    units :

    s

    Array Chunk Bytes 153.98 kiB 153.98 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_antenna_pattern_swath
    (VH_conditions_antenna_pattern_azimuth_time)
    <U3
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': 'U3', 'long_name': 'Swath'}

    dtype :

    U3

    long_name :

    Swath

    Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type

  • VH_conditions_antenna_pattern_terrain_height
    (VH_conditions_antenna_pattern_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time', 'units': 'm'}

    dtype :

    <f8

    long_name :

    mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time

    units :

    m

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_attitude_pitch
    (VH_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform pitch angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform pitch angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_attitude_q0
    (VH_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q0 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q0 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_attitude_q1
    (VH_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q1 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q1 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_attitude_q2
    (VH_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q2 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q2 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_attitude_q3
    (VH_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q3 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q3 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_attitude_roll
    (VH_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform roll angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform roll angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_attitude_wx
    (VH_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'x component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    x component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_attitude_wy
    (VH_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'y component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    y component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_attitude_wz
    (VH_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'z component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    z component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_attitude_yaw
    (VH_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform yaw angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform yaw angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_doppler_centroid_data_dc_polynomial
    (VH_conditions_doppler_centroid_azimuth_time, VH_conditions_doppler_centroid_degree)
    float32
    dask.array<chunksize=(27, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

    dtype :

    <f4

    long_name :

    doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

    Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_doppler_centroid_data_dc_rms_error
    (VH_conditions_doppler_centroid_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)'}

    dtype :

    <f8

    long_name :

    RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_doppler_centroid_data_dc_rms_error_above_threshold
    (VH_conditions_doppler_centroid_azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VH_conditions_doppler_centroid_fine_dce_azimuth_start_time
    (VH_conditions_doppler_centroid_azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VH_conditions_doppler_centroid_fine_dce_azimuth_stop_time
    (VH_conditions_doppler_centroid_azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VH_conditions_doppler_centroid_geometry_dc_polynomial
    (VH_conditions_doppler_centroid_azimuth_time, VH_conditions_doppler_centroid_degree)
    float32
    dask.array<chunksize=(27, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

    dtype :

    <f4

    long_name :

    doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

    Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_conditions_doppler_centroid_t0
    (VH_conditions_doppler_centroid_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'two-way slant range time origin for doppler centroid estimate', 'units': 's'}

    dtype :

    <f8

    long_name :

    two-way slant range time origin for doppler centroid estimate

    units :

    s

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_gcp_azimuth_time_gcp
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    datetime64[ns]
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • VH_conditions_gcp_elevation_angle
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_gcp_height
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_gcp_incidence_angle
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_gcp_latitude
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_gcp_longitude
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_gcp_slant_range_time_gcp
    (VH_conditions_gcp_azimuth_time, VH_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_orbit_position
    (VH_conditions_orbit_azimuth_time, VH_conditions_orbit_axis)
    float64
    dask.array<chunksize=(17, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'position vector', 'units': 'm'}

    dtype :

    <f8

    long_name :

    position vector

    units :

    m

    Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_conditions_orbit_velocity
    (VH_conditions_orbit_azimuth_time, VH_conditions_orbit_axis)
    float64
    dask.array<chunksize=(17, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'velocity vector', 'units': 'm/s'}

    dtype :

    <f8

    long_name :

    velocity vector

    units :

    m/s

    Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH_measurements_grd
    (VH_measurements_azimuth_time, VH_measurements_ground_range)
    uint16
    dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

    dtype :

    <u2

    long_name :

    measurement data set for GRD IW

    Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • VH_quality_calibration_beta_nought
    (VH_quality_calibration_azimuth_time, VH_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_quality_calibration_dn
    (VH_quality_calibration_azimuth_time, VH_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_quality_calibration_gamma
    (VH_quality_calibration_azimuth_time, VH_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_quality_calibration_sigma_nought
    (VH_quality_calibration_azimuth_time, VH_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_quality_azimuth_noise_azimuth_lut
    (VH_quality_azimuth_swath, VH_quality_azimuth_azimuth_time)
    float32
    dask.array<chunksize=(3, 1688), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['line', 'swath', 'first_azimuth_time', 'last_azimuth_time', 'first_range_sample', 'last_range_sample'], 'dimensions': ['swath', 'azimuth_time'], 'dtype': '<f4', 'long_name': 'azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

    dtype :

    <f4

    long_name :

    azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)

    Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH_quality_range_noise_range_lut
    (VH_quality_range_azimuth_time, VH_quality_range_ground_range)
    float32
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'range thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

    dtype :

    <f4

    long_name :

    range thermal noise correction vector power values (this array contains the count attribute number of floating point values)

    Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_antenna_pattern_elevation_angle
    (VV_conditions_antenna_pattern_azimuth_time, VV_conditions_antenna_pattern_count)
    float32
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'elevation angle to grid point', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    elevation angle to grid point

    units :

    degrees

    Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_antenna_pattern_incidence_angle
    (VV_conditions_antenna_pattern_azimuth_time, VV_conditions_antenna_pattern_count)
    float32
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'incidence angle to grid point', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    incidence angle to grid point

    units :

    degrees

    Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_antenna_pattern_roll
    (VV_conditions_antenna_pattern_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'estimated roll angle for this antenna pattern', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    estimated roll angle for this antenna pattern

    units :

    degrees

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_antenna_pattern_slant_range_time_ap
    (VV_conditions_antenna_pattern_azimuth_time, VV_conditions_antenna_pattern_count)
    float64
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f8', 'long_name': 'two-way slant range time to sample', 'units': 's'}

    dtype :

    <f8

    long_name :

    two-way slant range time to sample

    units :

    s

    Array Chunk Bytes 153.98 kiB 153.98 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_antenna_pattern_swath
    (VV_conditions_antenna_pattern_azimuth_time)
    <U3
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': 'U3', 'long_name': 'Swath'}

    dtype :

    U3

    long_name :

    Swath

    Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type

  • VV_conditions_antenna_pattern_terrain_height
    (VV_conditions_antenna_pattern_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time', 'units': 'm'}

    dtype :

    <f8

    long_name :

    mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time

    units :

    m

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_attitude_pitch
    (VV_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform pitch angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform pitch angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_attitude_q0
    (VV_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q0 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q0 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_attitude_q1
    (VV_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q1 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q1 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_attitude_q2
    (VV_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q2 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q2 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_attitude_q3
    (VV_conditions_attitude_azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q3 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q3 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_attitude_roll
    (VV_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform roll angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform roll angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_attitude_wx
    (VV_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'x component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    x component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_attitude_wy
    (VV_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'y component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    y component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_attitude_wz
    (VV_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'z component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    z component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_attitude_yaw
    (VV_conditions_attitude_azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform yaw angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform yaw angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_doppler_centroid_data_dc_polynomial
    (VV_conditions_doppler_centroid_azimuth_time, VV_conditions_doppler_centroid_degree)
    float32
    dask.array<chunksize=(27, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

    dtype :

    <f4

    long_name :

    doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

    Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_doppler_centroid_data_dc_rms_error
    (VV_conditions_doppler_centroid_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)'}

    dtype :

    <f8

    long_name :

    RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_doppler_centroid_data_dc_rms_error_above_threshold
    (VV_conditions_doppler_centroid_azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VV_conditions_doppler_centroid_fine_dce_azimuth_start_time
    (VV_conditions_doppler_centroid_azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VV_conditions_doppler_centroid_fine_dce_azimuth_stop_time
    (VV_conditions_doppler_centroid_azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VV_conditions_doppler_centroid_geometry_dc_polynomial
    (VV_conditions_doppler_centroid_azimuth_time, VV_conditions_doppler_centroid_degree)
    float32
    dask.array<chunksize=(27, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

    dtype :

    <f4

    long_name :

    doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

    Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_conditions_doppler_centroid_t0
    (VV_conditions_doppler_centroid_azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'two-way slant range time origin for doppler centroid estimate', 'units': 's'}

    dtype :

    <f8

    long_name :

    two-way slant range time origin for doppler centroid estimate

    units :

    s

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_gcp_azimuth_time_gcp
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    datetime64[ns]
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • VV_conditions_gcp_elevation_angle
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_gcp_height
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_gcp_incidence_angle
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_gcp_latitude
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_gcp_longitude
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_gcp_slant_range_time_gcp
    (VV_conditions_gcp_azimuth_time, VV_conditions_gcp_ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_orbit_position
    (VV_conditions_orbit_azimuth_time, VV_conditions_orbit_axis)
    float64
    dask.array<chunksize=(17, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'position vector', 'units': 'm'}

    dtype :

    <f8

    long_name :

    position vector

    units :

    m

    Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_conditions_orbit_velocity
    (VV_conditions_orbit_azimuth_time, VV_conditions_orbit_axis)
    float64
    dask.array<chunksize=(17, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'velocity vector', 'units': 'm/s'}

    dtype :

    <f8

    long_name :

    velocity vector

    units :

    m/s

    Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VV_measurements_grd
    (VV_measurements_azimuth_time, VV_measurements_ground_range)
    uint16
    dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

    dtype :

    <u2

    long_name :

    measurement data set for GRD IW

    Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • VV_quality_calibration_beta_nought
    (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_quality_calibration_dn
    (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_quality_calibration_gamma
    (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_quality_calibration_sigma_nought
    (VV_quality_calibration_azimuth_time, VV_quality_calibration_ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_quality_azimuth_noise_azimuth_lut
    (VV_quality_azimuth_swath, VV_quality_azimuth_azimuth_time)
    float32
    dask.array<chunksize=(3, 1688), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['line', 'swath', 'first_azimuth_time', 'last_azimuth_time', 'first_range_sample', 'last_range_sample'], 'dimensions': ['swath', 'azimuth_time'], 'dtype': '<f4', 'long_name': 'azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

    dtype :

    <f4

    long_name :

    azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)

    Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV_quality_range_noise_range_lut
    (VV_quality_range_azimuth_time, VV_quality_range_ground_range)
    float32
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'range thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

    dtype :

    <f4

    long_name :

    range thermal noise correction vector power values (this array contains the count attribute number of floating point values)

    Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (30)
  • VH_conditions_antenna_pattern_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:55.425896', '2026-03-16 16:47:56.276897',
                   '2026-03-16 16:47:57.222452', '2026-03-16 16:47:58.182397',
                   '2026-03-16 16:47:59.031342', '2026-03-16 16:47:59.983064',
                   '2026-03-16 16:48:00.938898', '2026-03-16 16:48:01.791954',
                   '2026-03-16 16:48:02.741621', '2026-03-16 16:48:03.695399',
                   '2026-03-16 16:48:04.548455', '2026-03-16 16:48:05.500177',
                   '2026-03-16 16:48:06.453956', '2026-03-16 16:48:07.307012',
                   '2026-03-16 16:48:08.256678', '2026-03-16 16:48:09.216623',
                   '2026-03-16 16:48:10.063513', '2026-03-16 16:48:11.011124',
                   '2026-03-16 16:48:11.966958', '2026-03-16 16:48:12.822069',
                   '2026-03-16 16:48:13.765569', '2026-03-16 16:48:14.725514',
                   '2026-03-16 16:48:15.578570', '2026-03-16 16:48:16.526181',
                   '2026-03-16 16:48:17.488182', '2026-03-16 16:48:18.337127',
                   '2026-03-16 16:48:19.280627'],
                  dtype='datetime64[ns]', name='VH_conditions_antenna_pattern_azimuth_time', freq=None))

  • VH_conditions_antenna_pattern_count
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           720, 721, 722, 723, 724, 725, 726, 727, 728, 729],
          dtype='int64', name='VH_conditions_antenna_pattern_count', length=730))

  • VH_conditions_attitude_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:55.000002', '2026-03-16 16:47:55.999998',
                   '2026-03-16 16:47:57.000002', '2026-03-16 16:47:57.999996',
                          '2026-03-16 16:47:59', '2026-03-16 16:47:59.999995',
                          '2026-03-16 16:48:01', '2026-03-16 16:48:01.999995',
                          '2026-03-16 16:48:03', '2026-03-16 16:48:04.000003',
                   '2026-03-16 16:48:04.999998', '2026-03-16 16:48:06.000003',
                   '2026-03-16 16:48:06.999997', '2026-03-16 16:48:08.000002',
                   '2026-03-16 16:48:08.999996', '2026-03-16 16:48:10.000001',
                   '2026-03-16 16:48:10.999996',        '2026-03-16 16:48:12',
                   '2026-03-16 16:48:12.999995',        '2026-03-16 16:48:14',
                   '2026-03-16 16:48:14.999994',        '2026-03-16 16:48:16',
                   '2026-03-16 16:48:17.000004', '2026-03-16 16:48:17.999998',
                   '2026-03-16 16:48:19.000004'],
                  dtype='datetime64[ns]', name='VH_conditions_attitude_azimuth_time', freq=None))

  • VH_conditions_doppler_centroid_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:56.021426', '2026-03-16 16:47:56.022979',
                   '2026-03-16 16:47:56.035054', '2026-03-16 16:47:58.779703',
                   '2026-03-16 16:47:58.781256', '2026-03-16 16:47:58.793331',
                   '2026-03-16 16:48:01.537980', '2026-03-16 16:48:01.539533',
                   '2026-03-16 16:48:01.551608', '2026-03-16 16:48:04.296257',
                   '2026-03-16 16:48:04.297810', '2026-03-16 16:48:04.309885',
                   '2026-03-16 16:48:07.054533', '2026-03-16 16:48:07.056086',
                   '2026-03-16 16:48:07.068161', '2026-03-16 16:48:09.812810',
                   '2026-03-16 16:48:09.814363', '2026-03-16 16:48:09.826438',
                   '2026-03-16 16:48:12.571087', '2026-03-16 16:48:12.572640',
                   '2026-03-16 16:48:12.584715', '2026-03-16 16:48:15.329364',
                   '2026-03-16 16:48:15.330917', '2026-03-16 16:48:15.342992',
                   '2026-03-16 16:48:18.087641', '2026-03-16 16:48:18.089194',
                   '2026-03-16 16:48:18.101269'],
                  dtype='datetime64[ns]', name='VH_conditions_doppler_centroid_azimuth_time', freq=None))

  • VH_conditions_doppler_centroid_degree
    PandasIndex

    PandasIndex(Index([2, 1, 0], dtype='int64', name='VH_conditions_doppler_centroid_degree'))

  • VH_conditions_gcp_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:57.762318',
                   '2026-03-16 16:48:00.762803', '2026-03-16 16:48:03.763288',
                   '2026-03-16 16:48:06.763773', '2026-03-16 16:48:09.764257',
                   '2026-03-16 16:48:12.764742', '2026-03-16 16:48:15.765227',
                   '2026-03-16 16:48:18.765712', '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='VH_conditions_gcp_azimuth_time', freq=None))

  • VH_conditions_gcp_ground_range
    PandasIndex

    PandasIndex(Index([     0.0,  13330.0,  26660.0,  39990.0,  53320.0,  66650.0,  79980.0,
            93310.0, 106640.0, 119970.0, 133300.0, 146630.0, 159960.0, 173290.0,
           186620.0, 199950.0, 213280.0, 226610.0, 239940.0, 253270.0, 266410.0],
          dtype='float64', name='VH_conditions_gcp_ground_range'))

  • VH_conditions_orbit_axis
    PandasIndex

    PandasIndex(Index(['x', 'y', 'z'], dtype='object', name='VH_conditions_orbit_axis'))

  • VH_conditions_orbit_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:46:46.681692', '2026-03-16 16:46:56.681692',
                   '2026-03-16 16:47:06.681692', '2026-03-16 16:47:16.681692',
                   '2026-03-16 16:47:26.681692', '2026-03-16 16:47:36.681693',
                   '2026-03-16 16:47:46.681693', '2026-03-16 16:47:56.681692',
                   '2026-03-16 16:48:06.681692', '2026-03-16 16:48:16.681692',
                   '2026-03-16 16:48:26.681692', '2026-03-16 16:48:36.681692',
                   '2026-03-16 16:48:46.681692', '2026-03-16 16:48:56.681693',
                   '2026-03-16 16:49:06.681693', '2026-03-16 16:49:16.681692',
                   '2026-03-16 16:49:26.681692'],
                  dtype='datetime64[ns]', name='VH_conditions_orbit_azimuth_time', freq=None))

  • VH_measurements_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex([   '2026-03-16 16:47:54.761834',
                   '2026-03-16 16:47:54.763331995',
                   '2026-03-16 16:47:54.764829990',
                   '2026-03-16 16:47:54.766327986',
                   '2026-03-16 16:47:54.767825981',
                   '2026-03-16 16:47:54.769323976',
                   '2026-03-16 16:47:54.770821972',
                   '2026-03-16 16:47:54.772319967',
                   '2026-03-16 16:47:54.773817963',
                   '2026-03-16 16:47:54.775315958',
                   ...
                   '2026-03-16 16:48:19.746899041',
                   '2026-03-16 16:48:19.748397036',
                   '2026-03-16 16:48:19.749895032',
                   '2026-03-16 16:48:19.751393027',
                   '2026-03-16 16:48:19.752891023',
                   '2026-03-16 16:48:19.754389018',
                   '2026-03-16 16:48:19.755887013',
                   '2026-03-16 16:48:19.757385009',
                   '2026-03-16 16:48:19.758883004',
                      '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='VH_measurements_azimuth_time', length=16689, freq=None))

  • VH_measurements_ground_range
    PandasIndex

    PandasIndex(Index([     0.0,     10.0,     20.0,     30.0,     40.0,     50.0,     60.0,
               70.0,     80.0,     90.0,
           ...
           266320.0, 266330.0, 266340.0, 266350.0, 266360.0, 266370.0, 266380.0,
           266390.0, 266400.0, 266410.0],
          dtype='float64', name='VH_measurements_ground_range', length=26642))

  • VH_quality_calibration_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:55.761834',
                   '2026-03-16 16:47:56.761834', '2026-03-16 16:47:57.761834',
                   '2026-03-16 16:47:58.761834', '2026-03-16 16:47:59.761834',
                   '2026-03-16 16:48:00.761834', '2026-03-16 16:48:01.761834',
                   '2026-03-16 16:48:02.761834', '2026-03-16 16:48:03.761834',
                   '2026-03-16 16:48:04.761834', '2026-03-16 16:48:05.761834',
                   '2026-03-16 16:48:06.761834', '2026-03-16 16:48:07.761834',
                   '2026-03-16 16:48:08.761834', '2026-03-16 16:48:09.761834',
                   '2026-03-16 16:48:10.761834', '2026-03-16 16:48:11.761834',
                   '2026-03-16 16:48:12.761834', '2026-03-16 16:48:13.761834',
                   '2026-03-16 16:48:14.761834', '2026-03-16 16:48:15.761834',
                   '2026-03-16 16:48:16.761834', '2026-03-16 16:48:17.761834',
                   '2026-03-16 16:48:18.761834', '2026-03-16 16:48:19.761834',
                   '2026-03-16 16:48:20.761834'],
                  dtype='datetime64[ns]', name='VH_quality_calibration_azimuth_time', freq=None))

  • VH_quality_calibration_ground_range
    PandasIndex

    PandasIndex(Index([         0.0,    6675200.0,   13350400.0,   20025600.0,   26700800.0,
             33376000.0,   40051200.0,   46726400.0,   53401600.0,   60076800.0,
           ...
           4392281600.0, 4398956800.0, 4405632000.0, 4412307200.0, 4418982400.0,
           4425657600.0, 4432332800.0, 4439008000.0, 4445683200.0, 4445850080.0],
          dtype='float64', name='VH_quality_calibration_ground_range', length=668))

  • VH_quality_azimuth_swath
    PandasIndex

    PandasIndex(Index(['IW1', 'IW2', 'IW3'], dtype='object', name='VH_quality_azimuth_swath'))

  • VH_quality_range_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:55.762495',
                   '2026-03-16 16:47:56.763156', '2026-03-16 16:47:57.763817',
                   '2026-03-16 16:47:58.764478', '2026-03-16 16:47:59.765139',
                   '2026-03-16 16:48:00.765800', '2026-03-16 16:48:01.766461',
                   '2026-03-16 16:48:02.767122', '2026-03-16 16:48:03.767783',
                   '2026-03-16 16:48:04.768444', '2026-03-16 16:48:05.769104',
                   '2026-03-16 16:48:06.769765', '2026-03-16 16:48:07.770426',
                   '2026-03-16 16:48:08.771087', '2026-03-16 16:48:09.771748',
                   '2026-03-16 16:48:10.772409', '2026-03-16 16:48:11.773070',
                   '2026-03-16 16:48:12.773731', '2026-03-16 16:48:13.774392',
                   '2026-03-16 16:48:14.775053', '2026-03-16 16:48:15.775714',
                   '2026-03-16 16:48:16.776375', '2026-03-16 16:48:17.777036',
                   '2026-03-16 16:48:18.777697', '2026-03-16 16:48:19.760382'],
                  dtype='datetime64[ns]', name='VH_quality_range_azimuth_time', freq=None))

  • VV_conditions_antenna_pattern_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:55.425896', '2026-03-16 16:47:56.276897',
                   '2026-03-16 16:47:57.222452', '2026-03-16 16:47:58.182397',
                   '2026-03-16 16:47:59.031342', '2026-03-16 16:47:59.983064',
                   '2026-03-16 16:48:00.938898', '2026-03-16 16:48:01.791954',
                   '2026-03-16 16:48:02.741621', '2026-03-16 16:48:03.695399',
                   '2026-03-16 16:48:04.548455', '2026-03-16 16:48:05.500177',
                   '2026-03-16 16:48:06.453956', '2026-03-16 16:48:07.307012',
                   '2026-03-16 16:48:08.256678', '2026-03-16 16:48:09.216623',
                   '2026-03-16 16:48:10.063513', '2026-03-16 16:48:11.011124',
                   '2026-03-16 16:48:11.966958', '2026-03-16 16:48:12.822069',
                   '2026-03-16 16:48:13.765569', '2026-03-16 16:48:14.725514',
                   '2026-03-16 16:48:15.578570', '2026-03-16 16:48:16.526181',
                   '2026-03-16 16:48:17.488182', '2026-03-16 16:48:18.337127',
                   '2026-03-16 16:48:19.280627'],
                  dtype='datetime64[ns]', name='VV_conditions_antenna_pattern_azimuth_time', freq=None))

  • VV_conditions_antenna_pattern_count
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           720, 721, 722, 723, 724, 725, 726, 727, 728, 729],
          dtype='int64', name='VV_conditions_antenna_pattern_count', length=730))

  • VV_conditions_attitude_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:55.000002', '2026-03-16 16:47:55.999998',
                   '2026-03-16 16:47:57.000002', '2026-03-16 16:47:57.999996',
                          '2026-03-16 16:47:59', '2026-03-16 16:47:59.999995',
                          '2026-03-16 16:48:01', '2026-03-16 16:48:01.999995',
                          '2026-03-16 16:48:03', '2026-03-16 16:48:04.000003',
                   '2026-03-16 16:48:04.999998', '2026-03-16 16:48:06.000003',
                   '2026-03-16 16:48:06.999997', '2026-03-16 16:48:08.000002',
                   '2026-03-16 16:48:08.999996', '2026-03-16 16:48:10.000001',
                   '2026-03-16 16:48:10.999996',        '2026-03-16 16:48:12',
                   '2026-03-16 16:48:12.999995',        '2026-03-16 16:48:14',
                   '2026-03-16 16:48:14.999994',        '2026-03-16 16:48:16',
                   '2026-03-16 16:48:17.000004', '2026-03-16 16:48:17.999998',
                   '2026-03-16 16:48:19.000004'],
                  dtype='datetime64[ns]', name='VV_conditions_attitude_azimuth_time', freq=None))

  • VV_conditions_doppler_centroid_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:56.021426', '2026-03-16 16:47:56.022979',
                   '2026-03-16 16:47:56.035054', '2026-03-16 16:47:58.779703',
                   '2026-03-16 16:47:58.781256', '2026-03-16 16:47:58.793331',
                   '2026-03-16 16:48:01.537980', '2026-03-16 16:48:01.539533',
                   '2026-03-16 16:48:01.551608', '2026-03-16 16:48:04.296257',
                   '2026-03-16 16:48:04.297810', '2026-03-16 16:48:04.309885',
                   '2026-03-16 16:48:07.054533', '2026-03-16 16:48:07.056086',
                   '2026-03-16 16:48:07.068161', '2026-03-16 16:48:09.812810',
                   '2026-03-16 16:48:09.814363', '2026-03-16 16:48:09.826438',
                   '2026-03-16 16:48:12.571087', '2026-03-16 16:48:12.572640',
                   '2026-03-16 16:48:12.584715', '2026-03-16 16:48:15.329364',
                   '2026-03-16 16:48:15.330917', '2026-03-16 16:48:15.342992',
                   '2026-03-16 16:48:18.087641', '2026-03-16 16:48:18.089194',
                   '2026-03-16 16:48:18.101269'],
                  dtype='datetime64[ns]', name='VV_conditions_doppler_centroid_azimuth_time', freq=None))

  • VV_conditions_doppler_centroid_degree
    PandasIndex

    PandasIndex(Index([2, 1, 0], dtype='int64', name='VV_conditions_doppler_centroid_degree'))

  • VV_conditions_gcp_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:57.762318',
                   '2026-03-16 16:48:00.762803', '2026-03-16 16:48:03.763288',
                   '2026-03-16 16:48:06.763773', '2026-03-16 16:48:09.764257',
                   '2026-03-16 16:48:12.764742', '2026-03-16 16:48:15.765227',
                   '2026-03-16 16:48:18.765712', '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='VV_conditions_gcp_azimuth_time', freq=None))

  • VV_conditions_gcp_ground_range
    PandasIndex

    PandasIndex(Index([     0.0,  13330.0,  26660.0,  39990.0,  53320.0,  66650.0,  79980.0,
            93310.0, 106640.0, 119970.0, 133300.0, 146630.0, 159960.0, 173290.0,
           186620.0, 199950.0, 213280.0, 226610.0, 239940.0, 253270.0, 266410.0],
          dtype='float64', name='VV_conditions_gcp_ground_range'))

  • VV_conditions_orbit_axis
    PandasIndex

    PandasIndex(Index(['x', 'y', 'z'], dtype='object', name='VV_conditions_orbit_axis'))

  • VV_conditions_orbit_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:46:46.681692', '2026-03-16 16:46:56.681692',
                   '2026-03-16 16:47:06.681692', '2026-03-16 16:47:16.681692',
                   '2026-03-16 16:47:26.681692', '2026-03-16 16:47:36.681693',
                   '2026-03-16 16:47:46.681693', '2026-03-16 16:47:56.681692',
                   '2026-03-16 16:48:06.681692', '2026-03-16 16:48:16.681692',
                   '2026-03-16 16:48:26.681692', '2026-03-16 16:48:36.681692',
                   '2026-03-16 16:48:46.681692', '2026-03-16 16:48:56.681693',
                   '2026-03-16 16:49:06.681693', '2026-03-16 16:49:16.681692',
                   '2026-03-16 16:49:26.681692'],
                  dtype='datetime64[ns]', name='VV_conditions_orbit_azimuth_time', freq=None))

  • VV_measurements_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex([   '2026-03-16 16:47:54.761834',
                   '2026-03-16 16:47:54.763331995',
                   '2026-03-16 16:47:54.764829990',
                   '2026-03-16 16:47:54.766327986',
                   '2026-03-16 16:47:54.767825981',
                   '2026-03-16 16:47:54.769323976',
                   '2026-03-16 16:47:54.770821972',
                   '2026-03-16 16:47:54.772319967',
                   '2026-03-16 16:47:54.773817963',
                   '2026-03-16 16:47:54.775315958',
                   ...
                   '2026-03-16 16:48:19.746899041',
                   '2026-03-16 16:48:19.748397036',
                   '2026-03-16 16:48:19.749895032',
                   '2026-03-16 16:48:19.751393027',
                   '2026-03-16 16:48:19.752891023',
                   '2026-03-16 16:48:19.754389018',
                   '2026-03-16 16:48:19.755887013',
                   '2026-03-16 16:48:19.757385009',
                   '2026-03-16 16:48:19.758883004',
                      '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='VV_measurements_azimuth_time', length=16689, freq=None))

  • VV_measurements_ground_range
    PandasIndex

    PandasIndex(Index([     0.0,     10.0,     20.0,     30.0,     40.0,     50.0,     60.0,
               70.0,     80.0,     90.0,
           ...
           266320.0, 266330.0, 266340.0, 266350.0, 266360.0, 266370.0, 266380.0,
           266390.0, 266400.0, 266410.0],
          dtype='float64', name='VV_measurements_ground_range', length=26642))

  • VV_quality_calibration_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:55.761834',
                   '2026-03-16 16:47:56.761834', '2026-03-16 16:47:57.761834',
                   '2026-03-16 16:47:58.761834', '2026-03-16 16:47:59.761834',
                   '2026-03-16 16:48:00.761834', '2026-03-16 16:48:01.761834',
                   '2026-03-16 16:48:02.761834', '2026-03-16 16:48:03.761834',
                   '2026-03-16 16:48:04.761834', '2026-03-16 16:48:05.761834',
                   '2026-03-16 16:48:06.761834', '2026-03-16 16:48:07.761834',
                   '2026-03-16 16:48:08.761834', '2026-03-16 16:48:09.761834',
                   '2026-03-16 16:48:10.761834', '2026-03-16 16:48:11.761834',
                   '2026-03-16 16:48:12.761834', '2026-03-16 16:48:13.761834',
                   '2026-03-16 16:48:14.761834', '2026-03-16 16:48:15.761834',
                   '2026-03-16 16:48:16.761834', '2026-03-16 16:48:17.761834',
                   '2026-03-16 16:48:18.761834', '2026-03-16 16:48:19.761834',
                   '2026-03-16 16:48:20.761834'],
                  dtype='datetime64[ns]', name='VV_quality_calibration_azimuth_time', freq=None))

  • VV_quality_calibration_ground_range
    PandasIndex

    PandasIndex(Index([         0.0,    6675200.0,   13350400.0,   20025600.0,   26700800.0,
             33376000.0,   40051200.0,   46726400.0,   53401600.0,   60076800.0,
           ...
           4392281600.0, 4398956800.0, 4405632000.0, 4412307200.0, 4418982400.0,
           4425657600.0, 4432332800.0, 4439008000.0, 4445683200.0, 4445850080.0],
          dtype='float64', name='VV_quality_calibration_ground_range', length=668))

  • VV_quality_azimuth_swath
    PandasIndex

    PandasIndex(Index(['IW1', 'IW2', 'IW3'], dtype='object', name='VV_quality_azimuth_swath'))

  • VV_quality_range_azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:55.762495',
                   '2026-03-16 16:47:56.763156', '2026-03-16 16:47:57.763817',
                   '2026-03-16 16:47:58.764478', '2026-03-16 16:47:59.765139',
                   '2026-03-16 16:48:00.765800', '2026-03-16 16:48:01.766461',
                   '2026-03-16 16:48:02.767122', '2026-03-16 16:48:03.767783',
                   '2026-03-16 16:48:04.768444', '2026-03-16 16:48:05.769104',
                   '2026-03-16 16:48:06.769765', '2026-03-16 16:48:07.770426',
                   '2026-03-16 16:48:08.771087', '2026-03-16 16:48:09.771748',
                   '2026-03-16 16:48:10.772409', '2026-03-16 16:48:11.773070',
                   '2026-03-16 16:48:12.773731', '2026-03-16 16:48:13.774392',
                   '2026-03-16 16:48:14.775053', '2026-03-16 16:48:15.775714',
                   '2026-03-16 16:48:16.776375', '2026-03-16 16:48:17.777036',
                   '2026-03-16 16:48:18.777697', '2026-03-16 16:48:19.760382'],
                  dtype='datetime64[ns]', name='VV_quality_range_azimuth_time', freq=None))

• Attributes: (2)

  other_metadata :

  {'eopf_category': 'eocontainer', 'title': 'S01SIWGRD'}

  stac_discovery :

  {'assets': {'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH': {'href': 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH'}, 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV': {'href': 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV'}}, 'bbox': [18.688345, 39.213028, 15.261218, 41.126934], 'geometry': {'coordinates': [[[15.261218, 40.715069], [18.376741, 41.126934], [18.688345, 39.626194], [15.640784, 39.213028], [15.261218, 40.715069]]], 'type': 'Polygon'}, 'id': 'SAR Standard L1 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'constellation': 'sentinel-1', 'created': '2026-03-16T17:57:14.000000Z', 'datetime': '2026-03-16T16:48:07.261108Z', 'end_datetime': '2026-03-16T16:48:19.760381Z', 'eopf:datatake_id': '56230', 'eopf:instrument_mode': 'IW', 'instruments': ['sar'], 'platform': 'sentinel-1c', 'processing:lineage': 'systematic', 'processing:software': {'Sentinel-1 IPF': '004.03'}, 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT-3h', 'product:type': 'S01SIWGRD', 'provider': [{'name': 'S1C-PS', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sar:frequency_band': 'C', 'sar:instrument_mode': 'IW', 'sar:polarizations': ['VV', 'VH'], 'sar:product_type': 'GRD', 'sat:absolute_orbit': 6792, 'sat:anx_datetime': '2026-03-16T16:37:16.668595', 'sat:orbit_state': 'ascending', 'sat:platform_international_designator': '2024-235A', 'sat:relative_orbit': 146, 'start_datetime': '2026-03-16T16:47:54.761834Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/sar/v1.2.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

The separator character used in flattened variable names can be customized via the group_sep parameter. Additionally, you can filter the returned variables using the variables keyword argument, which accepts a string, an iterable of names, or a regular expression (regex) pattern.

In this example, we use a regex to select all arrays associated with the VH band.

In [9]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    op_mode="native",
    chunks={},
    group_sep="/",
    variables="^VH.*",
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", op_mode="native", chunks={}, group_sep="/", variables="^VH.*", ) ds

Out[9]:

<xarray.Dataset> Size: 890MB
Dimensions:                                                           (
                                                                       VH/conditions/antenna_pattern/azimuth_time: 27,
                                                                       VH/conditions/antenna_pattern/count: 730,
                                                                       VH/conditions/attitude/azimuth_time: 25,
                                                                       VH/conditions/doppler_centroid/azimuth_time: 27,
                                                                       VH/conditions/doppler_centroid/degree: 3,
                                                                       ...
                                                                       VH/quality/calibration/azimuth_time: 27,
                                                                       VH/quality/calibration/ground_range: 668,
                                                                       VH/quality/azimuth/swath: 3,
                                                                       VH/quality/azimuth/azimuth_time: 1688,
                                                                       VH/quality/range/azimuth_time: 26,
                                                                       VH/quality/range/ground_range: 671)
Coordinates: (12/16)
  * VH/conditions/antenna_pattern/azimuth_time                        (VH/conditions/antenna_pattern/azimuth_time) datetime64[ns] 216B ...
  * VH/conditions/antenna_pattern/count                               (VH/conditions/antenna_pattern/count) int64 6kB ...
  * VH/conditions/attitude/azimuth_time                               (VH/conditions/attitude/azimuth_time) datetime64[ns] 200B ...
  * VH/conditions/doppler_centroid/azimuth_time                       (VH/conditions/doppler_centroid/azimuth_time) datetime64[ns] 216B ...
  * VH/conditions/doppler_centroid/degree                             (VH/conditions/doppler_centroid/degree) int64 24B ...
  * VH/conditions/gcp/azimuth_time                                    (VH/conditions/gcp/azimuth_time) datetime64[ns] 80B ...
    ...                                                                ...
  * VH/measurements/ground_range                                      (VH/measurements/ground_range) float64 213kB ...
  * VH/quality/calibration/azimuth_time                               (VH/quality/calibration/azimuth_time) datetime64[ns] 216B ...
  * VH/quality/calibration/ground_range                               (VH/quality/calibration/ground_range) float64 5kB ...
  * VH/quality/azimuth/swath                                          (VH/quality/azimuth/swath) <U3 36B ...
    VH/quality/range/ground_range                                     (VH/quality/range/azimuth_time, VH/quality/range/ground_range) float64 140kB dask.array<chunksize=(26, 671), meta=np.ndarray>
  * VH/quality/range/azimuth_time                                     (VH/quality/range/azimuth_time) datetime64[ns] 208B ...
Dimensions without coordinates: VH/quality/azimuth/azimuth_time
Data variables: (12/39)
    VH/conditions/antenna_pattern/elevation_angle                     (VH/conditions/antenna_pattern/azimuth_time, VH/conditions/antenna_pattern/count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
    VH/conditions/antenna_pattern/incidence_angle                     (VH/conditions/antenna_pattern/azimuth_time, VH/conditions/antenna_pattern/count) float32 79kB dask.array<chunksize=(27, 730), meta=np.ndarray>
    VH/conditions/antenna_pattern/roll                                (VH/conditions/antenna_pattern/azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
    VH/conditions/antenna_pattern/slant_range_time_ap                 (VH/conditions/antenna_pattern/azimuth_time, VH/conditions/antenna_pattern/count) float64 158kB dask.array<chunksize=(27, 730), meta=np.ndarray>
    VH/conditions/antenna_pattern/swath                               (VH/conditions/antenna_pattern/azimuth_time) <U3 324B dask.array<chunksize=(27,), meta=np.ndarray>
    VH/conditions/antenna_pattern/terrain_height                      (VH/conditions/antenna_pattern/azimuth_time) float64 216B dask.array<chunksize=(27,), meta=np.ndarray>
    ...                                                                ...
    VH/quality/calibration/beta_nought                                (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VH/quality/calibration/dn                                         (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VH/quality/calibration/gamma                                      (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VH/quality/calibration/sigma_nought                               (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range) float32 72kB dask.array<chunksize=(27, 668), meta=np.ndarray>
    VH/quality/azimuth/noise_azimuth_lut                              (VH/quality/azimuth/swath, VH/quality/azimuth/azimuth_time) float32 20kB dask.array<chunksize=(3, 1688), meta=np.ndarray>
    VH/quality/range/noise_range_lut                                  (VH/quality/range/azimuth_time, VH/quality/range/ground_range) float32 70kB dask.array<chunksize=(26, 671), meta=np.ndarray>
Attributes: (2)

xarray.Dataset

• Dimensions:
  • VH/conditions/antenna_pattern/azimuth_time: 27
  • VH/conditions/antenna_pattern/count: 730
  • VH/conditions/attitude/azimuth_time: 25
  • VH/conditions/doppler_centroid/azimuth_time: 27
  • VH/conditions/doppler_centroid/degree: 3
  • VH/conditions/gcp/azimuth_time: 10
  • VH/conditions/gcp/ground_range: 21
  • VH/conditions/orbit/azimuth_time: 17
  • VH/conditions/orbit/axis: 3
  • VH/measurements/azimuth_time: 16689
  • VH/measurements/ground_range: 26642
  • VH/quality/calibration/azimuth_time: 27
  • VH/quality/calibration/ground_range: 668
  • VH/quality/azimuth/swath: 3
  • VH/quality/azimuth/azimuth_time: 1688
  • VH/quality/range/azimuth_time: 26
  • VH/quality/range/ground_range: 671
• Coordinates: (16)
  • VH/conditions/antenna_pattern/azimuth_time
    (VH/conditions/antenna_pattern/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:55.425896 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time of antenna pattern measurement

    array(['2026-03-16T16:47:55.425896000', '2026-03-16T16:47:56.276897000',
           '2026-03-16T16:47:57.222452000', '2026-03-16T16:47:58.182397000',
           '2026-03-16T16:47:59.031342000', '2026-03-16T16:47:59.983064000',
           '2026-03-16T16:48:00.938898000', '2026-03-16T16:48:01.791954000',
           '2026-03-16T16:48:02.741621000', '2026-03-16T16:48:03.695399000',
           '2026-03-16T16:48:04.548455000', '2026-03-16T16:48:05.500177000',
           '2026-03-16T16:48:06.453956000', '2026-03-16T16:48:07.307012000',
           '2026-03-16T16:48:08.256678000', '2026-03-16T16:48:09.216623000',
           '2026-03-16T16:48:10.063513000', '2026-03-16T16:48:11.011124000',
           '2026-03-16T16:48:11.966958000', '2026-03-16T16:48:12.822069000',
           '2026-03-16T16:48:13.765569000', '2026-03-16T16:48:14.725514000',
           '2026-03-16T16:48:15.578570000', '2026-03-16T16:48:16.526181000',
           '2026-03-16T16:48:17.488182000', '2026-03-16T16:48:18.337127000',
           '2026-03-16T16:48:19.280627000'], dtype='datetime64[ns]')

  • VH/conditions/antenna_pattern/count
    (VH/conditions/antenna_pattern/count)
    int64
    0 1 2 3 4 5 ... 725 726 727 728 729

    array([  0,   1,   2, ..., 727, 728, 729], shape=(730,))

  • VH/conditions/attitude/azimuth_time
    (VH/conditions/attitude/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:55.000002 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    time stamp at which orbit state vectors apply

    array(['2026-03-16T16:47:55.000002000', '2026-03-16T16:47:55.999998000',
           '2026-03-16T16:47:57.000002000', '2026-03-16T16:47:57.999996000',
           '2026-03-16T16:47:59.000000000', '2026-03-16T16:47:59.999995000',
           '2026-03-16T16:48:01.000000000', '2026-03-16T16:48:01.999995000',
           '2026-03-16T16:48:03.000000000', '2026-03-16T16:48:04.000003000',
           '2026-03-16T16:48:04.999998000', '2026-03-16T16:48:06.000003000',
           '2026-03-16T16:48:06.999997000', '2026-03-16T16:48:08.000002000',
           '2026-03-16T16:48:08.999996000', '2026-03-16T16:48:10.000001000',
           '2026-03-16T16:48:10.999996000', '2026-03-16T16:48:12.000000000',
           '2026-03-16T16:48:12.999995000', '2026-03-16T16:48:14.000000000',
           '2026-03-16T16:48:14.999994000', '2026-03-16T16:48:16.000000000',
           '2026-03-16T16:48:17.000004000', '2026-03-16T16:48:17.999998000',
           '2026-03-16T16:48:19.000004000'], dtype='datetime64[ns]')

  • VH/conditions/doppler_centroid/azimuth_time
    (VH/conditions/doppler_centroid/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:56.021426 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time of terrain height measurement

    array(['2026-03-16T16:47:56.021426000', '2026-03-16T16:47:56.022979000',
           '2026-03-16T16:47:56.035054000', '2026-03-16T16:47:58.779703000',
           '2026-03-16T16:47:58.781256000', '2026-03-16T16:47:58.793331000',
           '2026-03-16T16:48:01.537980000', '2026-03-16T16:48:01.539533000',
           '2026-03-16T16:48:01.551608000', '2026-03-16T16:48:04.296257000',
           '2026-03-16T16:48:04.297810000', '2026-03-16T16:48:04.309885000',
           '2026-03-16T16:48:07.054533000', '2026-03-16T16:48:07.056086000',
           '2026-03-16T16:48:07.068161000', '2026-03-16T16:48:09.812810000',
           '2026-03-16T16:48:09.814363000', '2026-03-16T16:48:09.826438000',
           '2026-03-16T16:48:12.571087000', '2026-03-16T16:48:12.572640000',
           '2026-03-16T16:48:12.584715000', '2026-03-16T16:48:15.329364000',
           '2026-03-16T16:48:15.330917000', '2026-03-16T16:48:15.342992000',
           '2026-03-16T16:48:18.087641000', '2026-03-16T16:48:18.089194000',
           '2026-03-16T16:48:18.101269000'], dtype='datetime64[ns]')

  • VH/conditions/doppler_centroid/degree
    (VH/conditions/doppler_centroid/degree)
    int64
    2 1 0

    array([2, 1, 0])

  • VH/conditions/gcp/azimuth_time
    (VH/conditions/gcp/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:57.762318000',
           '2026-03-16T16:48:00.762803000', '2026-03-16T16:48:03.763288000',
           '2026-03-16T16:48:06.763773000', '2026-03-16T16:48:09.764257000',
           '2026-03-16T16:48:12.764742000', '2026-03-16T16:48:15.765227000',
           '2026-03-16T16:48:18.765712000', '2026-03-16T16:48:19.760381000'],
          dtype='datetime64[ns]')

  • VH/conditions/gcp/ground_range
    (VH/conditions/gcp/ground_range)
    float64
    0.0 1.333e+04 ... 2.664e+05

    array([     0.,  13330.,  26660.,  39990.,  53320.,  66650.,  79980.,  93310.,
           106640., 119970., 133300., 146630., 159960., 173290., 186620., 199950.,
           213280., 226610., 239940., 253270., 266410.])

  • VH/conditions/orbit/axis
    (VH/conditions/orbit/axis)
    <U1
    'x' 'y' 'z'

    array(['x', 'y', 'z'], dtype='<U1')

  • VH/conditions/orbit/azimuth_time
    (VH/conditions/orbit/azimuth_time)
    datetime64[ns]
    2026-03-16T16:46:46.681692 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    time stamp at which orbit state vectors apply

    array(['2026-03-16T16:46:46.681692000', '2026-03-16T16:46:56.681692000',
           '2026-03-16T16:47:06.681692000', '2026-03-16T16:47:16.681692000',
           '2026-03-16T16:47:26.681692000', '2026-03-16T16:47:36.681693000',
           '2026-03-16T16:47:46.681693000', '2026-03-16T16:47:56.681692000',
           '2026-03-16T16:48:06.681692000', '2026-03-16T16:48:16.681692000',
           '2026-03-16T16:48:26.681692000', '2026-03-16T16:48:36.681692000',
           '2026-03-16T16:48:46.681692000', '2026-03-16T16:48:56.681693000',
           '2026-03-16T16:49:06.681693000', '2026-03-16T16:49:16.681692000',
           '2026-03-16T16:49:26.681692000'], dtype='datetime64[ns]')

  • VH/measurements/azimuth_time
    (VH/measurements/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:54.763331995',
           '2026-03-16T16:47:54.764829990', ..., '2026-03-16T16:48:19.757385009',
           '2026-03-16T16:48:19.758883004', '2026-03-16T16:48:19.760381000'],
          shape=(16689,), dtype='datetime64[ns]')

  • VH/measurements/ground_range
    (VH/measurements/ground_range)
    float64
    0.0 10.0 ... 2.664e+05 2.664e+05

    array([0.0000e+00, 1.0000e+01, 2.0000e+01, ..., 2.6639e+05, 2.6640e+05,
           2.6641e+05], shape=(26642,))

  • VH/quality/calibration/azimuth_time
    (VH/quality/calibration/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time at which calibration vector applies

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.761834000',
           '2026-03-16T16:47:56.761834000', '2026-03-16T16:47:57.761834000',
           '2026-03-16T16:47:58.761834000', '2026-03-16T16:47:59.761834000',
           '2026-03-16T16:48:00.761834000', '2026-03-16T16:48:01.761834000',
           '2026-03-16T16:48:02.761834000', '2026-03-16T16:48:03.761834000',
           '2026-03-16T16:48:04.761834000', '2026-03-16T16:48:05.761834000',
           '2026-03-16T16:48:06.761834000', '2026-03-16T16:48:07.761834000',
           '2026-03-16T16:48:08.761834000', '2026-03-16T16:48:09.761834000',
           '2026-03-16T16:48:10.761834000', '2026-03-16T16:48:11.761834000',
           '2026-03-16T16:48:12.761834000', '2026-03-16T16:48:13.761834000',
           '2026-03-16T16:48:14.761834000', '2026-03-16T16:48:15.761834000',
           '2026-03-16T16:48:16.761834000', '2026-03-16T16:48:17.761834000',
           '2026-03-16T16:48:18.761834000', '2026-03-16T16:48:19.761834000',
           '2026-03-16T16:48:20.761834000'], dtype='datetime64[ns]')

  • VH/quality/calibration/ground_range
    (VH/quality/calibration/ground_range)
    float64
    0.0 6.675e+06 ... 4.446e+09

    dimensions :

    ['ground_range']

    dtype :

    <f8

    array([0.000000e+00, 6.675200e+06, 1.335040e+07, ..., 4.439008e+09,
           4.445683e+09, 4.445850e+09], shape=(668,))

  • VH/quality/azimuth/swath
    (VH/quality/azimuth/swath)
    <U3
    'IW1' 'IW2' 'IW3'

    dimensions :

    ['swath']

    dtype :

    U3

    long_name :

    Swath

    array(['IW1', 'IW2', 'IW3'], dtype='<U3')

  • VH/quality/range/ground_range
    (VH/quality/range/azimuth_time, VH/quality/range/ground_range)
    float64
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    Array Chunk Bytes 136.30 kiB 136.30 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/quality/range/azimuth_time
    (VH/quality/range/azimuth_time)
    datetime64[ns]
    2026-03-16T16:47:54.761834 ... 2...

    dimensions :

    ['azimuth_time']

    dtype :

    <M8[ns]

    long_name :

    zero doppler azimuth time at which noise vector applies

    array(['2026-03-16T16:47:54.761834000', '2026-03-16T16:47:55.762495000',
           '2026-03-16T16:47:56.763156000', '2026-03-16T16:47:57.763817000',
           '2026-03-16T16:47:58.764478000', '2026-03-16T16:47:59.765139000',
           '2026-03-16T16:48:00.765800000', '2026-03-16T16:48:01.766461000',
           '2026-03-16T16:48:02.767122000', '2026-03-16T16:48:03.767783000',
           '2026-03-16T16:48:04.768444000', '2026-03-16T16:48:05.769104000',
           '2026-03-16T16:48:06.769765000', '2026-03-16T16:48:07.770426000',
           '2026-03-16T16:48:08.771087000', '2026-03-16T16:48:09.771748000',
           '2026-03-16T16:48:10.772409000', '2026-03-16T16:48:11.773070000',
           '2026-03-16T16:48:12.773731000', '2026-03-16T16:48:13.774392000',
           '2026-03-16T16:48:14.775053000', '2026-03-16T16:48:15.775714000',
           '2026-03-16T16:48:16.776375000', '2026-03-16T16:48:17.777036000',
           '2026-03-16T16:48:18.777697000', '2026-03-16T16:48:19.760382000'],
          dtype='datetime64[ns]')

• Data variables: (39)
  • VH/conditions/antenna_pattern/elevation_angle
    (VH/conditions/antenna_pattern/azimuth_time, VH/conditions/antenna_pattern/count)
    float32
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'elevation angle to grid point', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    elevation angle to grid point

    units :

    degrees

    Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/antenna_pattern/incidence_angle
    (VH/conditions/antenna_pattern/azimuth_time, VH/conditions/antenna_pattern/count)
    float32
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f4', 'long_name': 'incidence angle to grid point', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    incidence angle to grid point

    units :

    degrees

    Array Chunk Bytes 76.99 kiB 76.99 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/antenna_pattern/roll
    (VH/conditions/antenna_pattern/azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'estimated roll angle for this antenna pattern', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    estimated roll angle for this antenna pattern

    units :

    degrees

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/antenna_pattern/slant_range_time_ap
    (VH/conditions/antenna_pattern/azimuth_time, VH/conditions/antenna_pattern/count)
    float64
    dask.array<chunksize=(27, 730), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'count'], 'dimensions': ['azimuth_time', 'count'], 'dtype': '<f8', 'long_name': 'two-way slant range time to sample', 'units': 's'}

    dtype :

    <f8

    long_name :

    two-way slant range time to sample

    units :

    s

    Array Chunk Bytes 153.98 kiB 153.98 kiB Shape (27, 730) (27, 730) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/antenna_pattern/swath
    (VH/conditions/antenna_pattern/azimuth_time)
    <U3
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': 'U3', 'long_name': 'Swath'}

    dtype :

    U3

    long_name :

    Swath

    Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type

  • VH/conditions/antenna_pattern/terrain_height
    (VH/conditions/antenna_pattern/azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time', 'units': 'm'}

    dtype :

    <f8

    long_name :

    mean terrain height in range for this antenna pattern (in metres above the ellipsoid) for the given zero-doppler azimuth time

    units :

    m

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/attitude/pitch
    (VH/conditions/attitude/azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform pitch angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform pitch angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/attitude/q0
    (VH/conditions/attitude/azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q0 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q0 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/attitude/q1
    (VH/conditions/attitude/azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q1 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q1 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/attitude/q2
    (VH/conditions/attitude/azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q2 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q2 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/attitude/q3
    (VH/conditions/attitude/azimuth_time)
    float32
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f4', 'long_name': 'q3 attitude quaternion as extracted from ancillary attitude data'}

    dtype :

    <f4

    long_name :

    q3 attitude quaternion as extracted from ancillary attitude data

    Array Chunk Bytes 100 B 100 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/attitude/roll
    (VH/conditions/attitude/azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform roll angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform roll angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/attitude/wx
    (VH/conditions/attitude/azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'x component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    x component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/attitude/wy
    (VH/conditions/attitude/azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'y component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    y component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/attitude/wz
    (VH/conditions/attitude/azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'z component of angular velocity vector as extracted from ancillary attitude data', 'units': 'degrees/s'}

    dtype :

    <f8

    long_name :

    z component of angular velocity vector as extracted from ancillary attitude data

    units :

    degrees/s

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/attitude/yaw
    (VH/conditions/attitude/azimuth_time)
    float64
    dask.array<chunksize=(25,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'platform yaw angle calculated from ancillary attitude data', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    platform yaw angle calculated from ancillary attitude data

    units :

    degrees

    Array Chunk Bytes 200 B 200 B Shape (25,) (25,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/doppler_centroid/data_dc_polynomial
    (VH/conditions/doppler_centroid/azimuth_time, VH/conditions/doppler_centroid/degree)
    float32
    dask.array<chunksize=(27, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

    dtype :

    <f4

    long_name :

    doppler centroid estimated from data, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

    Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/doppler_centroid/data_dc_rms_error
    (VH/conditions/doppler_centroid/azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)'}

    dtype :

    <f8

    long_name :

    RMS error of the doppler centroid estimate (calculated as the average of the individual RMS residual errors between input fine doppler centroid estimates and the fitted polynomial; if the doppler centroid was not estimated from data, this is set to 0)

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/doppler_centroid/data_dc_rms_error_above_threshold
    (VH/conditions/doppler_centroid/azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VH/conditions/doppler_centroid/fine_dce_azimuth_start_time
    (VH/conditions/doppler_centroid/azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VH/conditions/doppler_centroid/fine_dce_azimuth_stop_time
    (VH/conditions/doppler_centroid/azimuth_time)
    bool
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<b1', 'long_name': 'false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold'}

    dtype :

    <b1

    long_name :

    false if the RMS error is less than the acceptable threshold for the doppler centroid estimated from the data; true if the RMS error is more than or equal to the acceptable threshold

    Array Chunk Bytes 27 B 27 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • VH/conditions/doppler_centroid/geometry_dc_polynomial
    (VH/conditions/doppler_centroid/azimuth_time, VH/conditions/doppler_centroid/degree)
    float32
    dask.array<chunksize=(27, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'degree'], 'dimensions': ['azimuth_time', 'degree'], 'dtype': '<f4', 'long_name': 'doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time'}

    dtype :

    <f4

    long_name :

    doppler centroid estimated from orbit, expressed as polynomial with 5 coefficients: doppler centroid = d0 + d1(tsr-t0) + d2(tsr-t0)^2 + d3(tsr-t0)^3 + d4(tsr-t0)^4, where tsr = 2-way slant range time

    Array Chunk Bytes 324 B 324 B Shape (27, 3) (27, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/conditions/doppler_centroid/t0
    (VH/conditions/doppler_centroid/azimuth_time)
    float64
    dask.array<chunksize=(27,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time'], 'dimensions': ['azimuth_time'], 'dtype': '<f8', 'long_name': 'two-way slant range time origin for doppler centroid estimate', 'units': 's'}

    dtype :

    <f8

    long_name :

    two-way slant range time origin for doppler centroid estimate

    units :

    s

    Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/gcp/azimuth_time_gcp
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    datetime64[ns]
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • VH/conditions/gcp/elevation_angle
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/gcp/height
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/gcp/incidence_angle
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/gcp/latitude
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/gcp/longitude
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/gcp/slant_range_time_gcp
    (VH/conditions/gcp/azimuth_time, VH/conditions/gcp/ground_range)
    float64
    dask.array<chunksize=(10, 21), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'ground_range', 'line', 'pixel'], 'dimensions': ['azimuth_time', 'ground_range']}

    Array Chunk Bytes 1.64 kiB 1.64 kiB Shape (10, 21) (10, 21) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/orbit/position
    (VH/conditions/orbit/azimuth_time, VH/conditions/orbit/axis)
    float64
    dask.array<chunksize=(17, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'position vector', 'units': 'm'}

    dtype :

    <f8

    long_name :

    position vector

    units :

    m

    Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/conditions/orbit/velocity
    (VH/conditions/orbit/azimuth_time, VH/conditions/orbit/axis)
    float64
    dask.array<chunksize=(17, 3), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'axis'], 'dimensions': ['azimuth_time', 'axis'], 'dtype': '<f8', 'long_name': 'velocity vector', 'units': 'm/s'}

    dtype :

    <f8

    long_name :

    velocity vector

    units :

    m/s

    Array Chunk Bytes 408 B 408 B Shape (17, 3) (17, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • VH/measurements/grd
    (VH/measurements/azimuth_time, VH/measurements/ground_range)
    uint16
    dask.array<chunksize=(2048, 4096), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<u2', 'long_name': 'measurement data set for GRD IW'}

    dtype :

    <u2

    long_name :

    measurement data set for GRD IW

    Array Chunk Bytes 848.06 MiB 16.00 MiB Shape (16689, 26642) (2048, 4096) Dask graph 63 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • VH/quality/calibration/beta_nought
    (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    beta nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/quality/calibration/dn
    (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    digital number calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/quality/calibration/gamma
    (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    gamma calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/quality/calibration/sigma_nought
    (VH/quality/calibration/azimuth_time, VH/quality/calibration/ground_range)
    float32
    dask.array<chunksize=(27, 668), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)'}

    dtype :

    <f4

    long_name :

    sigma nought calibration vector (this array contains the count attribute number of floating point values; the values in this vector are aligned with the pixel vector)

    Array Chunk Bytes 70.45 kiB 70.45 kiB Shape (27, 668) (27, 668) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/quality/azimuth/noise_azimuth_lut
    (VH/quality/azimuth/swath, VH/quality/azimuth/azimuth_time)
    float32
    dask.array<chunksize=(3, 1688), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['line', 'swath', 'first_azimuth_time', 'last_azimuth_time', 'first_range_sample', 'last_range_sample'], 'dimensions': ['swath', 'azimuth_time'], 'dtype': '<f4', 'long_name': 'azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

    dtype :

    <f4

    long_name :

    azimuth thermal noise correction vector power values (this array contains the count attribute number of floating point values)

    Array Chunk Bytes 19.78 kiB 19.78 kiB Shape (3, 1688) (3, 1688) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VH/quality/range/noise_range_lut
    (VH/quality/range/azimuth_time, VH/quality/range/ground_range)
    float32
    dask.array<chunksize=(26, 671), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['azimuth_time', 'line', 'pixel', 'ground_range'], 'dimensions': ['azimuth_time', 'ground_range'], 'dtype': '<f4', 'long_name': 'range thermal noise correction vector power values (this array contains the count attribute number of floating point values)'}

    dtype :

    <f4

    long_name :

    range thermal noise correction vector power values (this array contains the count attribute number of floating point values)

    Array Chunk Bytes 68.15 kiB 68.15 kiB Shape (26, 671) (26, 671) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (15)
  • VH/conditions/antenna_pattern/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:55.425896', '2026-03-16 16:47:56.276897',
                   '2026-03-16 16:47:57.222452', '2026-03-16 16:47:58.182397',
                   '2026-03-16 16:47:59.031342', '2026-03-16 16:47:59.983064',
                   '2026-03-16 16:48:00.938898', '2026-03-16 16:48:01.791954',
                   '2026-03-16 16:48:02.741621', '2026-03-16 16:48:03.695399',
                   '2026-03-16 16:48:04.548455', '2026-03-16 16:48:05.500177',
                   '2026-03-16 16:48:06.453956', '2026-03-16 16:48:07.307012',
                   '2026-03-16 16:48:08.256678', '2026-03-16 16:48:09.216623',
                   '2026-03-16 16:48:10.063513', '2026-03-16 16:48:11.011124',
                   '2026-03-16 16:48:11.966958', '2026-03-16 16:48:12.822069',
                   '2026-03-16 16:48:13.765569', '2026-03-16 16:48:14.725514',
                   '2026-03-16 16:48:15.578570', '2026-03-16 16:48:16.526181',
                   '2026-03-16 16:48:17.488182', '2026-03-16 16:48:18.337127',
                   '2026-03-16 16:48:19.280627'],
                  dtype='datetime64[ns]', name='VH/conditions/antenna_pattern/azimuth_time', freq=None))

  • VH/conditions/antenna_pattern/count
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           720, 721, 722, 723, 724, 725, 726, 727, 728, 729],
          dtype='int64', name='VH/conditions/antenna_pattern/count', length=730))

  • VH/conditions/attitude/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:55.000002', '2026-03-16 16:47:55.999998',
                   '2026-03-16 16:47:57.000002', '2026-03-16 16:47:57.999996',
                          '2026-03-16 16:47:59', '2026-03-16 16:47:59.999995',
                          '2026-03-16 16:48:01', '2026-03-16 16:48:01.999995',
                          '2026-03-16 16:48:03', '2026-03-16 16:48:04.000003',
                   '2026-03-16 16:48:04.999998', '2026-03-16 16:48:06.000003',
                   '2026-03-16 16:48:06.999997', '2026-03-16 16:48:08.000002',
                   '2026-03-16 16:48:08.999996', '2026-03-16 16:48:10.000001',
                   '2026-03-16 16:48:10.999996',        '2026-03-16 16:48:12',
                   '2026-03-16 16:48:12.999995',        '2026-03-16 16:48:14',
                   '2026-03-16 16:48:14.999994',        '2026-03-16 16:48:16',
                   '2026-03-16 16:48:17.000004', '2026-03-16 16:48:17.999998',
                   '2026-03-16 16:48:19.000004'],
                  dtype='datetime64[ns]', name='VH/conditions/attitude/azimuth_time', freq=None))

  • VH/conditions/doppler_centroid/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:56.021426', '2026-03-16 16:47:56.022979',
                   '2026-03-16 16:47:56.035054', '2026-03-16 16:47:58.779703',
                   '2026-03-16 16:47:58.781256', '2026-03-16 16:47:58.793331',
                   '2026-03-16 16:48:01.537980', '2026-03-16 16:48:01.539533',
                   '2026-03-16 16:48:01.551608', '2026-03-16 16:48:04.296257',
                   '2026-03-16 16:48:04.297810', '2026-03-16 16:48:04.309885',
                   '2026-03-16 16:48:07.054533', '2026-03-16 16:48:07.056086',
                   '2026-03-16 16:48:07.068161', '2026-03-16 16:48:09.812810',
                   '2026-03-16 16:48:09.814363', '2026-03-16 16:48:09.826438',
                   '2026-03-16 16:48:12.571087', '2026-03-16 16:48:12.572640',
                   '2026-03-16 16:48:12.584715', '2026-03-16 16:48:15.329364',
                   '2026-03-16 16:48:15.330917', '2026-03-16 16:48:15.342992',
                   '2026-03-16 16:48:18.087641', '2026-03-16 16:48:18.089194',
                   '2026-03-16 16:48:18.101269'],
                  dtype='datetime64[ns]', name='VH/conditions/doppler_centroid/azimuth_time', freq=None))

  • VH/conditions/doppler_centroid/degree
    PandasIndex

    PandasIndex(Index([2, 1, 0], dtype='int64', name='VH/conditions/doppler_centroid/degree'))

  • VH/conditions/gcp/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:57.762318',
                   '2026-03-16 16:48:00.762803', '2026-03-16 16:48:03.763288',
                   '2026-03-16 16:48:06.763773', '2026-03-16 16:48:09.764257',
                   '2026-03-16 16:48:12.764742', '2026-03-16 16:48:15.765227',
                   '2026-03-16 16:48:18.765712', '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='VH/conditions/gcp/azimuth_time', freq=None))

  • VH/conditions/gcp/ground_range
    PandasIndex

    PandasIndex(Index([     0.0,  13330.0,  26660.0,  39990.0,  53320.0,  66650.0,  79980.0,
            93310.0, 106640.0, 119970.0, 133300.0, 146630.0, 159960.0, 173290.0,
           186620.0, 199950.0, 213280.0, 226610.0, 239940.0, 253270.0, 266410.0],
          dtype='float64', name='VH/conditions/gcp/ground_range'))

  • VH/conditions/orbit/axis
    PandasIndex

    PandasIndex(Index(['x', 'y', 'z'], dtype='object', name='VH/conditions/orbit/axis'))

  • VH/conditions/orbit/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:46:46.681692', '2026-03-16 16:46:56.681692',
                   '2026-03-16 16:47:06.681692', '2026-03-16 16:47:16.681692',
                   '2026-03-16 16:47:26.681692', '2026-03-16 16:47:36.681693',
                   '2026-03-16 16:47:46.681693', '2026-03-16 16:47:56.681692',
                   '2026-03-16 16:48:06.681692', '2026-03-16 16:48:16.681692',
                   '2026-03-16 16:48:26.681692', '2026-03-16 16:48:36.681692',
                   '2026-03-16 16:48:46.681692', '2026-03-16 16:48:56.681693',
                   '2026-03-16 16:49:06.681693', '2026-03-16 16:49:16.681692',
                   '2026-03-16 16:49:26.681692'],
                  dtype='datetime64[ns]', name='VH/conditions/orbit/azimuth_time', freq=None))

  • VH/measurements/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex([   '2026-03-16 16:47:54.761834',
                   '2026-03-16 16:47:54.763331995',
                   '2026-03-16 16:47:54.764829990',
                   '2026-03-16 16:47:54.766327986',
                   '2026-03-16 16:47:54.767825981',
                   '2026-03-16 16:47:54.769323976',
                   '2026-03-16 16:47:54.770821972',
                   '2026-03-16 16:47:54.772319967',
                   '2026-03-16 16:47:54.773817963',
                   '2026-03-16 16:47:54.775315958',
                   ...
                   '2026-03-16 16:48:19.746899041',
                   '2026-03-16 16:48:19.748397036',
                   '2026-03-16 16:48:19.749895032',
                   '2026-03-16 16:48:19.751393027',
                   '2026-03-16 16:48:19.752891023',
                   '2026-03-16 16:48:19.754389018',
                   '2026-03-16 16:48:19.755887013',
                   '2026-03-16 16:48:19.757385009',
                   '2026-03-16 16:48:19.758883004',
                      '2026-03-16 16:48:19.760381'],
                  dtype='datetime64[ns]', name='VH/measurements/azimuth_time', length=16689, freq=None))

  • VH/measurements/ground_range
    PandasIndex

    PandasIndex(Index([     0.0,     10.0,     20.0,     30.0,     40.0,     50.0,     60.0,
               70.0,     80.0,     90.0,
           ...
           266320.0, 266330.0, 266340.0, 266350.0, 266360.0, 266370.0, 266380.0,
           266390.0, 266400.0, 266410.0],
          dtype='float64', name='VH/measurements/ground_range', length=26642))

  • VH/quality/calibration/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:55.761834',
                   '2026-03-16 16:47:56.761834', '2026-03-16 16:47:57.761834',
                   '2026-03-16 16:47:58.761834', '2026-03-16 16:47:59.761834',
                   '2026-03-16 16:48:00.761834', '2026-03-16 16:48:01.761834',
                   '2026-03-16 16:48:02.761834', '2026-03-16 16:48:03.761834',
                   '2026-03-16 16:48:04.761834', '2026-03-16 16:48:05.761834',
                   '2026-03-16 16:48:06.761834', '2026-03-16 16:48:07.761834',
                   '2026-03-16 16:48:08.761834', '2026-03-16 16:48:09.761834',
                   '2026-03-16 16:48:10.761834', '2026-03-16 16:48:11.761834',
                   '2026-03-16 16:48:12.761834', '2026-03-16 16:48:13.761834',
                   '2026-03-16 16:48:14.761834', '2026-03-16 16:48:15.761834',
                   '2026-03-16 16:48:16.761834', '2026-03-16 16:48:17.761834',
                   '2026-03-16 16:48:18.761834', '2026-03-16 16:48:19.761834',
                   '2026-03-16 16:48:20.761834'],
                  dtype='datetime64[ns]', name='VH/quality/calibration/azimuth_time', freq=None))

  • VH/quality/calibration/ground_range
    PandasIndex

    PandasIndex(Index([         0.0,    6675200.0,   13350400.0,   20025600.0,   26700800.0,
             33376000.0,   40051200.0,   46726400.0,   53401600.0,   60076800.0,
           ...
           4392281600.0, 4398956800.0, 4405632000.0, 4412307200.0, 4418982400.0,
           4425657600.0, 4432332800.0, 4439008000.0, 4445683200.0, 4445850080.0],
          dtype='float64', name='VH/quality/calibration/ground_range', length=668))

  • VH/quality/azimuth/swath
    PandasIndex

    PandasIndex(Index(['IW1', 'IW2', 'IW3'], dtype='object', name='VH/quality/azimuth/swath'))

  • VH/quality/range/azimuth_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2026-03-16 16:47:54.761834', '2026-03-16 16:47:55.762495',
                   '2026-03-16 16:47:56.763156', '2026-03-16 16:47:57.763817',
                   '2026-03-16 16:47:58.764478', '2026-03-16 16:47:59.765139',
                   '2026-03-16 16:48:00.765800', '2026-03-16 16:48:01.766461',
                   '2026-03-16 16:48:02.767122', '2026-03-16 16:48:03.767783',
                   '2026-03-16 16:48:04.768444', '2026-03-16 16:48:05.769104',
                   '2026-03-16 16:48:06.769765', '2026-03-16 16:48:07.770426',
                   '2026-03-16 16:48:08.771087', '2026-03-16 16:48:09.771748',
                   '2026-03-16 16:48:10.772409', '2026-03-16 16:48:11.773070',
                   '2026-03-16 16:48:12.773731', '2026-03-16 16:48:13.774392',
                   '2026-03-16 16:48:14.775053', '2026-03-16 16:48:15.775714',
                   '2026-03-16 16:48:16.776375', '2026-03-16 16:48:17.777036',
                   '2026-03-16 16:48:18.777697', '2026-03-16 16:48:19.760382'],
                  dtype='datetime64[ns]', name='VH/quality/range/azimuth_time', freq=None))

• Attributes: (2)

  other_metadata :

  {'eopf_category': 'eocontainer', 'title': 'S01SIWGRD'}

  stac_discovery :

  {'assets': {'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH': {'href': 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VH'}, 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV': {'href': 'S01SIWGRD_20260316T164754_0025_C039_8EA0_00DBA6_VV'}}, 'bbox': [18.688345, 39.213028, 15.261218, 41.126934], 'geometry': {'coordinates': [[[15.261218, 40.715069], [18.376741, 41.126934], [18.688345, 39.626194], [15.640784, 39.213028], [15.261218, 40.715069]]], 'type': 'Polygon'}, 'id': 'SAR Standard L1 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'constellation': 'sentinel-1', 'created': '2026-03-16T17:57:14.000000Z', 'datetime': '2026-03-16T16:48:07.261108Z', 'end_datetime': '2026-03-16T16:48:19.760381Z', 'eopf:datatake_id': '56230', 'eopf:instrument_mode': 'IW', 'instruments': ['sar'], 'platform': 'sentinel-1c', 'processing:lineage': 'systematic', 'processing:software': {'Sentinel-1 IPF': '004.03'}, 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT-3h', 'product:type': 'S01SIWGRD', 'provider': [{'name': 'S1C-PS', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sar:frequency_band': 'C', 'sar:instrument_mode': 'IW', 'sar:polarizations': ['VV', 'VH'], 'sar:product_type': 'GRD', 'sat:absolute_orbit': 6792, 'sat:anx_datetime': '2026-03-16T16:37:16.668595', 'sat:orbit_state': 'ascending', 'sat:platform_international_designator': '2024-235A', 'sat:relative_orbit': 146, 'start_datetime': '2026-03-16T16:47:54.761834Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/sar/v1.2.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

We can now plot the GRD array and the coarse 2d latitude and longitude grids, which can be used for geolocation.

In [10]:

fig, ax = plt.subplots(1, 3, figsize=(15, 4))
ds["VH/measurements/grd"][::10, ::10].plot.imshow(ax=ax[0], vmax=200)
ds["VH/conditions/gcp/longitude"].plot.imshow(ax=ax[1])
ds["VH/conditions/gcp/latitude"].plot.imshow(ax=ax[2])
plt.tight_layout()

fig, ax = plt.subplots(1, 3, figsize=(15, 4)) ds["VH/measurements/grd"][::10, ::10].plot.imshow(ax=ax[0], vmax=200) ds["VH/conditions/gcp/longitude"].plot.imshow(ax=ax[1]) ds["VH/conditions/gcp/latitude"].plot.imshow(ax=ax[2]) plt.tight_layout()

---

Open a Sentinel-1 Level-2 OCN¶

We now access a Sentinel-1 Level-2 OCN product in native mode. The data access methods shown above apply equally to Sentinel-1 Level-2 OCN products.

Find a Sentinel-1 Level-2 OCN Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for available Sentinel-1 Level-2 OCN tiles.

In [11]:

%%time
catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-1-l2-ocn"],
        bbox=[16, 40, 17, 41],
        datetime=["2025-07-30", "2025-07-31"],
    ).items()
)
items

%%time catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-1-l2-ocn"], bbox=[16, 40, 17, 41], datetime=["2025-07-30", "2025-07-31"], ).items() ) items

CPU times: user 14.8 ms, sys: 1.1 ms, total: 15.9 ms
Wall time: 210 ms

Out[11]:

[<Item id=S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E>,
 <Item id=S1C_IW_OCN__2SDV_20250730T050243_20250730T050308_003445_006EB7_7BAE>]

In [12]:

item = items[0]
item

item = items[0] item

Out[12]:

<Item id=S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 6 items

  • 0 "https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"

  • 1 "https://stac-extensions.github.io/sat/v1.0.0/schema.json"

  • 2 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"

  • 3 "https://stac-extensions.github.io/product/v0.1.0/schema.json"

  • 4 "https://stac-extensions.github.io/sar/v1.0.0/schema.json"

  • 5 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"
• id "S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 5 items

      • 0[] 2 items

        • 0 16.616774

        • 1 39.495934

      • 1[] 2 items

        • 0 13.571783

        • 1 39.906666

      • 2[] 2 items

        • 0 13.88713

        • 1 41.406952

      • 3[] 2 items

        • 0 17.000872

        • 1 40.997356

      • 4[] 2 items

        • 0 16.616774

        • 1 39.495934
• bbox[] 4 items

  • 0 13.571783

  • 1 39.495934

  • 2 17.000872

  • 3 41.406952
• properties

  • created "2025-07-30T05:46:47.239656Z"
  • mission "Sentinel-1"
  • updated "2025-07-30T05:46:47.239656Z"
  • datetime "2025-07-30T05:03:08.294096Z"
  • platform "sentinel-1c"
  • providers[] 3 items

    • 0

      • url "https://commission.europa.eu/"
      • name "European Commission"
      • roles[] 1 items

        • 0 "licensor"

    • 1

      • url "https://sentinel.esa.int/web/sentinel/missions/sentinel-1"
      • name "ESA"
      • roles[] 2 items

        • 0 "producer"

        • 1 "processor"

    • 2

      • url "https://zarr.eopf.copernicus.eu/"
      • name "EOPF Sentinel Zarr Samples Service"
      • roles[] 2 items

        • 0 "host"

        • 1 "processor"
  • published "2025-07-30T05:46:47.239656Z"
  • instruments[] 1 items

    • 0 "sar"
  • end_datetime "2025-07-30T05:03:33.293235Z"
  • product:type "S01SIWOCN"
  • constellation "sentinel-1"
  • start_datetime "2025-07-30T05:03:08.294096Z"
  • sat:orbit_state "descending"
  • eopf:datatake_id "28343"
  • product:timeliness "PT3H"
  • sar:frequency_band "C"
  • sat:absolute_orbit 3445
  • sat:relative_orbit 124
  • processing:software

    • Sentinel-1 IPF "003.92"
  • sar:instrument_mode "IW"
  • eopf:instrument_mode "IW"
  • sar:center_frequency 5.405
  • product:timeliness_category "NRT-3h"
• links[] 5 items

  • 0

    • rel "collection"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-1-l2-ocn"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-1-l2-ocn"
    • type "application/json"

  • 2

    • rel "root"
    • href "https://stac.core.eopf.eodc.eu"
    • type "application/json"
    • title "EOPF Sentinel Zarr Samples Service STAC API"

  • 3

    • rel "self"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-1-l2-ocn/items/S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E"
    • type "application/geo+json"

  • 4

    • rel "license"
    • href "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
    • type "application/pdf"
    • title "Legal notice on the use of Copernicus Sentinel Data and Service Information"
• assets

  • osw

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202507-s01siwocn/30/products/cpm_v256/S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E.zarr/osw/S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV/measurements"
    • type "application/vnd+zarr"
    • title "Ocean Swell spectra"
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • owi

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202507-s01siwocn/30/products/cpm_v256/S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E.zarr/owi/S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV/measurements"
    • type "application/vnd+zarr"
    • title "Ocean Wind field"
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • rvl

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202507-s01siwocn/30/products/cpm_v256/S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E.zarr/rvl/S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV/measurements"
    • type "application/vnd+zarr"
    • title "Surface Radial Velocity"
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • product

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202507-s01siwocn/30/products/cpm_v256/S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E.zarr"
    • type "application/vnd+zarr"
    • title "EOPF Product"
    • description "The full Zarr store of the EOPF product"
    • xarray:open_datatree_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "metadata"
  • product_metadata

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202507-s01siwocn/30/products/cpm_v256/S1C_IW_OCN__2SDV_20250730T050308_20250730T050333_003445_006EB7_446E.zarr/.zmetadata"
    • type "application/json"
    • title "Consolidated Metadata"
    • description "Consolidated metadata of the EOPF product"
    • roles[] 1 items

      • 0 "metadata"
• collection "sentinel-1-l2-ocn"

Open Sentinel-1 Level-2 OCN as DataTree¶

We can use the "product" asset to obtain the href and xarray:open_datatree_kwargs from the STAC item, and open the product as an xarray.DataTree as shown below:

In [13]:

%%time
dt = xr.open_datatree(
    item.assets["product"].href,
    **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"]
)
dt

%%time dt = xr.open_datatree( item.assets["product"].href, **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"] ) dt

CPU times: user 89.9 ms, sys: 2.47 ms, total: 92.4 ms
Wall time: 261 ms

Out[13]:

<xarray.DatasetView> Size: 0B
Dimensions:  ()
Data variables:
    *empty*
Attributes: (2)

xarray.DataTree

• Groups: (3)
  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*
  Attributes: (2)

  osw

  • Groups: (1)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*
    Attributes: (2)

    S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV

    • Groups: (3)
      <xarray.DatasetView> Size: 153B
      Dimensions:                                 (azimuth: 1, range: 1,
                                                   partition: 1, lag: 1, ky: 1,
                                                   kx: 1, angular_binsize: 1,
                                                   wavenumber_binsize: 1)
      Coordinates:
          jx                                      (kx) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
          jy                                      (ky) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
          k                                       (wavenumber_binsize) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
        * kx                                      (kx) float32 4B nan
        * ky                                      (ky) float32 4B nan
          latitude                                (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          longitude                               (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          phi                                     (angular_binsize) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range, partition, lag,
                                      angular_binsize, wavenumber_binsize
      Data variables: (12/31)
          ambiguity_factor                        (azimuth, range, partition) float32 4B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          auxiliary_wind_direction                (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          auxiliary_wind_speed                    (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          azimuth_cutoff                          (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          azimuth_size                            (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          azimuth_size_slc                        (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          ...                                      ...
          range_cutoff                            (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          range_size_slc                          (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          skewness                                (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          spectrum_resolution                     (azimuth, range, angular_binsize) float32 4B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          wind_direction                          (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          wind_speed                              (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>

      conditions

      • Groups: (0)
      • Dimensions:
        • azimuth: 1
        • range: 1
        • partition: 1
        • lag: 1
        • ky: 1
        • kx: 1
        • angular_binsize: 1
        • wavenumber_binsize: 1
      • Coordinates: (8)
        • jx
          (kx)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['kx']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          normalised jacobian array in range direction describing the non-linear resampling transformation for the cartesian cross-spectra, half the array is provided corresponding to positive frequencies, it is normalized by osw_ground_range_size and has to be divided by this later in order to get the actual jacobian vector for the given imagette

          units :

          m/m

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • jy
          (ky)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['ky']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          normalised jacobian array in azimuth direction describing the non-linear resampling transformation for the cartesian cross-spectra, half the array is provided corresponding to positive frequencies, it is normalized by osw_azimuth_size and has to be divided by this later in order to get the actual jacobian vector for the given imagette

          units :

          m/m

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • k
          (wavenumber_binsize)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['wavenumber_binsize']

          dtype :

          <f4

          long_name :

          array of logarithmically spaced wavenumbers for the ocean swell wave spectra

          units :

          rad/m

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • kx
          (kx)
          float32
          nan

          dimensions :

          ['kx']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          normalised positive frequencies array of the cartesian cross-spectra in range direction, it is normalized by osw_ground_range_size and has to be divided by this later in order to get the actual spatial frequency vector for the given imagette

          units :

          m/m

          array([nan], dtype=float32)

        • ky
          (ky)
          float32
          nan

          dimensions :

          ['ky']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          normalised positive frequencies array of the cartesian cross-spectra in azimuth direction, it is normalized by osw_azimuth_size and has to be divided by this later in order to get the actual spatial frequency vector for the given imagette

          units :

          m/m

          array([nan], dtype=float32)

        • latitude
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic latitude at wave cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic longitude at wave cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • phi
          (angular_binsize)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['angular_binsize']

          dtype :

          <f4

          long_name :

          array of equidistantly spaced directional angles for the ocean swell wave spectra

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (31)
        • ambiguity_factor
          (azimuth, range, partition)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'ambiguity factor measuring the ability to estimate wave propagation direction from the sign of the imaginary part of the cross spectra', 'short_name': '/conditions/ambiguity_factor', 'units': '1'}

          dtype :

          <f4

          long_name :

          ambiguity factor measuring the ability to estimate wave propagation direction from the sign of the imaginary part of the cross spectra

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • auxiliary_wind_direction
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'ECMWF wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)', 'short_name': '/conditions/auxiliary_wind_direction', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          ECMWF wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • auxiliary_wind_speed
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'ECMWF wind speed', 'short_name': '/conditions/auxiliary_wind_speed', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          ECMWF wind speed

          units :

          m/s

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • azimuth_cutoff
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'azimuth cut-off wavelength, the shortest wavelength in the azimuth direction that is resolved in the swell wave spectra; the cut-off wavelength in the azimuth direction is computed from the imagette cross-spectra; the spectral resolution is derived from this parameter', 'short_name': '/conditions/azimuth_cutoff', 'units': 'm'}

          dtype :

          <f4

          long_name :

          azimuth cut-off wavelength, the shortest wavelength in the azimuth direction that is resolved in the swell wave spectra; the cut-off wavelength in the azimuth direction is computed from the imagette cross-spectra; the spectral resolution is derived from this parameter

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • azimuth_size
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'azimuth size of the estimation area', 'short_name': '/conditions/azimuth_size', 'units': 'm'}

          dtype :

          <f4

          long_name :

          azimuth size of the estimation area

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • azimuth_size_slc
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'Size of SLC swath over Azimuth dimension', 'short_name': '/conditions/azimuth_size_slc', 'units': 'pixels'}

          dtype :

          <f4

          long_name :

          Size of SLC swath over Azimuth dimension

          units :

          pixels

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • cartesian_spectra_imaginary
          (azimuth, range, lag, ky, kx)
          float32
          dask.array<chunksize=(1, 1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude', 'kx', 'ky', 'jx', 'jy'], 'dimensions': ['azimuth', 'range', 'lag', 'ky', 'kx'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'imaginary part of half cartesian cross-spectra between the different sub-views: zero time lag; 1rst time lag; 2nd time lag', 'short_name': '/conditions/cartesian_spectra_imaginary', 'units': 'm^4'}

          dtype :

          <f4

          long_name :

          imaginary part of half cartesian cross-spectra between the different sub-views: zero time lag; 1rst time lag; 2nd time lag

          units :

          m^4

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1, 1) (1, 1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • cartesian_spectra_real
          (azimuth, range, lag, ky, kx)
          float32
          dask.array<chunksize=(1, 1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude', 'kx', 'ky', 'jx', 'jy'], 'dimensions': ['azimuth', 'range', 'lag', 'ky', 'kx'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'real part of half cartesian cross-spectra between the different sub-views: zero time lag; 1rst time lag; 2nd time lag', 'short_name': '/conditions/cartesian_spectra_real', 'units': 'm^4'}

          dtype :

          <f4

          long_name :

          real part of half cartesian cross-spectra between the different sub-views: zero time lag; 1rst time lag; 2nd time lag

          units :

          m^4

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1, 1) (1, 1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • clutter_noise_cross_covariance_spectra
          (azimuth, range, lag)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'lag'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'Estimated clutter noise levels of the cross-covariance spectra', 'short_name': '/conditions/clutter_noise_cross_covariance_spectra', 'units': ''}

          dtype :

          <f4

          long_name :

          Estimated clutter noise levels of the cross-covariance spectra

          units :

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • clutter_noise_cross_spectra
          (azimuth, range, lag)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'lag'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'Estimated clutter noise levels on cross spectra from which low frequency signal is removed', 'short_name': '/conditions/clutter_noise_cross_spectra', 'units': ''}

          dtype :

          <f4

          long_name :

          Estimated clutter noise levels on cross spectra from which low frequency signal is removed

          units :

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • depth
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'water depth from auxiliary data set', 'short_name': '/conditions/depth', 'units': 'm'}

          dtype :

          <f4

          long_name :

          water depth from auxiliary data set

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • ground_range_size
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'Ground range size of the estimation area', 'short_name': '/conditions/ground_range_size', 'units': 'm'}

          dtype :

          <f4

          long_name :

          Ground range size of the estimation area

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • heading
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'local northing angle (azimuth from north)', 'short_name': '/conditions/heading', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          local northing angle (azimuth from north)

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • incidence_angle
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'radar incidence angle to the center of the wave cell', 'short_name': '/conditions/incidence_angle', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          radar incidence angle to the center of the wave cell

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • intensity
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'input SLC image intensity estimated within each wave cell', 'short_name': '/conditions/intensity', 'units': '1'}

          dtype :

          <f4

          long_name :

          input SLC image intensity estimated within each wave cell

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • kurtosis
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'kurtosis of the input SLC image estimated within each wave cell', 'short_name': '/conditions/kurtosis', 'units': '1'}

          dtype :

          <f4

          long_name :

          kurtosis of the input SLC image estimated within each wave cell

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • land_coverage
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 1e+20, 'long_name': 'percentage of land coverage within the cell', 'short_name': '/conditions/land_coverage', 'units': '%', 'valid_max': 100.0, 'valid_min': 0.0}

          dtype :

          <f4

          long_name :

          percentage of land coverage within the cell

          units :

          %

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • land_flag
          (azimuth, range)
          bool
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<b1', 'flag_meanings': 'no_land land', 'flag_values': '[false true]', 'long_name': 'land flag : true if presence of more than 10% of land coverage, false otherwise', 'short_name': '/conditions/land_flag'}

          dtype :

          <b1

          flag_meanings :

          no_land land

          flag_values :

          [false true]

          long_name :

          land flag : true if presence of more than 10% of land coverage, false otherwise

          Array Chunk Bytes 1 B 1 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • look_separation_time
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'separation time between inner and outer looks', 'short_name': '/conditions/look_separation_time', 'units': 's'}

          dtype :

          <f4

          long_name :

          separation time between inner and outer looks

          units :

          s

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • non_linear_spectral_width
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'non-linear inverse spectral width describing non-linear spectral cut-off computed from the cross-spectra', 'short_name': '/conditions/non_linear_spectral_width', 'units': 'm'}

          dtype :

          <f4

          long_name :

          non-linear inverse spectral width describing non-linear spectral cut-off computed from the cross-spectra

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • normalised_variance
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'variance of the input SLC image normalized by the square of the mean intensity estimated within each wave cell.', 'short_name': '/conditions/normalised_variance', 'units': '1'}

          dtype :

          <f4

          long_name :

          variance of the input SLC image normalized by the square of the mean intensity estimated within each wave cell.

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • nrcs
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'normalized radar cross section of the imagette, normalized to the area on ground', 'short_name': '/conditions/nrcs', 'units': 'dB'}

          dtype :

          <f4

          long_name :

          normalized radar cross section of the imagette, normalized to the area on ground

          units :

          dB

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • nrcs_denoised
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'denoised normalized radar cross section', 'short_name': '/conditions/nrcs_denoised', 'units': 'dB'}

          dtype :

          <f4

          long_name :

          denoised normalized radar cross section

          units :

          dB

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • polar_spectrum
          (azimuth, range, angular_binsize, wavenumber_binsize)
          float32
          dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['k', 'phi', 'longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'angular_binsize', 'wavenumber_binsize'], 'dtype': '<f4', 'long_name': 'two-dimensional representation of the ocean swell waveheight spectrum given on a log-polar grid of wavenumber [rad/m] and angle [degrees]', 'short_name': '/conditions/polar_spectrum', 'units': 'm^4'}

          dtype :

          <f4

          long_name :

          two-dimensional representation of the ocean swell waveheight spectrum given on a log-polar grid of wavenumber [rad/m] and angle [degrees]

          units :

          m^4

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1) (1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • polar_spectrum_normalised_variance
          (azimuth, range, angular_binsize, wavenumber_binsize)
          float32
          dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['k', 'phi', 'longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'angular_binsize', 'wavenumber_binsize'], 'dtype': '<f4', 'long_name': 'two-dimensional representation of the normalized variance of the ocean swell waveheight spectrum given on a log-polar grid of wavenumber [rad/m] and angle [degrees], it is estimated as the wave spectra variance among the periodograms within the overall image and normalized by the mean wave spectrum', 'short_name': '/conditions/polar_spectrum_normalised_variance', 'units': '1'}

          dtype :

          <f4

          long_name :

          two-dimensional representation of the normalized variance of the ocean swell waveheight spectrum given on a log-polar grid of wavenumber [rad/m] and angle [degrees], it is estimated as the wave spectra variance among the periodograms within the overall image and normalized by the mean wave spectrum

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1) (1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • range_cutoff
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'range cut-off wavelength, the shortest wavelength in range direction that can be resolved in the swell wave spectra; it is computed from the slant range resolution (or range bandwidth) and the local incidence angle', 'short_name': '/conditions/range_cutoff', 'units': 'm'}

          dtype :

          <f4

          long_name :

          range cut-off wavelength, the shortest wavelength in range direction that can be resolved in the swell wave spectra; it is computed from the slant range resolution (or range bandwidth) and the local incidence angle

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • range_size_slc
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'ground range size of the estimation area', 'short_name': '/conditions/range_size_slc', 'units': 'm'}

          dtype :

          <f4

          long_name :

          ground range size of the estimation area

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • skewness
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'skewness of the input SLC image estimated within each wave cell', 'short_name': '/conditions/skewness', 'units': '1'}

          dtype :

          <f4

          long_name :

          skewness of the input SLC image estimated within each wave cell

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • spectrum_resolution
          (azimuth, range, angular_binsize)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['phi', 'longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'angular_binsize'], 'dtype': '<f4', 'long_name': 'azimuth cut-off wavelength as function of wave direction; the spectral resolution gives the shortest ocean wavelength that can be detected; this depends on the sea state and the wave direction relative to azimuth; the vector length is equal to the number of directional bins; in range, the theoretical limit is given by the range bandwidth, and does not depend on the sea state', 'short_name': '/conditions/spectrum_resolution', 'units': 'm'}

          dtype :

          <f4

          long_name :

          azimuth cut-off wavelength as function of wave direction; the spectral resolution gives the shortest ocean wavelength that can be detected; this depends on the sea state and the wave direction relative to azimuth; the vector length is equal to the number of directional bins; in range, the theoretical limit is given by the range bandwidth, and does not depend on the sea state

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wind_direction
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'wind direction (meteorological convention = clockwise direction from where the wind comes respect to North)', 'short_name': '/conditions/wind_direction', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          wind direction (meteorological convention = clockwise direction from where the wind comes respect to North)

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wind_speed
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'wind speed used for wave spectral retrieval (this value is derived from the NRCS)', 'short_name': '/conditions/wind_speed', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          wind speed used for wave spectral retrieval (this value is derived from the NRCS)

          units :

          m/s

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 52B
      Dimensions:                      (azimuth: 1, range: 1, partition: 1,
                                        angular_binsize: 1, wavenumber_binsize: 1)
      Coordinates:
          k                            (wavenumber_binsize) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
          latitude                     (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          longitude                    (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          phi                          (angular_binsize) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range, partition, angular_binsize,
                                      wavenumber_binsize
      Data variables:
          hs                           (azimuth, range, partition) float32 4B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          hs_normalised_variance       (azimuth, range, partition) float32 4B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          partitions                   (azimuth, range, angular_binsize, wavenumber_binsize) float32 4B dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>
          total_hs                     (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          total_hs_standard_deviation  (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          wave_age                     (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          wave_direction               (azimuth, range, partition) float32 4B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          wave_length                  (azimuth, range, partition) float32 4B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          wind_sea_hs                  (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>

      measurements

      • Groups: (0)
      • Dimensions:
        • azimuth: 1
        • range: 1
        • partition: 1
        • angular_binsize: 1
        • wavenumber_binsize: 1
      • Coordinates: (4)
        • k
          (wavenumber_binsize)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['wavenumber_binsize']

          dtype :

          <f4

          long_name :

          array of logarithmically spaced wavenumbers for the ocean swell wave spectra

          units :

          rad/m

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • latitude
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic latitude at wave cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic longitude at wave cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • phi
          (angular_binsize)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['angular_binsize']

          dtype :

          <f4

          long_name :

          array of equidistantly spaced directional angles for the ocean swell wave spectra

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (9)
        • hs
          (azimuth, range, partition)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'significant wave height computed from the most energetic wave partitions of the swell wave spectra', 'short_name': '/measurements/hs', 'units': 'm'}

          dtype :

          <f4

          long_name :

          significant wave height computed from the most energetic wave partitions of the swell wave spectra

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • hs_normalised_variance
          (azimuth, range, partition)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'value of the ocean swell spectrum normalized variance computed for the most energetic wave partitions of the swell wave spectra, weighted by the spectral energy', 'short_name': '/measurements/hs_normalised_variance', 'units': '1'}

          dtype :

          <f4

          long_name :

          value of the ocean swell spectrum normalized variance computed for the most energetic wave partitions of the swell wave spectra, weighted by the spectral energy

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • partitions
          (azimuth, range, angular_binsize, wavenumber_binsize)
          float32
          dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['k', 'phi', 'longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'angular_binsize', 'wavenumber_binsize'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -128, 'long_name': 'total significant wave height', 'short_name': '/measurements/partitions', 'units': '1'}

          dtype :

          <f4

          long_name :

          total significant wave height

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1) (1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • total_hs
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'total significant wave height', 'short_name': '/measurements/total_hs', 'units': 'm'}

          dtype :

          <f4

          long_name :

          total significant wave height

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • total_hs_standard_deviation
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'standard deviation of total significant wave height', 'short_name': '/measurements/total_hs_standard_deviation', 'units': 'm'}

          dtype :

          <f4

          long_name :

          standard deviation of total significant wave height

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wave_age
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'parameter that describes the state of development of the wind sea component of the wave spectra', 'short_name': '/measurements/wave_age', 'units': '1'}

          dtype :

          <f4

          long_name :

          parameter that describes the state of development of the wind sea component of the wave spectra

          units :

          1

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wave_direction
          (azimuth, range, partition)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'dominant wave direction of the swell wave spectrum for each wave partition (with respect to North)', 'short_name': '/measurements/wave_direction', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          dominant wave direction of the swell wave spectrum for each wave partition (with respect to North)

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wave_length
          (azimuth, range, partition)
          float32
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'dominant wave length of the swell wave spectrum for each wave partition', 'short_name': '/measurements/wave_length', 'units': 'm'}

          dtype :

          <f4

          long_name :

          dominant wave length of the swell wave spectrum for each wave partition

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wind_sea_hs
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'significant wave height for the wind sea part of the total wave height spectrum, computed from the wind speed and the inverse wave age', 'short_name': '/measurements/wind_sea_hs', 'units': 'm'}

          dtype :

          <f4

          long_name :

          significant wave height for the wind sea part of the total wave height spectrum, computed from the wind speed and the inverse wave age

          units :

          m

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 52B
      Dimensions:                               (azimuth: 1, range: 1, partition: 1,
                                                 angular_binsize: 1,
                                                 wavenumber_binsize: 1)
      Coordinates:
          k                                     (wavenumber_binsize) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
          latitude                              (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          longitude                             (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>
          phi                                   (angular_binsize) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range, partition, angular_binsize,
                                      wavenumber_binsize
      Data variables:
          i_confidence                          (azimuth, range, partition) object 8B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          partition_quality_flag                (azimuth, range, partition) float64 8B dask.array<chunksize=(1, 1, 1), meta=np.ndarray>
          quality_cross_spectra_imaginary_part  (azimuth, range, angular_binsize, wavenumber_binsize) float32 4B dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>
          quality_cross_spectra_real_part       (azimuth, range, angular_binsize, wavenumber_binsize) float32 4B dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>
          quality_flag                          (azimuth, range) float64 8B dask.array<chunksize=(1, 1), meta=np.ndarray>
          snr                                   (azimuth, range) float32 4B dask.array<chunksize=(1, 1), meta=np.ndarray>

      quality

      • Groups: (0)
      • Dimensions:
        • azimuth: 1
        • range: 1
        • partition: 1
        • angular_binsize: 1
        • wavenumber_binsize: 1
      • Coordinates: (4)
        • k
          (wavenumber_binsize)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['wavenumber_binsize']

          dtype :

          <f4

          long_name :

          array of logarithmically spaced wavenumbers for the ocean swell wave spectra

          units :

          rad/m

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • latitude
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic latitude at wave cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic longitude at wave cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • phi
          (angular_binsize)
          float32
          dask.array<chunksize=(1,), meta=np.ndarray>

          dimensions :

          ['angular_binsize']

          dtype :

          <f4

          long_name :

          array of equidistantly spaced directional angles for the ocean swell wave spectra

          units :

          degrees

          Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (6)
        • i_confidence
          (azimuth, range, partition)
          object
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<b1', 'fill_value': False, 'flag_meanings': 'resolved ambiguous', 'flag_values': '[false true]', 'long_name': 'confidence flag for wave direction resolution, false if the wave direction is resolved and true if there is a 180-degree ambiguity in the wave direction', 'short_name': '/quality/i_confidence'}

          dtype :

          <b1

          flag_meanings :

          resolved ambiguous

          flag_values :

          [false true]

          long_name :

          confidence flag for wave direction resolution, false if the wave direction is resolved and true if there is a 180-degree ambiguity in the wave direction

          Array Chunk Bytes 8 B 8 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type object numpy.ndarray

        • partition_quality_flag
          (azimuth, range, partition)
          float64
          dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'partition'], 'dtype': '<u1', 'eopf_is_masked': True, 'fill_value': 1e+20, 'flag_meanings': 'excellent good fair low bad', 'flag_values': '[0 1 2 3 4]', 'long_name': 'quality flag for the integral parameters of each partition', 'short_name': '/quality/partition_quality_flag', 'valid_max': 4, 'valid_min': 0}

          dtype :

          <u1

          flag_meanings :

          excellent good fair low bad

          flag_values :

          [0 1 2 3 4]

          long_name :

          quality flag for the integral parameters of each partition

          Array Chunk Bytes 8 B 8 B Shape (1, 1, 1) (1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • quality_cross_spectra_imaginary_part
          (azimuth, range, angular_binsize, wavenumber_binsize)
          float32
          dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['k', 'phi', 'longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'angular_binsize', 'wavenumber_binsize'], 'dtype': '<f4', 'long_name': 'imaginary part of quality image cross spectra given on a log-polar grid of wavenumber [rad/m] and angle [degrees]', 'short_name': '/quality/quality_cross_spectra_imaginary_part', 'units': '(m/rad)^2'}

          dtype :

          <f4

          long_name :

          imaginary part of quality image cross spectra given on a log-polar grid of wavenumber [rad/m] and angle [degrees]

          units :

          (m/rad)^2

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1) (1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • quality_cross_spectra_real_part
          (azimuth, range, angular_binsize, wavenumber_binsize)
          float32
          dask.array<chunksize=(1, 1, 1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['k', 'phi', 'longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'angular_binsize', 'wavenumber_binsize'], 'dtype': '<f4', 'long_name': 'real part of quality image cross spectra given on a log-polar grid of wavenumber [rad/m] and angle [degrees]', 'short_name': '/quality/quality_cross_spectra_real_part', 'units': '(m/rad)^2'}

          dtype :

          <f4

          long_name :

          real part of quality image cross spectra given on a log-polar grid of wavenumber [rad/m] and angle [degrees]

          units :

          (m/rad)^2

          Array Chunk Bytes 4 B 4 B Shape (1, 1, 1, 1) (1, 1, 1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • quality_flag
          (azimuth, range)
          float64
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<u1', 'eopf_is_masked': True, 'fill_value': 1e+20, 'flag_meanings': 'good fair low bad', 'flag_values': '[0 1 2 3]', 'long_name': 'final quality flag of the directional swell spectrum', 'short_name': '/quality/quality_flag', 'valid_max': 3, 'valid_min': 0}

          dtype :

          <u1

          flag_meanings :

          good fair low bad

          flag_values :

          [0 1 2 3]

          long_name :

          final quality flag of the directional swell spectrum

          Array Chunk Bytes 8 B 8 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • snr
          (azimuth, range)
          float32
          dask.array<chunksize=(1, 1), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'signal-to-noise ratio of cross spectra', 'short_name': '/quality/snr', 'units': 'dB'}

          dtype :

          <f4

          long_name :

          signal-to-noise ratio of cross spectra

          units :

          dB

          Array Chunk Bytes 4 B 4 B Shape (1, 1) (1, 1) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (2)

      other_metadata :

      {'algorithm_version': 'S-1 OSW rev 1/7', 'azimuth_sampling_freq': 0.0, 'bathymetry_source': 'GEBCO', 'clm_source': 'OpenStreetMap', 'eopf_category': 'eoproduct', 'gmf': 'CMOD5n', 'ground_velocity': 0.0, 'history': 'MISSING', 'l2rp_version': 'Product prototype', 'mission_phase': 'Operational', 'noise_correction': '', 'osw_status': 'missing', 'polarisation': 'VV', 'polarisation_ratio': 'Identity', 'prf': 486.48631029955294, 'processing_center': 'APF', 'processing_start_time': '2025-07-30T05:38:08.924369Z', 'radar_frequency': 0.0, 'radar_sampling_freq': 0.0, 'statevector_acc': [0.0, 0.0, 0.0], 'statevector_pos': [5090845.987955, 1957917.965155, 4498843.204924], 'statevector_utc': '30-JUL-2025 05:03:18.318570', 'statevector_vel': [5030.729537, 24.443297, -5687.332543], 'title': "'Sentinel-1 OCN OSW Product'", 'wind_source': 'ECMWF'}

      stac_discovery :

      {'assets': {}, 'bbox': [17.000872, 39.495934, 13.571783, 41.406952], 'geometry': {'coordinates': [[[16.616774, 39.495934], [13.571783, 39.906666], [13.88713, 41.406952], [17.000872, 40.997356]]], 'type': 'Polygon'}, 'id': 'SAR Standard L2 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}, 'osw', 'owi', 'rvl'], 'properties': {'processing:version': '004.00', 'product:type': 'S01SIWOCN'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json'], 'stac_version': '1.0.0', 'type': 'Feature'}

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (2)

    other_metadata :

    {'eopf_category': 'eocontainer'}

    stac_discovery :

    {'links': ['S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV'], 'properties': {'product:type': ''}}

  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*
  Attributes: (2)

  owi

  • Groups: (1)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*
    Attributes: (2)

    S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV

    • Groups: (3)
      <xarray.DatasetView> Size: 3MB
      Dimensions:                   (azimuth: 166, range: 265, polarisation: 2)
      Coordinates:
          latitude                  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          longitude                 (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
        * polarisation              (polarisation) uint8 2B 0 1
      Dimensions without coordinates: azimuth, range
      Data variables:
          auxiliary_wind_direction  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          auxiliary_wind_speed      (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          elevation_angle           (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          heading                   (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          incidence_angle           (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          mask                      (azimuth, range) float64 352kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          nesz                      (azimuth, range, polarisation) float32 352kB dask.array<chunksize=(166, 265, 2), meta=np.ndarray>
          nrcs                      (azimuth, range, polarisation) float32 352kB dask.array<chunksize=(166, 265, 2), meta=np.ndarray>
          nrcs_noise_corrected      (azimuth, range, polarisation) float32 352kB dask.array<chunksize=(166, 265, 2), meta=np.ndarray>
          nrcs_wind_gmf             (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>

      conditions

      • Groups: (0)
      • Dimensions:
        • azimuth: 166
        • range: 265
        • polarisation: 2
      • Coordinates: (3)
        • latitude
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic latitude at wind cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic longitude at wind cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • polarisation
          (polarisation)
          uint8
          0 1

          dimensions :

          ['polarisation']

          dtype :

          <u1

          flag_meanings :

          VV VH HH HV DH DV

          flag_values :

          [0 1 2 3 4 5]

          long_name :

          identifier of the receiving polarization in order to discriminate co and cross polarisations

          array([0, 1], dtype=uint8)

      • Data variables: (10)
        • auxiliary_wind_direction
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'ECMWF wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)', 'short_name': '/conditions/auxiliary_wind_direction', 'standard_name': 'wind_direction', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          ECMWF wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)

          standard_name :

          wind_direction

          units :

          degrees

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • auxiliary_wind_speed
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'ECMWF wind speed', 'short_name': '/conditions/auxiliary_wind_speed', 'standard_name': 'wind_speed', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          ECMWF wind speed

          standard_name :

          wind_speed

          units :

          m/s

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • elevation_angle
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'radar elevation angle at wind cell center, this parameter is provided at each azimuth line in the wind cell grid', 'short_name': '/conditions/elevation_angle', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          radar elevation angle at wind cell center, this parameter is provided at each azimuth line in the wind cell grid

          units :

          degrees

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • heading
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'local northing angle (azimuth from north)', 'short_name': '/conditions/heading', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          local northing angle (azimuth from north)

          units :

          degrees

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • incidence_angle
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'long_name': 'radar incidence angle at wind cell center, this parameter is provided at each azimuth line in the wind cell grid', 'short_name': '/conditions/incidence_angle', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          radar incidence angle at wind cell center, this parameter is provided at each azimuth line in the wind cell grid

          units :

          degrees

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • mask
          (azimuth, range)
          float64
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<u1', 'eopf_is_masked': True, 'fill_value': 1e+20, 'flag_meanings': 'valid_data land ice no_data', 'flag_values': '[0, 1, 2, 4]', 'long_name': 'mask of classification: set to 1 if land coverage > 10%, set to 2 if inside 10km-dilated ice edge area', 'short_name': '/conditions/mask', 'valid_max': 8, 'valid_min': 0}

          dtype :

          <u1

          flag_meanings :

          valid_data land ice no_data

          flag_values :

          [0, 1, 2, 4]

          long_name :

          mask of classification: set to 1 if land coverage > 10%, set to 2 if inside 10km-dilated ice edge area

          Array Chunk Bytes 343.67 kiB 343.67 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • nesz
          (azimuth, range, polarisation)
          float32
          dask.array<chunksize=(166, 265, 2), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude', 'polarisation'], 'dimensions': ['azimuth', 'range', 'polarisation'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'noise equivalent sigma-naught, equivalent to measured radar cross section normalized to the area on ground; this is a copy of the level-1 calibrated noise LUT interpolated on the wind cell grid', 'short_name': '/conditions/nesz', 'units': 'm^2/m^2'}

          dtype :

          <f4

          long_name :

          noise equivalent sigma-naught, equivalent to measured radar cross section normalized to the area on ground; this is a copy of the level-1 calibrated noise LUT interpolated on the wind cell grid

          units :

          m^2/m^2

          Array Chunk Bytes 343.67 kiB 343.67 kiB Shape (166, 265, 2) (166, 265, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • nrcs
          (azimuth, range, polarisation)
          float32
          dask.array<chunksize=(166, 265, 2), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude', 'polarisation'], 'dimensions': ['azimuth', 'range', 'polarisation'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'normalized radar cross section of the imagette, measured radar cross section normalized to the area on ground', 'short_name': '/conditions/nrcs', 'units': 'm^2/m^2'}

          dtype :

          <f4

          long_name :

          normalized radar cross section of the imagette, measured radar cross section normalized to the area on ground

          units :

          m^2/m^2

          Array Chunk Bytes 343.67 kiB 343.67 kiB Shape (166, 265, 2) (166, 265, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • nrcs_noise_corrected
          (azimuth, range, polarisation)
          float32
          dask.array<chunksize=(166, 265, 2), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude', 'polarisation'], 'dimensions': ['azimuth', 'range', 'polarisation'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'noise corrected NRCS averaged within and provided for each wind cell', 'short_name': '/conditions/nrcs_noise_corrected', 'units': 'm^2/m^2'}

          dtype :

          <f4

          long_name :

          noise corrected NRCS averaged within and provided for each wind cell

          units :

          m^2/m^2

          Array Chunk Bytes 343.67 kiB 343.67 kiB Shape (166, 265, 2) (166, 265, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • nrcs_wind_gmf
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'predicted normalized radar cross section from CMOD and ECMWF 10m wind for each wind cell', 'short_name': '/conditions/nrcs_wind_gmf', 'units': 'm^2/m^2'}

          dtype :

          <f4

          long_name :

          predicted normalized radar cross section from CMOD and ECMWF 10m wind for each wind cell

          units :

          m^2/m^2

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 704kB
      Dimensions:         (azimuth: 166, range: 265)
      Coordinates:
          latitude        (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          longitude       (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range
      Data variables:
          wind_direction  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          wind_speed      (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>

      measurements

      • Groups: (0)
      • Dimensions:
        • azimuth: 166
        • range: 265
      • Coordinates: (2)
        • latitude
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic latitude at wind cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic longitude at wind cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (2)
        • wind_direction
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'SAR wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)', 'short_name': '/measurements/wind_direction', 'standard_name': 'wind_direction', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          SAR wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)

          standard_name :

          wind_direction

          units :

          degrees

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wind_speed
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'SAR wind speed', 'short_name': '/measurements/wind_speed', 'standard_name': 'wind_speed', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          SAR wind speed

          standard_name :

          wind_speed

          units :

          m/s

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 1MB
      Dimensions:                   (calib_const_inc_angles: 158, azimuth: 166,
                                     range: 265)
      Coordinates:
          latitude                  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          longitude                 (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
      Dimensions without coordinates: calib_const_inc_angles, azimuth, range
      Data variables:
          calibration_constant      (calib_const_inc_angles) float32 632B dask.array<chunksize=(158,), meta=np.ndarray>
          inversion_quality         (azimuth, range) float64 352kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          percentage_bright_points  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
          wind_quality              (azimuth, range) float64 352kB dask.array<chunksize=(166, 265), meta=np.ndarray>

      quality

      • Groups: (0)
      • Dimensions:
        • calib_const_inc_angles: 158
        • azimuth: 166
        • range: 265
      • Coordinates: (2)
        • latitude
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic latitude at wind cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range']

          dtype :

          <f4

          long_name :

          geodetic longitude at wind cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (4)
        • calibration_constant
          (calib_const_inc_angles)
          float32
          dask.array<chunksize=(158,), meta=np.ndarray>

          _eopf_attrs :

          {'dimensions': ['calib_const_inc_angles'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'geophysical calibration constant estimated from ECMWF and CMOD GMF', 'short_name': '/quality/calibration_constant'}

          dtype :

          <f4

          long_name :

          geophysical calibration constant estimated from ECMWF and CMOD GMF

          Array Chunk Bytes 632 B 632 B Shape (158,) (158,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • inversion_quality
          (azimuth, range)
          float64
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<u1', 'eopf_is_masked': True, 'fill_value': 1e+20, 'flag_meanings': 'good fair bad', 'flag_values': '[0 1 2]', 'long_name': 'inversion quality flag for each wind cell', 'short_name': '/quality/inversion_quality', 'valid_max': 2, 'valid_min': 0}

          dtype :

          <u1

          flag_meanings :

          good fair bad

          flag_values :

          [0 1 2]

          long_name :

          inversion quality flag for each wind cell

          Array Chunk Bytes 343.67 kiB 343.67 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

        • percentage_bright_points
          (azimuth, range)
          float32
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'percentage of bright target pixels detected in each wind cell and removed for the computation of the mean NRCS', 'short_name': '/quality/percentage_bright_points', 'units': '%', 'valid_max': 100.0, 'valid_min': 0.0}

          dtype :

          <f4

          long_name :

          percentage of bright target pixels detected in each wind cell and removed for the computation of the mean NRCS

          units :

          %

          Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • wind_quality
          (azimuth, range)
          float64
          dask.array<chunksize=(166, 265), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<u1', 'eopf_is_masked': True, 'fill_value': -128, 'flag_meanings': 'good fair low bad', 'flag_values': '[0 1 2 3]', 'long_name': 'wind quality flag for each wind cell; this flag takes into account the consistency between wind invertion, NRCS and Doppler', 'short_name': '/quality/wind_quality', 'valid_max': 3, 'valid_min': 0}

          dtype :

          <u1

          flag_meanings :

          good fair low bad

          flag_values :

          [0 1 2 3]

          long_name :

          wind quality flag for each wind cell; this flag takes into account the consistency between wind invertion, NRCS and Doppler

          Array Chunk Bytes 343.67 kiB 343.67 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (2)

      other_metadata :

      {'algorithm_version': 'S-1 OWI rev 2/2', 'azimuth_sampling_freq': 0.0, 'clm_source': 'OpenStreetMap', 'eopf_category': 'eoproduct', 'gmf': 'CMOD5n', 'ground_velocity': 0.0, 'history': 'MISSING', 'ice_source': 'EUMETSAT OSI SAF', 'l2rp_version': 'Product prototype', 'mission_phase': 'Operational', 'noise_correction': 'True', 'owi_status': 'nominal', 'polarisation': 'VV', 'polarisation_ratio': 'Identity', 'prf': 486.48631029955294, 'processing_center': 'APF', 'processing_start_time': '2025-07-30T05:38:08.924369Z', 'radar_frequency': 0.0, 'radar_sampling_freq': 0.0, 'statevector_acc': [0.0, 0.0, 0.0], 'statevector_pos': [5090845.987955, 1957917.965155, 4498843.204924], 'statevector_utc': '30-JUL-2025 05:03:18.318570', 'statevector_vel': [5030.729537, 24.443297, -5687.332543], 'title': 'Sentinel-1 OCN OWI Product', 'wind_source': 'ECMWF'}

      stac_discovery :

      {'assets': {}, 'bbox': [17.000872, 39.495934, 13.571783, 41.406952], 'geometry': {'coordinates': [[[16.616774, 39.495934], [13.571783, 39.906666], [13.88713, 41.406952], [17.000872, 40.997356]]], 'type': 'Polygon'}, 'id': 'SAR Standard L2 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}, 'osw', 'owi', 'rvl'], 'properties': {'processing:version': '004.00', 'product:type': 'S01SIWOCN'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json'], 'stac_version': '1.0.0', 'type': 'Feature'}

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (2)

    other_metadata :

    {'eopf_category': 'eocontainer'}

    stac_discovery :

    {'links': ['S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV'], 'properties': {'product:type': ''}}

  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*
  Attributes: (2)

  rvl

  • Groups: (1)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*
    Attributes: (2)

    S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV

    • Groups: (3)
      <xarray.DatasetView> Size: 9MB
      Dimensions:            (azimuth: 212, range: 136, swath: 3, length: 26)
      Coordinates:
          latitude           (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          longitude          (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range, swath, length
      Data variables: (12/18)
          azimuth_size       (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          dc_geo             (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          dc_miss            (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          dc_obs             (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          ground_range_size  (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          heading            (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          ...                 ...
          slant_range_time   (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          stokes_drift_x     (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          stokes_drift_y     (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          sweep_angle        (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          yaw                (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          zero_doppler_time  (azimuth, range, length, swath) |S1 2MB dask.array<chunksize=(212, 136, 26, 3), meta=np.ndarray>

      conditions

      • Groups: (0)
      • Dimensions:
        • azimuth: 212
        • range: 136
        • swath: 3
        • length: 26
      • Coordinates: (2)
        • latitude
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range', 'swath']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          geodetic latitude at cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range', 'swath']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          geodetic longitude at cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (18)
        • azimuth_size
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'azimuth resolution, the pixel size is half this value', 'short_name': '/conditions/azimuth_size', 'units': 'm'}

          dtype :

          <f4

          long_name :

          azimuth resolution, the pixel size is half this value

          units :

          m

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • dc_geo
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'doppler centroid frequency from geometry, this parameter is interpolated from the L1 data', 'short_name': '/conditions/dc_geo', 'units': 'Hz'}

          dtype :

          <f4

          long_name :

          doppler centroid frequency from geometry, this parameter is interpolated from the L1 data

          units :

          Hz

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • dc_miss
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'doppler centroid frequency from antenna mispointing at cell centre', 'short_name': '/conditions/dc_miss', 'units': 'Hz'}

          dtype :

          <f4

          long_name :

          doppler centroid frequency from antenna mispointing at cell centre

          units :

          Hz

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • dc_obs
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'estimated doppler centroid frequency', 'short_name': '/conditions/dc_obs', 'units': 'Hz'}

          dtype :

          <f4

          long_name :

          estimated doppler centroid frequency

          units :

          Hz

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • ground_range_size
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'ground range resolution, the pixel size is half this value', 'short_name': '/conditions/ground_range_size', 'units': 'm'}

          dtype :

          <f4

          long_name :

          ground range resolution, the pixel size is half this value

          units :

          m

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • heading
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'local northing angle (azimuth from north)', 'short_name': '/conditions/heading', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          local northing angle (azimuth from north)

          units :

          degrees

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • incidence_angle
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'radar incidence angle at cell center, this parameter is provided at each azimuth line in the radial velocity cell grid', 'short_name': '/conditions/incidence_angle', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          radar incidence angle at cell center, this parameter is provided at each azimuth line in the radial velocity cell grid

          units :

          degrees

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • land_coverage
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'percentage of land coverage within the cell', 'short_name': '/conditions/land_coverage', 'valid_max': 100.0, 'valid_min': 0.0}

          dtype :

          <f4

          long_name :

          percentage of land coverage within the cell

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • land_flag
          (azimuth, range, swath)
          bool
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<b1', 'eopf_is_masked': True, 'flag_meanings': 'no_land land', 'flag_values': '[false true]', 'long_name': 'land flag : true if presence of more than 10% of land coverage, false otherwise', 'short_name': '/conditions/land_flag'}

          dtype :

          <b1

          flag_meanings :

          no_land land

          flag_values :

          [false true]

          long_name :

          land flag : true if presence of more than 10% of land coverage, false otherwise

          Array Chunk Bytes 84.47 kiB 84.47 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • nrcs
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'normalized radar cross section, side-band corrected signal intensity; the intensity is estimated within and provided for each radial velocity cell', 'short_name': '/conditions/nrcs', 'units': 'm^2/m^2'}

          dtype :

          <f4

          long_name :

          normalized radar cross section, side-band corrected signal intensity; the intensity is estimated within and provided for each radial velocity cell

          units :

          m^2/m^2

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • pitch
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'platform pitch angle at zero doppler time', 'short_name': '/conditions/pitch', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          platform pitch angle at zero doppler time

          units :

          degrees

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • roll
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'platform roll angle at zero doppler time', 'short_name': '/conditions/roll', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          platform roll angle at zero doppler time

          units :

          degrees

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • slant_range_time
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'two-way slant range time to cell center', 'short_name': '/conditions/slant_range_time', 'units': 's'}

          dtype :

          <f4

          long_name :

          two-way slant range time to cell center

          units :

          s

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • stokes_drift_x
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'easterly component of surface stokes drift from wavewatchIII model', 'short_name': '/conditions/stokes_drift_x', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          easterly component of surface stokes drift from wavewatchIII model

          units :

          m/s

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • stokes_drift_y
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'northerly component of surface stokes drift from wavewatchIII model', 'short_name': '/conditions/stokes_drift_y', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          northerly component of surface stokes drift from wavewatchIII model

          units :

          m/s

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • sweep_angle
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'sweep angle of the TOPS mode', 'short_name': '/conditions/sweep_angle', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          sweep angle of the TOPS mode

          units :

          degrees

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • yaw
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'platform yaw angle at zero doppler time', 'short_name': '/conditions/yaw', 'units': 'degrees'}

          dtype :

          <f4

          long_name :

          platform yaw angle at zero doppler time

          units :

          degrees

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • zero_doppler_time
          (azimuth, range, length, swath)
          |S1
          dask.array<chunksize=(212, 136, 26, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'length', 'swath'], 'dtype': 'S1', 'eopf_is_masked': True, 'long_name': 'zero doppler time at cell center', 'short_name': '/conditions/zero_doppler_time'}

          dtype :

          S1

          long_name :

          zero doppler time at cell center

          Array Chunk Bytes 2.14 MiB 2.14 MiB Shape (212, 136, 26, 3) (212, 136, 26, 3) Dask graph 1 chunks in 2 graph layers Data type |S1 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 1MB
      Dimensions:                             (azimuth: 212, range: 136, swath: 3)
      Coordinates:
          latitude                            (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          longitude                           (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range, swath
      Data variables:
          radial_velocity                     (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          radial_velocity_standard_deviation  (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

      measurements

      • Groups: (0)
      • Dimensions:
        • azimuth: 212
        • range: 136
        • swath: 3
      • Coordinates: (2)
        • latitude
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range', 'swath']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          geodetic latitude at cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range', 'swath']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          geodetic longitude at cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (2)
        • radial_velocity
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'radial velocity', 'short_name': '/measurements/radial_velocity', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          radial velocity

          units :

          m/s

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • radial_velocity_standard_deviation
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'standard deviation of radial velocities', 'short_name': '/measurements/radial_velocity_standard_deviation', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          standard deviation of radial velocities

          units :

          m/s

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 1MB
      Dimensions:            (azimuth: 212, range: 136, swath: 3)
      Coordinates:
          latitude           (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          longitude          (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
      Dimensions without coordinates: azimuth, range, swath
      Data variables:
          dc_obs_confidence  (azimuth, range, swath) bool 86kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          dc_obs_std         (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>
          snr                (azimuth, range, swath) float32 346kB dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

      quality

      • Groups: (0)
      • Dimensions:
        • azimuth: 212
        • range: 136
        • swath: 3
      • Coordinates: (2)
        • latitude
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range', 'swath']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          geodetic latitude at cell center

          standard_name :

          latitude

          units :

          degrees_north

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • longitude
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          dimensions :

          ['azimuth', 'range', 'swath']

          dtype :

          <f4

          eopf_is_masked :

          True

          fill_value :

          -999.0

          long_name :

          geodetic longitude at cell center

          standard_name :

          longitude

          units :

          degrees_east

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Data variables: (3)
        • dc_obs_confidence
          (azimuth, range, swath)
          bool
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<b1', 'eopf_is_masked': True, 'flag_meanings': 'bad good', 'flag_values': '[false true]', 'long_name': 'confidence flag on estimated doppler centroid frequency; true is good confidence and false is bad confidence', 'short_name': '/quality/dc_obs_confidence'}

          dtype :

          <b1

          flag_meanings :

          bad good

          flag_values :

          [false true]

          long_name :

          confidence flag on estimated doppler centroid frequency; true is good confidence and false is bad confidence

          Array Chunk Bytes 84.47 kiB 84.47 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

        • dc_obs_std
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'estimated doppler centroid frequency standard deviation', 'short_name': '/quality/dc_obs_std', 'units': 'Hz'}

          dtype :

          <f4

          long_name :

          estimated doppler centroid frequency standard deviation

          units :

          Hz

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • snr
          (azimuth, range, swath)
          float32
          dask.array<chunksize=(212, 136, 3), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range', 'swath'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'signal-to-noise ratio estimated from doppler spectra', 'short_name': '/quality/snr', 'units': '1'}

          dtype :

          <f4

          long_name :

          signal-to-noise ratio estimated from doppler spectra

          units :

          1

          Array Chunk Bytes 337.88 kiB 337.88 kiB Shape (212, 136, 3) (212, 136, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (2)

      other_metadata :

      {'algorithm_version': 'S-1 RVL rev 1/6', 'azimuth_sampling_freq': 0.0, 'clm_source': 'OpenStreetMap', 'eopf_category': 'eoproduct', 'ground_velocity': 0.0, 'history': 'MISSING', 'mission_phase': 'Operational', 'polarisation': 'VV', 'prf': 486.48631029955294, 'processing_center': 'APF', 'processing_start_time': '2025-07-30T05:38:08.924369Z', 'radar_frequency': 0.0, 'radar_sampling_freq': 0.0, 'rvl_status': 'nominal', 'statevector_acc': [0.0, 0.0, 0.0], 'statevector_pos': [5090845.987955, 1957917.965155, 4498843.204924], 'statevector_utc': '30-JUL-2025 05:03:18.318570', 'statevector_vel': [5030.729537, 24.443297, -5687.332543], 'stokes_source': 'WAVEWATCH III (R)', 'title': 'Sentinel-1 OCN RVL Product'}

      stac_discovery :

      {'assets': {}, 'bbox': [17.000872, 39.495934, 13.571783, 41.406952], 'geometry': {'coordinates': [[[16.616774, 39.495934], [13.571783, 39.906666], [13.88713, 41.406952], [17.000872, 40.997356]]], 'type': 'Polygon'}, 'id': 'SAR Standard L2 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}, 'osw', 'owi', 'rvl'], 'properties': {'processing:version': '004.00', 'product:type': 'S01SIWOCN'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json'], 'stac_version': '1.0.0', 'type': 'Feature'}

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (2)

    other_metadata :

    {'eopf_category': 'eocontainer'}

    stac_discovery :

    {'links': ['S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV'], 'properties': {'product:type': ''}}

• Dimensions:
• Coordinates: (0)
• Inherited coordinates: (0)
• Data variables: (0)
• Attributes: (2)

  other_metadata :

  {'eopf_category': 'eocontainer', 'history': [{'output': 'Downlinked Stream', 'processingTime': '2025-07-30T04:59:27.654179', 'processor': None, 'type': 'Raw Data'}, {'inputs': {'Applicable Document1': 'S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Applicable Document2': 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Raw Data': 'Downlinked Stream'}, 'output': 'Unknown', 'processingTime': '2025-07-30T05:20:55.776135', 'processor': None, 'type': 'Raw Data'}, {'output': 'Downlinked Stream', 'processingTime': '2025-07-30T04:59:13.279914', 'processor': None, 'type': 'Raw Data'}, {'inputs': {'Applicable Document1': 'S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Applicable Document2': 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Raw Data': 'Downlinked Stream'}, 'output': 'Unknown', 'processingTime': '2025-07-30T05:25:09.927028', 'processor': None, 'type': 'Raw Data'}, {'inputs': {'Applicable Document1': 'S-1 Core PDGS S-1 Level-0 Product Format Specifications S1PD.SP.00110.ASTR', 'Applicable Document2': 'Sentinel-1 SAR Space Packet Protocol Data Unit S1-IF-ASD-PL-0007', 'Raw Data1': 'Unknown', 'Raw Data2': 'Unknown'}, 'output': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_IW_RAW__0SDV_20250730T050304_20250730T050336_003445_006EB7_BD90.SAFE', 'processingCentre': 'S1C-PS, ESA', 'processingTime': '2025-07-30T05:26:58.433712', 'processor': '', 'type': 'Level-0 Product'}, {'inputs': {'AUX_CAL': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_AUX_CAL_V20250224T000000_G20250519T123501.SAFE', 'AUX_INS': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_AUX_INS_V20250224T000000_G20250519T123409.SAFE', 'AUX_PP1': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_AUX_PP1_V20250224T000000_G20250521T131402.SAFE', 'AUX_PRE': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_OPER_AUX_PREORB_OPOD_20250730T050835_V20250730T042458_20250730T105958.EOF', 'Level-0 Annotation Product': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_IW_RAW__0ADV_20250730T045354_20250730T050636_003445_006EB7_8BD9.SAFE', 'Level-0 Calibration Product': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_IW_RAW__0CDV_20250730T045354_20250730T050636_003445_006EB7_591C.SAFE', 'Level-0 Noise Product': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_IW_RAW__0NDV_20250730T045354_20250730T050636_003445_006EB7_8579.SAFE', 'Level-0 Product': '/data_pwa/cps/wd/eops-cps-pnode-s1-l2-1/IW_RAW__0_OCN__2_3_92_IW_RAW__0_OCN__2_3_92_85294577/S1C_IW_RAW__0SDV_20250730T050304_20250730T050336_003445_006EB7_BD90.SAFE', 'product definition': 'Sentinel-1 Product Definition (S1-RS-MDA-52-7440) release 2/7', 'product specification': 'Sentinel-1 Product Specification (S1-RS-MDA-52-7441) release 3/14'}, 'output': 'S1C_IW_SL2__1_DV_20250730T050306_20250730T050336_003445_006EB7_852D.SAFE', 'processingCentre': 'S1C-PS, ESA', 'processingTime': '2025-07-30T05:37:09.000000', 'processor': 'Sentinel-1 IPF', 'type': 'Level-1 Intermediate SLC Product', 'version': '003.92'}, {'inputs': {'Level-1 Intermediate SLC Product': 'S1C_IW_SL2__1_DV_20250730T050306_20250730T050336_003445_006EB7_852D.SAFE'}, 'output': 'S1C_IW_GR2__1_DV_20250730T050308_20250730T050333_003445_006EB7_490D.SAFE', 'processingCentre': 'S1C-PS, ESA', 'processingTime': '2025-07-30T05:38:04.000000', 'processor': 'Sentinel-1 IPF', 'type': 'Level-1 Intermediate GRD Product', 'version': '003.92'}, {'inputs': {'AUX_ICE': 'S1__AUX_ICE_V20250729T120000_G20250730T042821.SAFE', 'AUX_PP2': 'S1C_AUX_PP2_V20250224T000000_G20250220T125410.SAFE', 'AUX_WAV': 'S1__AUX_WAV_V20250730T060000_G20250730T043438.SAFE', 'AUX_WND': 'S1__AUX_WND_V20250730T050000_G20250729T175131.SAFE', 'Level-1 Intermediate GRD Product': 'S1C_IW_GR2__1_DV_20250730T050308_20250730T050333_003445_006EB7_490D.SAFE'}, 'output': '', 'processingCentre': 'S1C-PS, ESA', 'processingTime': '2025-07-30T05:46:47.239656', 'processor': 'Sentinel-1 IPF', 'type': 'L2', 'version': '003.92'}]}

  stac_discovery :

  {'assets': {}, 'bbox': [17.000872, 39.495934, 13.571783, 41.406952], 'geometry': {'coordinates': [[[16.616774, 39.495934], [13.571783, 39.906666], [13.88713, 41.406952], [17.000872, 40.997356]]], 'type': 'Polygon'}, 'id': 'SAR Standard L2 Product', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}, 'osw', 'owi', 'rvl'], 'properties': {'constellation': 'sentinel-1', 'created': '2025-07-30T05:46:47.239656Z', 'datetime': 'null', 'end_datetime': '2025-07-30T05:03:33.293235Z', 'eopf:datatake_id': '28343', 'eopf:instrument_mode': 'IW', 'instrument': 'sar', 'platform': 'sentinel-1c', 'processing:expression': 'systematic', 'processing:software': {'Sentinel-1 IPF': '003.92'}, 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT-3h', 'product:type': 'S01SIWOCN', 'sat:absolute_orbit': 3445, 'sat:orbit_state': 'descending', 'sat:relative_orbit': 124, 'start_datetime': '2025-07-30T05:03:08.294096Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json'], 'stac_version': '1.0.0', 'type': 'Feature'}

We can extract the ocean wind field dataset as shown below:

In [14]:

ds = dt.owi.S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV.measurements.to_dataset()
ds

ds = dt.owi.S01SIWOCN_20250730T050308_0025_C020_446E_006EB7_VV.measurements.to_dataset() ds

Out[14]:

<xarray.Dataset> Size: 704kB
Dimensions:         (azimuth: 166, range: 265)
Coordinates:
    latitude        (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
    longitude       (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
Dimensions without coordinates: azimuth, range
Data variables:
    wind_direction  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
    wind_speed      (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • azimuth: 166
  • range: 265
• Coordinates: (2)
  • latitude
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    dimensions :

    ['azimuth', 'range']

    dtype :

    <f4

    long_name :

    geodetic latitude at wind cell center

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • longitude
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    dimensions :

    ['azimuth', 'range']

    dtype :

    <f4

    long_name :

    geodetic longitude at wind cell center

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Data variables: (2)
  • wind_direction
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'SAR wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)', 'short_name': '/measurements/wind_direction', 'standard_name': 'wind_direction', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    SAR wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)

    standard_name :

    wind_direction

    units :

    degrees

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'SAR wind speed', 'short_name': '/measurements/wind_speed', 'standard_name': 'wind_speed', 'units': 'm/s'}

    dtype :

    <f4

    long_name :

    SAR wind speed

    standard_name :

    wind_speed

    units :

    m/s

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

As an example, we plot the two ocean wind field array.

In [15]:

fig, ax = plt.subplots(1, 2, figsize=(12, 5))
ds.wind_direction.plot(ax=ax[0])
ds.wind_speed.plot(ax=ax[1])
plt.tight_layout()

fig, ax = plt.subplots(1, 2, figsize=(12, 5)) ds.wind_direction.plot(ax=ax[0]) ds.wind_speed.plot(ax=ax[1]) plt.tight_layout()

Open Sentinel-1 Level-2 OCN OWI Group as Dataset¶

We can access the individual sub-groups directly by using the assets "osw", "owi", and "rvl". The opening parameters are stored in the asset’s extra field "xarray:open_dataset_kwargs".

In [16]:

ds = xr.open_dataset(
    item.assets["owi"].href,
    **item.assets["owi"].extra_fields["xarray:open_dataset_kwargs"],
)
ds

ds = xr.open_dataset( item.assets["owi"].href, **item.assets["owi"].extra_fields["xarray:open_dataset_kwargs"], ) ds

Out[16]:

<xarray.Dataset> Size: 704kB
Dimensions:         (azimuth: 166, range: 265)
Coordinates:
    latitude        (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
    longitude       (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
Dimensions without coordinates: azimuth, range
Data variables:
    wind_direction  (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>
    wind_speed      (azimuth, range) float32 176kB dask.array<chunksize=(166, 265), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • azimuth: 166
  • range: 265
• Coordinates: (2)
  • latitude
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    dimensions :

    ['azimuth', 'range']

    dtype :

    <f4

    long_name :

    geodetic latitude at wind cell center

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • longitude
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    dimensions :

    ['azimuth', 'range']

    dtype :

    <f4

    long_name :

    geodetic longitude at wind cell center

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Data variables: (2)
  • wind_direction
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'SAR wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)', 'short_name': '/measurements/wind_direction', 'standard_name': 'wind_direction', 'units': 'degrees'}

    dtype :

    <f4

    long_name :

    SAR wind direction (meteorological convention = clockwise direction from where the wind comes respect to north)

    standard_name :

    wind_direction

    units :

    degrees

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (azimuth, range)
    float32
    dask.array<chunksize=(166, 265), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['longitude', 'latitude'], 'dimensions': ['azimuth', 'range'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -999.0, 'long_name': 'SAR wind speed', 'short_name': '/measurements/wind_speed', 'standard_name': 'wind_speed', 'units': 'm/s'}

    dtype :

    <f4

    long_name :

    SAR wind speed

    standard_name :

    wind_speed

    units :

    m/s

    Array Chunk Bytes 171.84 kiB 171.84 kiB Shape (166, 265) (166, 265) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

---

Conclusion¶

This notebook demonstrates how to access Sentinel-1 EOPF Zarr samples in native mode using the xarray-eopf plugin. Key takeaways are:

• Access Sentinel-1 Level-1 GRD, and Sentinel-1 Level-2 OCN products.
• Open the full Zarr store as an xr.DataTree using xr.open_datatree and the asset "product".
• Open subgroups (e.g., "vh", "vv", "owi", and "rvl") as xr.Dataset using xr.open_dataset.
• Open the full Zarr store as a flattened xr.Dataset using xr.open_dataset and the asset "product".
• Filter variables using the variables keyword argument.

Note:
This notebook only covers the native mode, which presents the data as close as possible to the original product.
The anaylsis mode will be presented once implemented.
Examples for Sentienl-1 SLC will be presented once updated Zarr samples are published through the STAC API.