Access Sentinel-2 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-2 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-2 Level-1C¶

We begin with an example that accesses a Sentinel-2 Level-1C product in native mode.

Find a Sentinel-2 Level-1C Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-2 Level-1C tile.

In [2]:

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-2-l1c"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-2-l1c"], bbox=[7.2, 44.5, 7.4, 44.7], datetime=["2026-03-13", "2026-03-13"], ).items() ) items

Out[2]:

[<Item id=S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853>]

Next, we can inspect the item’s contents. The asset "product" links to the entire Zarr store. The additional field xarray:open_datatree_kwargs has been included in the asset "product", which provides the arguments needed to open the product using Xarray’s eopf-zarr engine.

In [3]:

item = items[0]
item

item = items[0] item

Out[3]:

<Item id=S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 13 items

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

  • 1 "https://stac-extensions.github.io/eo/v2.0.0/schema.json"

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

  • 3 "https://stac-extensions.github.io/projection/v2.0.0/schema.json"

  • 4 "https://stac-extensions.github.io/mgrs/v1.0.0/schema.json"

  • 5 "https://stac-extensions.github.io/grid/v1.1.0/schema.json"

  • 6 "https://stac-extensions.github.io/view/v1.0.0/schema.json"

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

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

  • 9 "https://stac-extensions.github.io/scientific/v1.0.0/schema.json"

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

  • 11 "https://stac-extensions.github.io/version/v1.2.0/schema.json"

  • 12 "https://stac-extensions.github.io/raster/v2.0.0/schema.json"
• id "S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 11 items

      • 0[] 2 items

        • 0 6.814260663879186

        • 1 44.14299463915654

      • 1[] 2 items

        • 0 6.862849633524148

        • 1 44.28854980816078

      • 2[] 2 items

        • 0 6.913542734971571

        • 1 44.434750348537925

      • 3[] 2 items

        • 0 6.962313790019774

        • 1 44.58155306058976

      • 4[] 2 items

        • 0 7.010955757503

        • 1 44.728499728638816

      • 5[] 2 items

        • 0 7.060405299364974

        • 1 44.875326118891536

      • 6[] 2 items

        • 0 7.107587454389481

        • 1 45.02285613715742

      • 7[] 2 items

        • 0 7.146790489526708

        • 1 45.1366650993253

      • 8[] 2 items

        • 0 7.852592618382666

        • 1 45.148073182871244

      • 9[] 2 items

        • 0 7.871918704100081

        • 1 44.15979663371134

      • 10[] 2 items

        • 0 6.814260663879186

        • 1 44.14299463915654
• bbox[] 4 items

  • 0 6.814260663879186

  • 1 44.14299463915654

  • 2 7.871918704100081

  • 3 45.148073182871244
• properties

  • gsd 10.0
  • created "2026-03-13T20:00:53.332462Z"
  • mission "Sentinel-2"
  • sci:doi "10.5270/S2_-742ikth"
  • updated "2026-03-13T20:00:53.332462Z"
  • datetime "2026-03-13T10:17:41.024000Z"
  • platform "sentinel-2a"
  • grid:code "MGRS-32TLQ"
  • proj:bbox[] 4 items

    • 0 6.814260663879186

    • 1 44.14299463915654

    • 2 7.871918704100081

    • 3 45.148073182871244
  • proj:code "EPSG:32632"
  • 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-2"
      • 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 "2026-03-13T20:00:53.332462Z"
  • deprecated False
  • instruments[] 1 items

    • 0 "msi"
  • end_datetime "2026-03-13T10:17:41.024000Z"
  • product:type "S02MSIL1C"
  • constellation "sentinel-2"
  • eo:snow_cover 24.7909244257559
  • mgrs:utm_zone 32
  • proj:centroid

    • lat 44.61398
    • lon 7.41649
  • eo:cloud_cover 13.9006004077606
  • start_datetime "2026-03-13T10:17:41.024000Z"
  • sat:orbit_state "descending"
  • eopf:datatake_id "GS2A_20260313T101741_056004_N05.12"
  • mgrs:grid_square "LQ"
  • processing:level "L1C"
  • view:sun_azimuth 156.346195853372
  • eopf:datastrip_id "S2A_OPER_MSI_L1C_DS_2APS_20260313T153853_S20260313T102456_N05.12"
  • mgrs:latitude_band "T"
  • processing:lineage "systematic"
  • processing:version "05.12"
  • product:timeliness "PT3H"
  • sat:absolute_orbit 56004
  • sat:relative_orbit 65
  • view:sun_elevation 50.1170463180154
  • processing:facility "ESA"
  • processing:software

    • EOPF-CPM "2.6.2"
  • eopf:instrument_mode "INS-NOBS"
  • product:timeliness_category "NRT"
  • sat:platform_international_designator "2015-028A"
• links[] 6 items

  • 0

    • rel "collection"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-2-l1c"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-2-l1c"
    • 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-2-l1c/items/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853"
    • type "application/geo+json"

  • 4

    • rel "cite-as"
    • href "https://doi.org/10.5270/S2_-742ikth"

  • 5

    • 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

  • SR_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r10m"
    • type "application/vnd+zarr"
    • title "Surface Reflectance - 10m"
    • gsd 10
    • bands[] 4 items

      • 0

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098

      • 1

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 2

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 3

        • name "B08"
        • description "NIR 1 (band 8)"
        • eo:common_name "nir"
        • eo:center_wavelength 0.842
        • eo:full_width_half_max 0.145
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

      • 2 "dataset"
  • SR_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m"
    • type "application/vnd+zarr"
    • title "Surface Reflectance - 20m"
    • gsd 20
    • bands[] 10 items

      • 0

        • name "B01"
        • description "Coastal aerosol (band 1)"
        • eo:common_name "coastal"
        • eo:center_wavelength 0.443
        • eo:full_width_half_max 0.027

      • 1

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098

      • 2

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 3

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 4

        • name "B05"
        • description "Red edge 1 (band 5)"
        • eo:common_name "rededge071"
        • eo:center_wavelength 0.704
        • eo:full_width_half_max 0.019

      • 5

        • name "B06"
        • description "Red edge 2 (band 6)"
        • eo:common_name "rededge075"
        • eo:center_wavelength 0.74
        • eo:full_width_half_max 0.018

      • 6

        • name "B07"
        • description "Red edge 3 (band 7)"
        • eo:common_name "rededge078"
        • eo:center_wavelength 0.783
        • eo:full_width_half_max 0.028

      • 7

        • name "B8A"
        • description "NIR 2 (band 8A)"
        • eo:common_name "nir08"
        • eo:center_wavelength 0.865
        • eo:full_width_half_max 0.033

      • 8

        • name "B11"
        • description "SWIR 1 (band 11)"
        • eo:common_name "swir16"
        • eo:center_wavelength 1.61
        • eo:full_width_half_max 0.143

      • 9

        • name "B12"
        • description "SWIR 2 (band 12)"
        • eo:common_name "swir22"
        • eo:center_wavelength 2.19
        • eo:full_width_half_max 0.242
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

      • 2 "dataset"
  • SR_60m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r60m"
    • type "application/vnd+zarr"
    • title "Surface Reflectance - 60m"
    • gsd 60
    • bands[] 11 items

      • 0

        • name "B01"
        • description "Coastal aerosol (band 1)"
        • eo:common_name "coastal"
        • eo:center_wavelength 0.443
        • eo:full_width_half_max 0.027

      • 1

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098

      • 2

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 3

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 4

        • name "B05"
        • description "Red edge 1 (band 5)"
        • eo:common_name "rededge071"
        • eo:center_wavelength 0.704
        • eo:full_width_half_max 0.019

      • 5

        • name "B06"
        • description "Red edge 2 (band 6)"
        • eo:common_name "rededge075"
        • eo:center_wavelength 0.74
        • eo:full_width_half_max 0.018

      • 6

        • name "B07"
        • description "Red edge 3 (band 7)"
        • eo:common_name "rededge078"
        • eo:center_wavelength 0.783
        • eo:full_width_half_max 0.028

      • 7

        • name "B8A"
        • description "NIR 2 (band 8A)"
        • eo:common_name "nir08"
        • eo:center_wavelength 0.865
        • eo:full_width_half_max 0.033

      • 8

        • name "B09"
        • description "NIR 3 (band 9)"
        • eo:common_name "nir09"
        • eo:center_wavelength 0.945
        • eo:full_width_half_max 0.026

      • 9

        • name "B11"
        • description "SWIR 1 (band 11)"
        • eo:common_name "swir16"
        • eo:center_wavelength 1.61
        • eo:full_width_half_max 0.143

      • 10

        • name "B12"
        • description "SWIR 2 (band 12)"
        • eo:common_name "swir22"
        • eo:center_wavelength 2.19
        • eo:full_width_half_max 0.242
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

      • 2 "dataset"
  • B01_60m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r60m/b01"
    • type "application/vnd+zarr"
    • title "Coastal aerosol (band 1) - 60m"
    • description "TOA reflectance from MSI acquisition at spectral band 01 443 nm"
    • gsd 60
    • bands[] 1 items

      • 0

        • name "B01"
        • description "Coastal aerosol (band 1)"
        • eo:common_name "coastal"
        • eo:center_wavelength 0.443
        • eo:full_width_half_max 0.027
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B02_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r10m/b02"
    • type "application/vnd+zarr"
    • title "Blue (band 2) - 10m"
    • description "TOA reflectance from MSI acquisition at spectral band 02 490 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B03_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r10m/b03"
    • type "application/vnd+zarr"
    • title "Green (band 3) - 10m"
    • description "TOA reflectance from MSI acquisition at spectral band 03 560 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B04_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r10m/b04"
    • type "application/vnd+zarr"
    • title "Red (band 4) - 10m"
    • description "TOA reflectance from MSI acquisition at spectral band 04 665 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B05_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m/b05"
    • type "application/vnd+zarr"
    • title "Red edge 1 (band 5) - 20m"
    • description "TOA reflectance from MSI acquisition at spectral band 05 705 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B05"
        • description "Red edge 1 (band 5)"
        • eo:common_name "rededge071"
        • eo:center_wavelength 0.704
        • eo:full_width_half_max 0.019
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B06_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m/b06"
    • type "application/vnd+zarr"
    • title "Red edge 2 (band 6) - 20m"
    • description "TOA reflectance from MSI acquisition at spectral band 06 740 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B06"
        • description "Red edge 2 (band 6)"
        • eo:common_name "rededge075"
        • eo:center_wavelength 0.74
        • eo:full_width_half_max 0.018
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B07_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m/b07"
    • type "application/vnd+zarr"
    • title "Red edge 3 (band 7) - 20m"
    • description "TOA reflectance from MSI acquisition at spectral band 07 783 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B07"
        • description "Red edge 3 (band 7)"
        • eo:common_name "rededge078"
        • eo:center_wavelength 0.783
        • eo:full_width_half_max 0.028
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B08_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r10m/b08"
    • type "application/vnd+zarr"
    • title "NIR 1 (band 8) - 10m"
    • description "TOA reflectance from MSI acquisition at spectral band 08 842 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B08"
        • description "NIR 1 (band 8)"
        • eo:common_name "nir"
        • eo:center_wavelength 0.842
        • eo:full_width_half_max 0.145
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B09_60m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r60m/b09"
    • type "application/vnd+zarr"
    • title "NIR 3 (band 9) - 60m"
    • description "TOA reflectance from MSI acquisition at spectral band 09 940 nm"
    • gsd 60
    • bands[] 1 items

      • 0

        • name "B09"
        • description "NIR 3 (band 9)"
        • eo:common_name "nir09"
        • eo:center_wavelength 0.945
        • eo:full_width_half_max 0.026
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B11_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m/b11"
    • type "application/vnd+zarr"
    • title "SWIR 1 (band 11) - 20m"
    • description "TOA reflectance from MSI acquisition at spectral band 11 1610 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B11"
        • description "SWIR 1 (band 11)"
        • eo:common_name "swir16"
        • eo:center_wavelength 1.61
        • eo:full_width_half_max 0.143
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B12_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m/b12"
    • type "application/vnd+zarr"
    • title "SWIR 2 (band 12) - 20m"
    • description "TOA reflectance from MSI acquisition at spectral band 12 2190 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B12"
        • description "SWIR 2 (band 12)"
        • eo:common_name "swir22"
        • eo:center_wavelength 2.19
        • eo:full_width_half_max 0.242
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B8A_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/measurements/reflectance/r20m/b8a"
    • type "application/vnd+zarr"
    • title "NIR 2 (band 8A) - 20m"
    • description "TOA reflectance from MSI acquisition at spectral band 8A 865 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B8A"
        • description "NIR 2 (band 8A)"
        • eo:common_name "nir08"
        • eo:center_wavelength 0.865
        • eo:full_width_half_max 0.033
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • TCI_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/quality/l1c_quicklook/r10m/tci"
    • type "application/vnd+zarr"
    • title "True color image"
    • gsd 10
    • bands[] 3 items

      • 0

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 1

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 2

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098
    • roles[] 1 items

      • 0 "data"
  • product

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.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"
  • zipped_product

    • href "https://download.user.eopf.eodc.eu/zip/collections/sentinel-2-l1c/items/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.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-s02msil1c-eu/13/products/cpm_v262/S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr/.zmetadata"
    • type "application/json"
    • title "Consolidated Metadata"
    • description "Consolidated metadata of the EOPF product"
    • roles[] 1 items

      • 0 "metadata"
• collection "sentinel-2-l1c"

Open Sentinel-2 Level-1C as xarray.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]:

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

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

Out[4]:

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

xarray.DataTree

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

  conditions

  • Groups: (3)
    <xarray.DatasetView> Size: 449kB
    Dimensions:                        (angle: 2, band: 13, y: 23, x: 23,
                                        detector: 4)
    Coordinates:
      * angle                          (angle) <U7 56B 'zenith' 'azimuth'
      * band                           (band) <U3 156B 'b00' 'b01' ... 'b11' 'b12'
      * detector                       (detector) <U3 48B 'd01' 'd02' 'd03' 'd04'
      * x                              (x) float32 92B 3e+05 3.05e+05 ... 4.1e+05
      * y                              (y) float32 92B 5e+06 4.995e+06 ... 4.89e+06
    Data variables:
        mean_sun_angles                (angle) float64 16B dask.array<chunksize=(2,), meta=np.ndarray>
        mean_viewing_incidence_angles  (band, angle) float64 208B dask.array<chunksize=(13, 2), meta=np.ndarray>
        sun_angles                     (angle, y, x) float64 8kB dask.array<chunksize=(2, 23, 23), meta=np.ndarray>
        viewing_incidence_angles       (band, detector, angle, y, x) float64 440kB dask.array<chunksize=(13, 4, 2, 23, 23), meta=np.ndarray>

    geometry

    • Groups: (0)
    • Dimensions:
      • angle: 2
      • band: 13
      • y: 23
      • x: 23
      • detector: 4
    • Coordinates: (5)
      • angle
        (angle)
        <U7
        'zenith' 'azimuth'

        dimensions :

        ['angle']

        long_name :

        zenith and azimuth angles

        array(['zenith', 'azimuth'], dtype='<U7')

      • band
        (band)
        <U3
        'b00' 'b01' 'b02' ... 'b11' 'b12'

        dimensions :

        ['band']

        dtype :

        <U3

        flag_meanings :

        B01 B02 B03 B04 B05 B06 B07 B08 B8A B09 B10 B11 B12

        flag_values :

        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

        long_name :

        spectral band number

        array(['b00', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07', 'b08', 'b09',
               'b10', 'b11', 'b12'], dtype='<U3')

      • detector
        (detector)
        <U3
        'd01' 'd02' 'd03' 'd04'

        dimensions :

        ['detector']

        dtype :

        <U3

        long_name :

        detector module number

        array(['d01', 'd02', 'd03', 'd04'], dtype='<U3')

      • x
        (x)
        float32
        3e+05 3.05e+05 ... 4.05e+05 4.1e+05

        dimensions :

        ['x']

        dtype :

        <f4

        long_name :

        x coordinates of angles grid, in steps of 5km from up-left pixel

        units :

        m

        array([300000., 305000., 310000., 315000., 320000., 325000., 330000., 335000.,
               340000., 345000., 350000., 355000., 360000., 365000., 370000., 375000.,
               380000., 385000., 390000., 395000., 400000., 405000., 410000.],
              dtype=float32)

      • y
        (y)
        float32
        5e+06 4.995e+06 ... 4.89e+06

        dimensions :

        ['y']

        dtype :

        <f4

        long_name :

        y coordinates of angles grid, in steps of 5km from up-left pixel

        units :

        m

        array([5000040., 4995040., 4990040., 4985040., 4980040., 4975040., 4970040.,
               4965040., 4960040., 4955040., 4950040., 4945040., 4940040., 4935040.,
               4930040., 4925040., 4920040., 4915040., 4910040., 4905040., 4900040.,
               4895040., 4890040.], dtype=float32)

    • Data variables: (4)
      • mean_sun_angles
        (angle)
        float64
        dask.array<chunksize=(2,), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle'], 'dimensions': ['angle'], 'dtype': '<f8', 'long_name': 'mean solar angles (all detectors, all bands)', 'short_name': 'mean_sun_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        mean solar angles (all detectors, all bands)

        units :

        degrees

        Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • mean_viewing_incidence_angles
        (band, angle)
        float64
        dask.array<chunksize=(13, 2), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle', 'band'], 'dimensions': ['band', 'angle'], 'dtype': '<f8', 'long_name': 'mean viewing incidence angles', 'short_name': 'mean_viewing_incidence_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        mean viewing incidence angles

        units :

        degrees

        Array Chunk Bytes 208 B 208 B Shape (13, 2) (13, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • sun_angles
        (angle, y, x)
        float64
        dask.array<chunksize=(2, 23, 23), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle', 'y', 'x'], 'dimensions': ['angle', 'y', 'x'], 'dtype': '<f8', 'long_name': 'solar angles', 'short_name': 'sun_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        solar angles

        units :

        degrees

        Array Chunk Bytes 8.27 kiB 8.27 kiB Shape (2, 23, 23) (2, 23, 23) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • viewing_incidence_angles
        (band, detector, angle, y, x)
        float64
        dask.array<chunksize=(13, 4, 2, 23, 23), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detector', 'angle', 'y', 'x', 'band'], 'dimensions': ['band', 'detector', 'angle', 'y', 'x'], 'dtype': '<f8', 'long_name': 'viewing incidence angles', 'short_name': 'viewing_incidence_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        viewing incidence angles

        units :

        degrees

        Array Chunk Bytes 429.81 kiB 429.81 kiB Shape (13, 4, 2, 23, 23) (13, 4, 2, 23, 23) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

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

    mask

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

      detector_footprint

      • Groups: (3)
        <xarray.DatasetView> Size: 482MB
        Dimensions:  (y: 10980, x: 10980)
        Coordinates:
          * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
          * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
        Data variables:
            b02      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
            b03      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
            b04      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
            b08      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

        r10m

        • Groups: (0)
        • Dimensions:
          • y: 10980
          • x: 10980
        • Coordinates: (2)
          • x
            (x)
            float32
            3e+05 3e+05 ... 4.098e+05 4.098e+05

            dimensions :

            ['x']

            dtype :

            <f4

            long_name :

            x coordinates of image at 10m in meters from up-left pixel

            units :

            m

            array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
                  shape=(10980,), dtype=float32)

          • y
            (y)
            float32
            5e+06 5e+06 ... 4.89e+06 4.89e+06

            dimensions :

            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of image at 10m in meters from up-left pixel

            units :

            m

            array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
                  shape=(10980,), dtype=float32)

        • Data variables: (4)
          • b02
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b02'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b03
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b03'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b04
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b04'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b08
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b08'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • Attributes: (0)

        <xarray.DatasetView> Size: 181MB
        Dimensions:  (y: 5490, x: 5490)
        Coordinates:
          * x        (x) float32 22kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
          * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
        Data variables:
            b05      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
            b06      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
            b07      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
            b11      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
            b12      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
            b8a      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>

        r20m

        • Groups: (0)
        • Dimensions:
          • y: 5490
          • x: 5490
        • Coordinates: (2)
          • x
            (x)
            float32
            3e+05 3e+05 ... 4.098e+05 4.098e+05

            dimensions :

            ['x']

            dtype :

            <f4

            long_name :

            x coordinates of image at 20m in meters from up-left pixel

            units :

            m

            array([300010., 300030., 300050., ..., 409750., 409770., 409790.],
                  shape=(5490,), dtype=float32)

          • y
            (y)
            float32
            5e+06 5e+06 ... 4.89e+06 4.89e+06

            dimensions :

            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of image at 20m in meters from up-left pixel

            units :

            m

            array([5000030., 5000010., 4999990., ..., 4890290., 4890270., 4890250.],
                  shape=(5490,), dtype=float32)

        • Data variables: (6)
          • b05
            (y, x)
            uint8
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b05'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b06
            (y, x)
            uint8
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b06'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b07
            (y, x)
            uint8
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b07'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b11
            (y, x)
            uint8
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b11'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b12
            (y, x)
            uint8
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b12'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b8a
            (y, x)
            uint8
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b8a'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • Attributes: (0)

        <xarray.DatasetView> Size: 10MB
        Dimensions:  (y: 1830, x: 1830)
        Coordinates:
          * x        (x) float32 7kB 3e+05 3.001e+05 3.002e+05 ... 4.097e+05 4.098e+05
          * y        (y) float32 7kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
        Data variables:
            b01      (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
            b09      (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
            b10      (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>

        r60m

        • Groups: (0)
        • Dimensions:
          • y: 1830
          • x: 1830
        • Coordinates: (2)
          • x
            (x)
            float32
            3e+05 3.001e+05 ... 4.098e+05

            dimensions :

            ['x']

            dtype :

            <f4

            long_name :

            x coordinates of image at 60m in meters from up-left pixel

            units :

            m

            array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
                  shape=(1830,), dtype=float32)

          • y
            (y)
            float32
            5e+06 5e+06 ... 4.89e+06 4.89e+06

            dimensions :

            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of image at 60m in meters from up-left pixel

            units :

            m

            array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
                  shape=(1830,), dtype=float32)

        • Data variables: (3)
          • b01
            (y, x)
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            dask.array<chunksize=(305, 305), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b01'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b09
            (y, x)
            uint8
            dask.array<chunksize=(305, 305), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b09'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b10
            (y, x)
            uint8
            dask.array<chunksize=(305, 305), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b10'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • Attributes: (0)

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

      <xarray.DatasetView> Size: 0B
      Dimensions:  ()
      Data variables:
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      l1c_classification

      • Groups: (1)
        <xarray.DatasetView> Size: 3MB
        Dimensions:  (y: 1830, x: 1830)
        Coordinates:
          * x        (x) float32 7kB 3e+05 3.001e+05 3.002e+05 ... 4.097e+05 4.098e+05
          * y        (y) float32 7kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
        Data variables:
            b00      (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>

        r60m

        • Groups: (0)
        • Dimensions:
          • y: 1830
          • x: 1830
        • Coordinates: (2)
          • x
            (x)
            float32
            3e+05 3.001e+05 ... 4.098e+05

            dimensions :

            ['x']

            dtype :

            <f4

            long_name :

            x coordinates of image at 60m in meters from up-left pixel

            units :

            m

            array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
                  shape=(1830,), dtype=float32)

          • y
            (y)
            float32
            5e+06 5e+06 ... 4.89e+06 4.89e+06

            dimensions :

            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of image at 60m in meters from up-left pixel

            units :

            m

            array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
                  shape=(1830,), dtype=float32)

        • Data variables: (1)
          • b00
            (y, x)
            uint8
            dask.array<chunksize=(305, 305), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4], 'flag_meanings': 'opaque cirrus snow_ice', 'long_name': 'cloud classification bitmask provided in the final reference frame (ground geometry)', 'short_name': 'msk_cls_b00'}

            dtype :

            <u1

            flag_masks :

            [1, 2, 4]

            flag_meanings :

            opaque cirrus snow_ice

            long_name :

            cloud classification bitmask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • Attributes: (0)

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

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

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

    meteorology

    • Groups: (2)
      <xarray.DatasetView> Size: 4kB
      Dimensions:    (latitude: 9, longitude: 9)
      Coordinates:
        * latitude   (latitude) float32 36B 45.13 45.01 44.88 ... 44.4 44.28 44.16
        * longitude  (longitude) float32 36B 6.457 6.634 6.811 ... 7.518 7.695 7.872
      Data variables:
          aod1240    (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          aod469     (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          aod550     (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          aod670     (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          aod865     (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          bcaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          duaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          omaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          ssaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          suaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          z          (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>

      cams

      • Groups: (0)
      • Dimensions:
        • latitude: 9
        • longitude: 9
      • Coordinates: (2)
        • latitude
          (latitude)
          float32
          45.13 45.01 44.88 ... 44.28 44.16

          dimensions :

          ['latitude']

          dtype :

          <f4

          long_name :

          latitudes of ECMWF grid

          standard_name :

          latitude

          units :

          degrees_north

          array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4  , 44.279, 44.16 ],
                dtype=float32)

        • longitude
          (longitude)
          float32
          6.457 6.634 6.811 ... 7.695 7.872

          dimensions :

          ['longitude']

          dtype :

          <f4

          long_name :

          longitudes of ECMWF grid

          standard_name :

          longitude

          units :

          degrees_east

          array([6.457   , 6.633875, 6.81075 , 6.987625, 7.1645  , 7.341375, 7.51825 ,
                 7.695125, 7.872   ], dtype=float32)

      • Data variables: (11)
        • aod1240
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 1240nm', 'short_name': 'aod1240'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 1240nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod469
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 469nm', 'short_name': 'aod469'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 469nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 550nm', 'short_name': 'aod550'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod670
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 670nm', 'short_name': 'aod670'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 670nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod865
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 865nm', 'short_name': 'aod865'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 865nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • bcaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'black carbon aerosol optical thickness at 550nm', 'short_name': 'bcaod550'}

          dtype :

          <f4

          long_name :

          black carbon aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • duaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'dust aerosol optical thickness at 550nm', 'short_name': 'duaod550'}

          dtype :

          <f4

          long_name :

          dust aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • omaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'organic matter aerosol optical thickness at 550nm', 'short_name': 'omaod550'}

          dtype :

          <f4

          long_name :

          organic matter aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • ssaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'sea salt aerosol optical thickness at 550nm', 'short_name': 'ssaod550'}

          dtype :

          <f4

          long_name :

          sea salt aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • suaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'sulphate aerosol optical thickness at 550nm', 'short_name': 'suaod550'}

          dtype :

          <f4

          long_name :

          sulphate aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • z
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'geopotential surface z', 'short_name': 'z', 'units': 'm2/s2'}

          dtype :

          <f4

          long_name :

          geopotential surface z

          units :

          m2/s2

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 2kB
      Dimensions:    (latitude: 9, longitude: 9)
      Coordinates:
        * latitude   (latitude) float32 36B 45.13 45.01 44.88 ... 44.4 44.28 44.16
        * longitude  (longitude) float32 36B 6.457 6.634 6.811 ... 7.518 7.695 7.872
      Data variables:
          msl        (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          r          (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          tco3       (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          tcwv       (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          u10        (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          v10        (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>

      ecmwf

      • Groups: (0)
      • Dimensions:
        • latitude: 9
        • longitude: 9
      • Coordinates: (2)
        • latitude
          (latitude)
          float32
          45.13 45.01 44.88 ... 44.28 44.16

          dimensions :

          ['latitude']

          dtype :

          <f4

          long_name :

          latitudes of ECMWF grid

          standard_name :

          latitude

          units :

          degrees_north

          array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4  , 44.279, 44.16 ],
                dtype=float32)

        • longitude
          (longitude)
          float32
          6.457 6.634 6.811 ... 7.695 7.872

          dimensions :

          ['longitude']

          dtype :

          <f4

          long_name :

          longitudes of ECMWF grid

          standard_name :

          longitude

          units :

          degrees_east

          array([6.457   , 6.633875, 6.81075 , 6.987625, 7.1645  , 7.341375, 7.51825 ,
                 7.695125, 7.872   ], dtype=float32)

      • Data variables: (6)
        • msl
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'mean sea level pressure', 'short_name': 'msl', 'standard_name': 'air_pressure_at_mean_sea_level', 'units': 'hPa'}

          dtype :

          <f4

          long_name :

          mean sea level pressure

          standard_name :

          air_pressure_at_mean_sea_level

          units :

          hPa

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • r
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'relative humidity at surface pressure', 'short_name': 'r', 'units': '%'}

          dtype :

          <f4

          long_name :

          relative humidity at surface pressure

          units :

          %

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • tco3
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total column ozone', 'short_name': 'tco3', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg/m2'}

          dtype :

          <f4

          long_name :

          total column ozone

          standard_name :

          atmosphere_mass_content_of_ozone

          units :

          kg/m2

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • tcwv
          (latitude, longitude)
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          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total column vertically-integrated water vapour', 'short_name': 'tcwv', 'standard_name': 'lwe_thickness_of_atmosphere_mass_content_of_water_vapor', 'units': 'kg/m2'}

          dtype :

          <f4

          long_name :

          total column vertically-integrated water vapour

          standard_name :

          lwe_thickness_of_atmosphere_mass_content_of_water_vapor

          units :

          kg/m2

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • u10
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': '10m eastward wind component', 'short_name': 'u10', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          10m eastward wind component

          units :

          m/s

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • v10
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': '10m northward wind component', 'short_name': 'v10', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          10m northward wind component

          units :

          m/s

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

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

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

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

  • Groups: (1)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*

    reflectance

    • Groups: (3)
      <xarray.DatasetView> Size: 4GB
      Dimensions:  (y: 10980, x: 10980)
      Coordinates:
        * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
          b02      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b03      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b04      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b08      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

      r10m

      • Groups: (0)
      • Dimensions:
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        • x: 10980
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3e+05 ... 4.098e+05 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of image at 10m in meters from up-left pixel

          units :

          m

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                shape=(10980,), dtype=float32)

        • y
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          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of image at 10m in meters from up-left pixel

          units :

          m

          array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
                shape=(10980,), dtype=float32)

      • Data variables: (4)
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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 02 490 nm

          units :

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          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b03
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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 03 560 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b04
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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 04 665 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b08
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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 08 842 nm', 'scale_factor': 0.0001, 'short_name': 'b08_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 08 842 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 1GB
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        * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
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          b06      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b07      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b11      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b12      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b8a      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

      • Groups: (0)
      • Dimensions:
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        • x: 5490
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          (x)
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          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

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          units :

          m

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        • y
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          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

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          units :

          m

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                shape=(5490,), dtype=float32)

      • Data variables: (6)
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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 05 705 nm

          units :

          digital_counts

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        • b06
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          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

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          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 06 740 nm

          units :

          digital_counts

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        • b07
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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 07 783 nm

          units :

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          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b11
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          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 11 1610 nm', 'scale_factor': 0.0001, 'short_name': 'b11_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 11 1610 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b12
          (y, x)
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          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 12 2190 nm', 'scale_factor': 0.0001, 'short_name': 'b12_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 12 2190 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b8a
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          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 8A 865 nm', 'scale_factor': 0.0001, 'short_name': 'b8a_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 8A 865 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 80MB
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      Coordinates:
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          b09      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b10      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>

      r60m

      • Groups: (0)
      • Dimensions:
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        • x: 1830
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3.001e+05 ... 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

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          m

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        • y
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          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of image at 60m in meters from up-left pixel

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      • Data variables: (3)
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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 01 443 nm', 'scale_factor': 0.0001, 'short_name': 'b01_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 01 443 nm

          units :

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          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b09
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          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 09 940 nm', 'scale_factor': 0.0001, 'short_name': 'b09_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 09 940 nm

          units :

          digital_counts

          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b10
          (y, x)
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          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 10 1375 nm', 'scale_factor': 0.0001, 'short_name': 'b10_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          TOA reflectance from MSI acquisition at spectral band 10 1375 nm

          units :

          digital_counts

          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

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

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

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

  • Groups: (1)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
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    mask

    • Groups: (3)
      <xarray.DatasetView> Size: 482MB
      Dimensions:  (y: 10980, x: 10980)
      Coordinates:
        * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
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          b03      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b04      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b08      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

      r10m

      • Groups: (0)
      • Dimensions:
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        • x: 10980
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3e+05 ... 4.098e+05 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of image at 10m in meters from up-left pixel

          units :

          m

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        • y
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          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of image at 10m in meters from up-left pixel

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      • Data variables: (4)
        • b02
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          _eopf_attrs :

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          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

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        • b03
          (y, x)
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          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b03'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b04
          (y, x)
          uint8
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'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B05', 'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B8A', 'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B06', 'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B09', 'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B04', 'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B10', 'S2A_OPER_GIP_R2L2NC_MPC__20150605T094742_V20150622T000000_21000101T000000_B12', 'S2A_OPER_GIP_R2NOMO_MPC__20250117T000005_V20250121T080000_21000101T000000_B00', 'S2A_OPER_GIP_R2PARA_MPC__20221206T000009_V20221206T073000_21000101T000000_B00', 'S2A_OPER_GIP_R2SWIR_MPC__20180406T000021_V20180604T100000_21000101T000000_B00', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B8A', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B09', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B02', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B10', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B12', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B07', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B11', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B01', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B06', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B05', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B03', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B08', 'S2A_OPER_GIP_R2WAFI_MPC__20150605T094742_V20150622T000000_21000101T000000_B04', 'S2A_OPER_GIP_RESPAR_MPC__20150605T094736_V20150622T000000_21000101T000000_B00', 'S2A_OPER_GIP_SPAMOD_MPC__20250310T090027_V20250310T120000_21000101T000000_B00', 'S2A_OPER_GIP_TILPAR_MPC__20151209T095117_V20150622T000000_21000101T000000_B00', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B8A', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131049_V20150703T000000_21000101T000000_B03', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B10', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B08', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B05', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B11', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131048_V20150703T000000_21000101T000000_B01', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131049_V20150703T000000_21000101T000000_B04', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B07', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131051_V20150703T000000_21000101T000000_B12', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B06', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131049_V20150703T000000_21000101T000000_B02', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B09'], 'facility': 'ESA', 'inputs': 'S02MSIL1B_etc', 'outputs': ['S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.SAFE'], 'processor': 'Sentinel-2 IPF', 'time': '2026-03-13T15:38:53.000000Z', 'version': '05.12'}, {'facility': 'CS-SopraSteria', 'inputs': ['S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.SAFE'], 'outputs': ['S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr.zarr'], 'processor': 'EOPF-CPM', 'time': '2026-03-13T19:59:51.696657Z', 'version': '2.6.2'}]}

  stac_discovery :

  {'assets': {'aod1240': {'href': '/conditions/meteorology/cams/aod1240', 'title': 'aod1240'}, 'aod469': {'href': '/conditions/meteorology/cams/aod469', 'title': 'aod469'}, 'aod550': {'href': '/conditions/meteorology/cams/aod550', 'title': 'aod550'}, 'aod670': {'href': '/conditions/meteorology/cams/aod670', 'title': 'aod670'}, 'aod865': {'href': '/conditions/meteorology/cams/aod865', 'title': 'aod865'}, 'b01_60m': {'href': '/measurements/reflectance/r60m/b01', 'title': 'b01_60m'}, 'b02_10m': {'href': '/measurements/reflectance/r10m/b02', 'title': 'b02_10m'}, 'b03_10m': {'href': '/measurements/reflectance/r10m/b03', 'title': 'b03_10m'}, 'b04_10m': {'href': '/measurements/reflectance/r10m/b04', 'title': 'b04_10m'}, 'b05_20m': {'href': '/measurements/reflectance/r20m/b05', 'title': 'b05_20m'}, 'b06_20m': {'href': '/measurements/reflectance/r20m/b06', 'title': 'b06_20m'}, 'b07_20m': {'href': '/measurements/reflectance/r20m/b07', 'title': 'b07_20m'}, 'b08_10m': {'href': '/measurements/reflectance/r10m/b08', 'title': 'b08_10m'}, 'b09_r60m': {'href': '/measurements/reflectance/r60m/b09', 'title': 'b09_r60m'}, 'b10_60m': {'href': '/measurements/reflectance/r60m/b10', 'title': 'b10_60m'}, 'b11_20m': {'href': '/measurements/reflectance/r20m/b11', 'title': 'b11_20m'}, 'b12_20m': {'href': '/measurements/reflectance/r20m/b12', 'title': 'b12_20m'}, 'b8a_20m': {'href': '/measurements/reflectance/r20m/b8a', 'title': 'b8a_20m'}, 'bcaod550': {'href': '/conditions/meteorology/cams/bcaod550', 'title': 'bcaod550'}, 'duaod550': {'href': '/conditions/meteorology/cams/duaod550', 'title': 'duaod550'}, 'mean_sun_angles': {'href': '/conditions/geometry/mean_sun_angles', 'title': 'mean_sun_angles'}, 'mean_viewing_incidence_angles': {'href': '/conditions/geometry/mean_viewing_incidence_angles', 'title': 'mean_viewing_incidence_angles'}, 'msk_cls_b00': {'href': '/conditions/mask/l1c_classification/r60m/b00', 'title': 'msk_cls_b00'}, 'msk_detfoo_b01': {'href': '/conditions/mask/detector_footprint/r60m/b01', 'title': 'msk_detfoo_b01'}, 'msk_detfoo_b02': {'href': '/conditions/mask/detector_footprint/r10m/b02', 'title': 'msk_detfoo_b02'}, 'msk_detfoo_b03': {'href': '/conditions/mask/detector_footprint/r10m/b03', 'title': 'msk_detfoo_b03'}, 'msk_detfoo_b04': {'href': '/conditions/mask/detector_footprint/r10m/b04', 'title': 'msk_detfoo_b04'}, 'msk_detfoo_b05': {'href': '/conditions/mask/detector_footprint/r20m/b05', 'title': 'msk_detfoo_b05'}, 'msk_detfoo_b06': {'href': '/conditions/mask/detector_footprint/r20m/b06', 'title': 'msk_detfoo_b06'}, 'msk_detfoo_b07': {'href': '/conditions/mask/detector_footprint/r20m/b07', 'title': 'msk_detfoo_b07'}, 'msk_detfoo_b08': {'href': '/conditions/mask/detector_footprint/r10m/b08', 'title': 'msk_detfoo_b08'}, 'msk_detfoo_b09': {'href': '/conditions/mask/detector_footprint/r60m/b09', 'title': 'msk_detfoo_b09'}, 'msk_detfoo_b10': {'href': '/conditions/mask/detector_footprint/r60m/b10', 'title': 'msk_detfoo_b10'}, 'msk_detfoo_b11': {'href': '/conditions/mask/detector_footprint/r20m/b11', 'title': 'msk_detfoo_b11'}, 'msk_detfoo_b12': {'href': '/conditions/mask/detector_footprint/r20m/b12', 'title': 'msk_detfoo_b12'}, 'msk_detfoo_b8a': {'href': '/conditions/mask/detector_footprint/r20m/b8a', 'title': 'msk_detfoo_b8a'}, 'msk_quality_b01': {'href': '/quality/mask/r60m/b01', 'title': 'msk_quality_b01'}, 'msk_quality_b02': {'href': '/quality/mask/r10m/b02', 'title': 'msk_quality_b02'}, 'msk_quality_b03': {'href': '/quality/mask/r10m/b03', 'title': 'msk_quality_b03'}, 'msk_quality_b04': {'href': '/quality/mask/r10m/b04', 'title': 'msk_quality_b04'}, 'msk_quality_b05': {'href': '/quality/mask/r20m/b05', 'title': 'msk_quality_b05'}, 'msk_quality_b06': {'href': '/quality/mask/r20m/b06', 'title': 'msk_quality_b06'}, 'msk_quality_b07': {'href': '/quality/mask/r20m/b07', 'title': 'msk_quality_b07'}, 'msk_quality_b08': {'href': '/quality/mask/r10m/b08', 'title': 'msk_quality_b08'}, 'msk_quality_b09': {'href': '/quality/mask/r60m/b09', 'title': 'msk_quality_b09'}, 'msk_quality_b10': {'href': '/quality/mask/r60m/b10', 'title': 'msk_quality_b10'}, 'msk_quality_b11': {'href': '/quality/mask/r20m/b11', 'title': 'msk_quality_b11'}, 'msk_quality_b12': {'href': '/quality/mask/r20m/b12', 'title': 'msk_quality_b12'}, 'msk_quality_b8a': {'href': '/quality/mask/r20m/b8a', 'title': 'msk_quality_b8a'}, 'msl': {'href': '/conditions/meteorology/ecmwf/msl', 'title': 'msl'}, 'omaod550': {'href': '/conditions/meteorology/cams/omaod550', 'title': 'omaod550'}, 'r': {'href': '/conditions/meteorology/ecmwf/r', 'title': 'r'}, 'ssaod550': {'href': '/conditions/meteorology/cams/ssaod550', 'title': 'ssaod550'}, 'suaod550': {'href': '/conditions/meteorology/cams/suaod550', 'title': 'suaod550'}, 'sun_angles': {'href': '/conditions/geometry/sun_angles', 'title': 'sun_angles'}, 'tco3': {'href': '/conditions/meteorology/ecmwf/tco3', 'title': 'tco3'}, 'tcwv': {'href': '/conditions/meteorology/ecmwf/tcwv', 'title': 'tcwv'}, 'u10': {'href': '/conditions/meteorology/ecmwf/u10', 'title': 'u10'}, 'v10': {'href': '/conditions/meteorology/ecmwf/v10', 'title': 'v10'}, 'viewing_incidence_angles': {'href': '/conditions/geometry/viewing_incidence_angles', 'title': 'viewing_incidence_angles'}, 'z': {'href': '/conditions/meteorology/cams/z', 'title': 'z'}}, 'bbox': [7.871918704100081, 44.14299463915654, 6.814260663879186, 45.148073182871244], 'geometry': {'coordinates': [[[6.814260663879186, 44.14299463915654], [6.862849633524148, 44.28854980816078], [6.913542734971571, 44.434750348537925], [6.962313790019774, 44.58155306058976], [7.010955757503, 44.728499728638816], [7.060405299364974, 44.875326118891536], [7.107587454389481, 45.02285613715742], [7.146790489526708, 45.1366650993253], [7.852592618382666, 45.148073182871244], [7.871918704100081, 44.15979663371134], [6.814260663879186, 44.14299463915654]]], 'type': 'Polygon'}, 'id': 'S02MSIL1C_20260313T101741_0000_A065_T003', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4427, 'eo:common_name': 'coastal', 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1884.69, 'name': '01', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 0.49269999999999997, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.065, 'eo:solar_illumination': 1959.66, 'name': '02', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.5598, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.035, 'eo:solar_illumination': 1823.24, 'name': '03', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.6646000000000001, 'eo:common_name': 'red', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 1512.06, 'name': '04', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.7041000000000001, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1424.64, 'name': '05', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7405, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1287.61, 'name': '06', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7827999999999999, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1162.08, 'name': '07', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.8328, 'eo:common_name': 'nir', 'eo:full_width_half_max': 0.105, 'eo:solar_illumination': 1041.63, 'name': '08', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.8647, 'eo:full_width_half_max': 0.02, 'name': '8A', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.9451, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 812.92, 'name': '09', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.3735, 'eo:common_name': 'cirrus', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 367.15, 'name': '10', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.6137000000000001, 'eo:common_name': 'swir16', 'eo:full_width_half_max': 0.09, 'eo:solar_illumination': 245.59, 'name': '11', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 2.2024000000000004, 'eo:common_name': 'swir22', 'eo:full_width_half_max': 0.18, 'name': '12', 'raster:spatial_resolution': '20'}], 'constellation': 'sentinel-2', 'created': '2026-03-13T15:38:53.000000Z', 'datetime': '2026-03-13T10:17:41.024000Z', 'end_datetime': '2026-03-13T10:17:41.024Z', 'eo:cloud_cover': 13.9006004077606, 'eo:snow_cover': 24.7909244257559, 'eopf:datastrip_id': 'S2A_OPER_MSI_L1C_DS_2APS_20260313T153853_S20260313T102456_N05.12', 'eopf:datatake_id': 'GS2A_20260313T101741_056004_N05.12', 'eopf:instrument_mode': 'INS-NOBS', 'gsd': '10', 'instruments': ['msi'], 'platform': 'sentinel-2a', 'processing:facility': 'ESA', 'processing:level': 'L1C', 'processing:lineage': 'systematic', 'processing:version': '05.12', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT', 'product:type': 'S02MSIL1C', 'providers': [{'name': 'L1C processor', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 56004, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2015-028A', 'sat:relative_orbit': 65, 'sci:doi': '10.5270/S2_-742ikth', 'start_datetime': '2026-03-13T10:17:41.024Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/raster/v2.0.0/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/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

As an example, we plot the spectral band 9 (b09) at 60 meters resolution, which will trigger loading and visualization of the data. Additionally, we will plot the viewing and solar zenith angle on the side.

In [5]:

fig, ax = plt.subplots(1, 3, figsize=(15, 5))
dt.measurements.reflectance.r60m.b09.plot.imshow(ax=ax[0], vmin=0, vmax=0.5)
dt.conditions.geometry.sun_angles.sel(angle="zenith").plot.imshow(ax=ax[1])
dt.conditions.geometry.viewing_incidence_angles.sel(angle="zenith", band="b09").mean(dim="detector").plot.imshow(ax=ax[2])
plt.tight_layout()

fig, ax = plt.subplots(1, 3, figsize=(15, 5)) dt.measurements.reflectance.r60m.b09.plot.imshow(ax=ax[0], vmin=0, vmax=0.5) dt.conditions.geometry.sun_angles.sel(angle="zenith").plot.imshow(ax=ax[1]) dt.conditions.geometry.viewing_incidence_angles.sel(angle="zenith", band="b09").mean(dim="detector").plot.imshow(ax=ax[2]) plt.tight_layout()

Open Sentinel-2 Level-1C Reflectance Groups as xarray.Dataset¶

Similarly, we can open the individual reflectance groups at 10 m (SR_10m), 20 m (SR_20m), and 60 m (SR_60m) resolution as xarray.Dataset objects.

The opening parameters are stored in the asset’s extra field "xarray:open_dataset_kwargs". Note that when opening a group, only the bands available at that resolution (e.g., 10 m) are included.

In [6]:

ds = xr.open_dataset(
    item.assets["SR_10m"].href,
    **item.assets["SR_10m"].extra_fields["xarray:open_dataset_kwargs"],
)
ds

ds = xr.open_dataset( item.assets["SR_10m"].href, **item.assets["SR_10m"].extra_fields["xarray:open_dataset_kwargs"], ) ds

Out[6]:

<xarray.Dataset> Size: 4GB
Dimensions:  (y: 10980, x: 10980)
Coordinates:
  * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
  * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
Data variables:
    b02      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b03      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b04      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b08      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • y: 10980
  • x: 10980
• Coordinates: (2)
  • x
    (x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
          shape=(10980,), dtype=float32)

  • y
    (y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
          shape=(10980,), dtype=float32)

• Data variables: (4)
  • b02
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b03
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b04
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b08
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 08 842 nm', 'scale_factor': 0.0001, 'short_name': 'b08_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 08 842 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([300005.0, 300015.0, 300025.0, 300035.0, 300045.0, 300055.0, 300065.0,
           300075.0, 300085.0, 300095.0,
           ...
           409705.0, 409715.0, 409725.0, 409735.0, 409745.0, 409755.0, 409765.0,
           409775.0, 409785.0, 409795.0],
          dtype='float32', name='x', length=10980))

  • y
    PandasIndex

    PandasIndex(Index([5000035.0, 5000025.0, 5000015.0, 5000005.0, 4999995.0, 4999985.0,
           4999975.0, 4999965.0, 4999955.0, 4999945.0,
           ...
           4890335.0, 4890325.0, 4890315.0, 4890305.0, 4890295.0, 4890285.0,
           4890275.0, 4890265.0, 4890255.0, 4890245.0],
          dtype='float32', name='y', length=10980))

• Attributes: (0)

We can also filter the varaibles by band names as shown below:

In [7]:

ds = xr.open_dataset(
    item.assets["SR_10m"].href,
    **item.assets["SR_10m"].extra_fields["xarray:open_dataset_kwargs"],
    variables="b0[234]",
)
ds

ds = xr.open_dataset( item.assets["SR_10m"].href, **item.assets["SR_10m"].extra_fields["xarray:open_dataset_kwargs"], variables="b0[234]", ) ds

Out[7]:

<xarray.Dataset> Size: 3GB
Dimensions:  (y: 10980, x: 10980)
Coordinates:
  * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
  * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
Data variables:
    b02      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b03      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b04      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • y: 10980
  • x: 10980
• Coordinates: (2)
  • x
    (x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
          shape=(10980,), dtype=float32)

  • y
    (y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
          shape=(10980,), dtype=float32)

• Data variables: (3)
  • b02
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b03
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b04
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([300005.0, 300015.0, 300025.0, 300035.0, 300045.0, 300055.0, 300065.0,
           300075.0, 300085.0, 300095.0,
           ...
           409705.0, 409715.0, 409725.0, 409735.0, 409745.0, 409755.0, 409765.0,
           409775.0, 409785.0, 409795.0],
          dtype='float32', name='x', length=10980))

  • y
    PandasIndex

    PandasIndex(Index([5000035.0, 5000025.0, 5000015.0, 5000005.0, 4999995.0, 4999985.0,
           4999975.0, 4999965.0, 4999955.0, 4999945.0,
           ...
           4890335.0, 4890325.0, 4890315.0, 4890305.0, 4890295.0, 4890285.0,
           4890275.0, 4890265.0, 4890255.0, 4890245.0],
          dtype='float32', name='y', length=10980))

• Attributes: (0)

And we can plot a cutout of the RGB image, which triggers loading the data.

In [8]:

array = ds[["b04", "b03", "b02"]].to_dataarray(dim="band")
array = array.isel(x=slice(-1830, None), y=slice(-1830, None))
array

array = ds[["b04", "b03", "b02"]].to_dataarray(dim="band") array = array.isel(x=slice(-1830, None), y=slice(-1830, None)) array

Out[8]:

<xarray.DataArray (band: 3, y: 1830, x: 1830)> Size: 80MB
dask.array<getitem, shape=(3, 1830, 1830), dtype=float64, chunksize=(1, 1830, 1830), chunktype=numpy.ndarray>
Coordinates:
  * x        (x) float32 7kB 3.915e+05 3.915e+05 ... 4.098e+05 4.098e+05
  * y        (y) float32 7kB 4.909e+06 4.909e+06 4.909e+06 ... 4.89e+06 4.89e+06
  * band     (band) object 24B 'b04' 'b03' 'b02'

xarray.DataArray

• band: 3
• y: 1830
• x: 1830

• dask.array<chunksize=(1, 1830, 1830), meta=np.ndarray>

  Array Chunk Bytes 76.65 MiB 25.55 MiB Shape (3, 1830, 1830) (1, 1830, 1830) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

• Coordinates: (3)
  • x
    (x)
    float32
    3.915e+05 3.915e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([391505., 391515., 391525., ..., 409775., 409785., 409795.],
          shape=(1830,), dtype=float32)

  • y
    (y)
    float32
    4.909e+06 4.909e+06 ... 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([4908535., 4908525., 4908515., ..., 4890265., 4890255., 4890245.],
          shape=(1830,), dtype=float32)

  • band
    (band)
    object
    'b04' 'b03' 'b02'

    array(['b04', 'b03', 'b02'], dtype=object)

• Indexes: (3)
  • x
    PandasIndex

    PandasIndex(Index([391505.0, 391515.0, 391525.0, 391535.0, 391545.0, 391555.0, 391565.0,
           391575.0, 391585.0, 391595.0,
           ...
           409705.0, 409715.0, 409725.0, 409735.0, 409745.0, 409755.0, 409765.0,
           409775.0, 409785.0, 409795.0],
          dtype='float32', name='x', length=1830))

  • y
    PandasIndex

    PandasIndex(Index([4908535.0, 4908525.0, 4908515.0, 4908505.0, 4908495.0, 4908485.0,
           4908475.0, 4908465.0, 4908455.0, 4908445.0,
           ...
           4890335.0, 4890325.0, 4890315.0, 4890305.0, 4890295.0, 4890285.0,
           4890275.0, 4890265.0, 4890255.0, 4890245.0],
          dtype='float32', name='y', length=1830))

  • band
    PandasIndex

    PandasIndex(Index(['b04', 'b03', 'b02'], dtype='object', name='band'))

• Attributes: (0)

In [9]:

ax = (array / 0.3).clip(0, 1).plot.imshow(rgb="band")
ax.axes.set_aspect("equal")

ax = (array / 0.3).clip(0, 1).plot.imshow(rgb="band") ax.axes.set_aspect("equal")

Open Sentinel-2 Level-1C as xarray.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, measurements/reflectance/r10m/b02 becomes measurements_reflectance_r10m_b02.

In [10]:

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

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

Out[10]:

<xarray.Dataset> Size: 7GB
Dimensions:                                            (
                                                        conditions_geometry_angle: 2,
                                                        conditions_geometry_band: 13,
                                                        conditions_geometry_y: 23,
                                                        conditions_geometry_x: 23,
                                                        conditions_geometry_detector: 4,
                                                        ...
                                                        quality_r10m_y: 10980,
                                                        quality_r10m_x: 10980,
                                                        quality_r20m_y: 5490,
                                                        quality_r20m_x: 5490,
                                                        quality_r60m_y: 1830,
                                                        quality_r60m_x: 1830)
Coordinates: (12/29)
  * conditions_geometry_angle                          (conditions_geometry_angle) <U7 56B ...
  * conditions_geometry_band                           (conditions_geometry_band) <U3 156B ...
  * conditions_geometry_detector                       (conditions_geometry_detector) <U3 48B ...
  * conditions_geometry_x                              (conditions_geometry_x) float32 92B ...
  * conditions_geometry_y                              (conditions_geometry_y) float32 92B ...
  * conditions_mask_detector_footprint_r10m_x          (conditions_mask_detector_footprint_r10m_x) float32 44kB ...
    ...                                                 ...
  * quality_r10m_x                                     (quality_r10m_x) float32 44kB ...
  * quality_r10m_y                                     (quality_r10m_y) float32 44kB ...
  * quality_r20m_x                                     (quality_r20m_x) float32 22kB ...
  * quality_r20m_y                                     (quality_r20m_y) float32 22kB ...
  * quality_r60m_x                                     (quality_r60m_x) float32 7kB ...
  * quality_r60m_y                                     (quality_r60m_y) float32 7kB ...
Data variables: (12/61)
    conditions_geometry_mean_sun_angles                (conditions_geometry_angle) float64 16B dask.array<chunksize=(2,), meta=np.ndarray>
    conditions_geometry_mean_viewing_incidence_angles  (conditions_geometry_band, conditions_geometry_angle) float64 208B dask.array<chunksize=(13, 2), meta=np.ndarray>
    conditions_geometry_sun_angles                     (conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x) float64 8kB dask.array<chunksize=(2, 23, 23), meta=np.ndarray>
    conditions_geometry_viewing_incidence_angles       (conditions_geometry_band, conditions_geometry_detector, conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x) float64 440kB dask.array<chunksize=(13, 4, 2, 23, 23), meta=np.ndarray>
    conditions_mask_detector_footprint_r10m_b02        (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    conditions_mask_detector_footprint_r10m_b03        (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    ...                                                 ...
    quality_r20m_b11                                   (quality_r20m_y, quality_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
    quality_r20m_b12                                   (quality_r20m_y, quality_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
    quality_r20m_b8a                                   (quality_r20m_y, quality_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
    quality_r60m_b01                                   (quality_r60m_y, quality_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    quality_r60m_b09                                   (quality_r60m_y, quality_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    quality_r60m_b10                                   (quality_r60m_y, quality_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
Attributes: (3)

xarray.Dataset

• Dimensions:
  • conditions_geometry_angle: 2
  • conditions_geometry_band: 13
  • conditions_geometry_y: 23
  • conditions_geometry_x: 23
  • conditions_geometry_detector: 4
  • conditions_mask_detector_footprint_r10m_y: 10980
  • conditions_mask_detector_footprint_r10m_x: 10980
  • conditions_mask_detector_footprint_r20m_y: 5490
  • conditions_mask_detector_footprint_r20m_x: 5490
  • conditions_mask_detector_footprint_r60m_y: 1830
  • conditions_mask_detector_footprint_r60m_x: 1830
  • conditions_mask_l1c_classification_y: 1830
  • conditions_mask_l1c_classification_x: 1830
  • conditions_meteorology_cams_latitude: 9
  • conditions_meteorology_cams_longitude: 9
  • conditions_meteorology_ecmwf_latitude: 9
  • conditions_meteorology_ecmwf_longitude: 9
  • measurements_r10m_y: 10980
  • measurements_r10m_x: 10980
  • measurements_r20m_y: 5490
  • measurements_r20m_x: 5490
  • measurements_r60m_y: 1830
  • measurements_r60m_x: 1830
  • quality_r10m_y: 10980
  • quality_r10m_x: 10980
  • quality_r20m_y: 5490
  • quality_r20m_x: 5490
  • quality_r60m_y: 1830
  • quality_r60m_x: 1830
• Coordinates: (29)
  • conditions_geometry_angle
    (conditions_geometry_angle)
    <U7
    'zenith' 'azimuth'

    dimensions :

    ['angle']

    long_name :

    zenith and azimuth angles

    array(['zenith', 'azimuth'], dtype='<U7')

  • conditions_geometry_band
    (conditions_geometry_band)
    <U3
    'b00' 'b01' 'b02' ... 'b11' 'b12'

    dimensions :

    ['band']

    dtype :

    <U3

    flag_meanings :

    B01 B02 B03 B04 B05 B06 B07 B08 B8A B09 B10 B11 B12

    flag_values :

    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    spectral band number

    array(['b00', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07', 'b08', 'b09',
           'b10', 'b11', 'b12'], dtype='<U3')

  • conditions_geometry_detector
    (conditions_geometry_detector)
    <U3
    'd01' 'd02' 'd03' 'd04'

    dimensions :

    ['detector']

    dtype :

    <U3

    long_name :

    detector module number

    array(['d01', 'd02', 'd03', 'd04'], dtype='<U3')

  • conditions_geometry_x
    (conditions_geometry_x)
    float32
    3e+05 3.05e+05 ... 4.05e+05 4.1e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of angles grid, in steps of 5km from up-left pixel

    units :

    m

    array([300000., 305000., 310000., 315000., 320000., 325000., 330000., 335000.,
           340000., 345000., 350000., 355000., 360000., 365000., 370000., 375000.,
           380000., 385000., 390000., 395000., 400000., 405000., 410000.],
          dtype=float32)

  • conditions_geometry_y
    (conditions_geometry_y)
    float32
    5e+06 4.995e+06 ... 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of angles grid, in steps of 5km from up-left pixel

    units :

    m

    array([5000040., 4995040., 4990040., 4985040., 4980040., 4975040., 4970040.,
           4965040., 4960040., 4955040., 4950040., 4945040., 4940040., 4935040.,
           4930040., 4925040., 4920040., 4915040., 4910040., 4905040., 4900040.,
           4895040., 4890040.], dtype=float32)

  • conditions_mask_detector_footprint_r10m_x
    (conditions_mask_detector_footprint_r10m_x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
          shape=(10980,), dtype=float32)

  • conditions_mask_detector_footprint_r10m_y
    (conditions_mask_detector_footprint_r10m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
          shape=(10980,), dtype=float32)

  • conditions_mask_detector_footprint_r20m_x
    (conditions_mask_detector_footprint_r20m_x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 20m in meters from up-left pixel

    units :

    m

    array([300010., 300030., 300050., ..., 409750., 409770., 409790.],
          shape=(5490,), dtype=float32)

  • conditions_mask_detector_footprint_r20m_y
    (conditions_mask_detector_footprint_r20m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 20m in meters from up-left pixel

    units :

    m

    array([5000030., 5000010., 4999990., ..., 4890290., 4890270., 4890250.],
          shape=(5490,), dtype=float32)

  • conditions_mask_detector_footprint_r60m_x
    (conditions_mask_detector_footprint_r60m_x)
    float32
    3e+05 3.001e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
          shape=(1830,), dtype=float32)

  • conditions_mask_detector_footprint_r60m_y
    (conditions_mask_detector_footprint_r60m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
          shape=(1830,), dtype=float32)

  • conditions_mask_l1c_classification_x
    (conditions_mask_l1c_classification_x)
    float32
    3e+05 3.001e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
          shape=(1830,), dtype=float32)

  • conditions_mask_l1c_classification_y
    (conditions_mask_l1c_classification_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
          shape=(1830,), dtype=float32)

  • conditions_meteorology_cams_latitude
    (conditions_meteorology_cams_latitude)
    float32
    45.13 45.01 44.88 ... 44.28 44.16

    dimensions :

    ['latitude']

    dtype :

    <f4

    long_name :

    latitudes of ECMWF grid

    standard_name :

    latitude

    units :

    degrees_north

    array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4  , 44.279, 44.16 ],
          dtype=float32)

  • conditions_meteorology_cams_longitude
    (conditions_meteorology_cams_longitude)
    float32
    6.457 6.634 6.811 ... 7.695 7.872

    dimensions :

    ['longitude']

    dtype :

    <f4

    long_name :

    longitudes of ECMWF grid

    standard_name :

    longitude

    units :

    degrees_east

    array([6.457   , 6.633875, 6.81075 , 6.987625, 7.1645  , 7.341375, 7.51825 ,
           7.695125, 7.872   ], dtype=float32)

  • conditions_meteorology_ecmwf_latitude
    (conditions_meteorology_ecmwf_latitude)
    float32
    45.13 45.01 44.88 ... 44.28 44.16

    dimensions :

    ['latitude']

    dtype :

    <f4

    long_name :

    latitudes of ECMWF grid

    standard_name :

    latitude

    units :

    degrees_north

    array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4  , 44.279, 44.16 ],
          dtype=float32)

  • conditions_meteorology_ecmwf_longitude
    (conditions_meteorology_ecmwf_longitude)
    float32
    6.457 6.634 6.811 ... 7.695 7.872

    dimensions :

    ['longitude']

    dtype :

    <f4

    long_name :

    longitudes of ECMWF grid

    standard_name :

    longitude

    units :

    degrees_east

    array([6.457   , 6.633875, 6.81075 , 6.987625, 7.1645  , 7.341375, 7.51825 ,
           7.695125, 7.872   ], dtype=float32)

  • measurements_r10m_x
    (measurements_r10m_x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
          shape=(10980,), dtype=float32)

  • measurements_r10m_y
    (measurements_r10m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
          shape=(10980,), dtype=float32)

  • measurements_r20m_x
    (measurements_r20m_x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 20m in meters from up-left pixel

    units :

    m

    array([300010., 300030., 300050., ..., 409750., 409770., 409790.],
          shape=(5490,), dtype=float32)

  • measurements_r20m_y
    (measurements_r20m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 20m in meters from up-left pixel

    units :

    m

    array([5000030., 5000010., 4999990., ..., 4890290., 4890270., 4890250.],
          shape=(5490,), dtype=float32)

  • measurements_r60m_x
    (measurements_r60m_x)
    float32
    3e+05 3.001e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
          shape=(1830,), dtype=float32)

  • measurements_r60m_y
    (measurements_r60m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
          shape=(1830,), dtype=float32)

  • quality_r10m_x
    (quality_r10m_x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
          shape=(10980,), dtype=float32)

  • quality_r10m_y
    (quality_r10m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 10m in meters from up-left pixel

    units :

    m

    array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
          shape=(10980,), dtype=float32)

  • quality_r20m_x
    (quality_r20m_x)
    float32
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 20m in meters from up-left pixel

    units :

    m

    array([300010., 300030., 300050., ..., 409750., 409770., 409790.],
          shape=(5490,), dtype=float32)

  • quality_r20m_y
    (quality_r20m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 20m in meters from up-left pixel

    units :

    m

    array([5000030., 5000010., 4999990., ..., 4890290., 4890270., 4890250.],
          shape=(5490,), dtype=float32)

  • quality_r60m_x
    (quality_r60m_x)
    float32
    3e+05 3.001e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
          shape=(1830,), dtype=float32)

  • quality_r60m_y
    (quality_r60m_y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
          shape=(1830,), dtype=float32)

• Data variables: (61)
  • conditions_geometry_mean_sun_angles
    (conditions_geometry_angle)
    float64
    dask.array<chunksize=(2,), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['angle'], 'dimensions': ['angle'], 'dtype': '<f8', 'long_name': 'mean solar angles (all detectors, all bands)', 'short_name': 'mean_sun_angles', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    mean solar angles (all detectors, all bands)

    units :

    degrees

    Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_mean_viewing_incidence_angles
    (conditions_geometry_band, conditions_geometry_angle)
    float64
    dask.array<chunksize=(13, 2), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['angle', 'band'], 'dimensions': ['band', 'angle'], 'dtype': '<f8', 'long_name': 'mean viewing incidence angles', 'short_name': 'mean_viewing_incidence_angles', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    mean viewing incidence angles

    units :

    degrees

    Array Chunk Bytes 208 B 208 B Shape (13, 2) (13, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_sun_angles
    (conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x)
    float64
    dask.array<chunksize=(2, 23, 23), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['angle', 'y', 'x'], 'dimensions': ['angle', 'y', 'x'], 'dtype': '<f8', 'long_name': 'solar angles', 'short_name': 'sun_angles', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    solar angles

    units :

    degrees

    Array Chunk Bytes 8.27 kiB 8.27 kiB Shape (2, 23, 23) (2, 23, 23) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_viewing_incidence_angles
    (conditions_geometry_band, conditions_geometry_detector, conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x)
    float64
    dask.array<chunksize=(13, 4, 2, 23, 23), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['detector', 'angle', 'y', 'x', 'band'], 'dimensions': ['band', 'detector', 'angle', 'y', 'x'], 'dtype': '<f8', 'long_name': 'viewing incidence angles', 'short_name': 'viewing_incidence_angles', 'units': 'degrees'}

    dtype :

    <f8

    long_name :

    viewing incidence angles

    units :

    degrees

    Array Chunk Bytes 429.81 kiB 429.81 kiB Shape (13, 4, 2, 23, 23) (13, 4, 2, 23, 23) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_mask_detector_footprint_r10m_b02
    (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b02'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r10m_b03
    (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b03'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r10m_b04
    (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b04'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r10m_b08
    (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b08'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r20m_b05
    (conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b05'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r20m_b06
    (conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b06'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r20m_b07
    (conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b07'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r20m_b11
    (conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b11'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r20m_b12
    (conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b12'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r20m_b8a
    (conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b8a'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r60m_b01
    (conditions_mask_detector_footprint_r60m_y, conditions_mask_detector_footprint_r60m_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b01'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r60m_b09
    (conditions_mask_detector_footprint_r60m_y, conditions_mask_detector_footprint_r60m_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b09'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_detector_footprint_r60m_b10
    (conditions_mask_detector_footprint_r60m_y, conditions_mask_detector_footprint_r60m_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b10'}

    dtype :

    <u1

    flag_meanings :

    D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

    flag_values :

    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

    long_name :

    detector footprint mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_mask_l1c_classification_b00
    (conditions_mask_l1c_classification_y, conditions_mask_l1c_classification_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4], 'flag_meanings': 'opaque cirrus snow_ice', 'long_name': 'cloud classification bitmask provided in the final reference frame (ground geometry)', 'short_name': 'msk_cls_b00'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4]

    flag_meanings :

    opaque cirrus snow_ice

    long_name :

    cloud classification bitmask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • conditions_meteorology_cams_aod1240
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 1240nm', 'short_name': 'aod1240'}

    dtype :

    <f4

    long_name :

    total aerosol optical thickness at 1240nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_aod469
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 469nm', 'short_name': 'aod469'}

    dtype :

    <f4

    long_name :

    total aerosol optical thickness at 469nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_aod550
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 550nm', 'short_name': 'aod550'}

    dtype :

    <f4

    long_name :

    total aerosol optical thickness at 550nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_aod670
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 670nm', 'short_name': 'aod670'}

    dtype :

    <f4

    long_name :

    total aerosol optical thickness at 670nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_aod865
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 865nm', 'short_name': 'aod865'}

    dtype :

    <f4

    long_name :

    total aerosol optical thickness at 865nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_bcaod550
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'black carbon aerosol optical thickness at 550nm', 'short_name': 'bcaod550'}

    dtype :

    <f4

    long_name :

    black carbon aerosol optical thickness at 550nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_duaod550
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'dust aerosol optical thickness at 550nm', 'short_name': 'duaod550'}

    dtype :

    <f4

    long_name :

    dust aerosol optical thickness at 550nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_omaod550
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'organic matter aerosol optical thickness at 550nm', 'short_name': 'omaod550'}

    dtype :

    <f4

    long_name :

    organic matter aerosol optical thickness at 550nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_ssaod550
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'sea salt aerosol optical thickness at 550nm', 'short_name': 'ssaod550'}

    dtype :

    <f4

    long_name :

    sea salt aerosol optical thickness at 550nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_suaod550
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'sulphate aerosol optical thickness at 550nm', 'short_name': 'suaod550'}

    dtype :

    <f4

    long_name :

    sulphate aerosol optical thickness at 550nm

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_cams_z
    (conditions_meteorology_cams_latitude, conditions_meteorology_cams_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'geopotential surface z', 'short_name': 'z', 'units': 'm2/s2'}

    dtype :

    <f4

    long_name :

    geopotential surface z

    units :

    m2/s2

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_ecmwf_msl
    (conditions_meteorology_ecmwf_latitude, conditions_meteorology_ecmwf_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'mean sea level pressure', 'short_name': 'msl', 'standard_name': 'air_pressure_at_mean_sea_level', 'units': 'hPa'}

    dtype :

    <f4

    long_name :

    mean sea level pressure

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    hPa

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_ecmwf_r
    (conditions_meteorology_ecmwf_latitude, conditions_meteorology_ecmwf_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'relative humidity at surface pressure', 'short_name': 'r', 'units': '%'}

    dtype :

    <f4

    long_name :

    relative humidity at surface pressure

    units :

    %

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_ecmwf_tco3
    (conditions_meteorology_ecmwf_latitude, conditions_meteorology_ecmwf_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total column ozone', 'short_name': 'tco3', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg/m2'}

    dtype :

    <f4

    long_name :

    total column ozone

    standard_name :

    atmosphere_mass_content_of_ozone

    units :

    kg/m2

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_ecmwf_tcwv
    (conditions_meteorology_ecmwf_latitude, conditions_meteorology_ecmwf_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total column vertically-integrated water vapour', 'short_name': 'tcwv', 'standard_name': 'lwe_thickness_of_atmosphere_mass_content_of_water_vapor', 'units': 'kg/m2'}

    dtype :

    <f4

    long_name :

    total column vertically-integrated water vapour

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    kg/m2

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_ecmwf_u10
    (conditions_meteorology_ecmwf_latitude, conditions_meteorology_ecmwf_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': '10m eastward wind component', 'short_name': 'u10', 'units': 'm/s'}

    dtype :

    <f4

    long_name :

    10m eastward wind component

    units :

    m/s

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_ecmwf_v10
    (conditions_meteorology_ecmwf_latitude, conditions_meteorology_ecmwf_longitude)
    float32
    dask.array<chunksize=(9, 9), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': '10m northward wind component', 'short_name': 'v10', 'units': 'm/s'}

    dtype :

    <f4

    long_name :

    10m northward wind component

    units :

    m/s

    Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • measurements_r10m_b02
    (measurements_r10m_y, measurements_r10m_x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r10m_b03
    (measurements_r10m_y, measurements_r10m_x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r10m_b04
    (measurements_r10m_y, measurements_r10m_x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r10m_b08
    (measurements_r10m_y, measurements_r10m_x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 08 842 nm', 'scale_factor': 0.0001, 'short_name': 'b08_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 08 842 nm

    units :

    digital_counts

    Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r20m_b05
    (measurements_r20m_y, measurements_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 05 705 nm', 'scale_factor': 0.0001, 'short_name': 'b05_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 05 705 nm

    units :

    digital_counts

    Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r20m_b06
    (measurements_r20m_y, measurements_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 06 740 nm', 'scale_factor': 0.0001, 'short_name': 'b06_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 06 740 nm

    units :

    digital_counts

    Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r20m_b07
    (measurements_r20m_y, measurements_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 07 783 nm', 'scale_factor': 0.0001, 'short_name': 'b07_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 07 783 nm

    units :

    digital_counts

    Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r20m_b11
    (measurements_r20m_y, measurements_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 11 1610 nm', 'scale_factor': 0.0001, 'short_name': 'b11_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 11 1610 nm

    units :

    digital_counts

    Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r20m_b12
    (measurements_r20m_y, measurements_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 12 2190 nm', 'scale_factor': 0.0001, 'short_name': 'b12_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 12 2190 nm

    units :

    digital_counts

    Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r20m_b8a
    (measurements_r20m_y, measurements_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 8A 865 nm', 'scale_factor': 0.0001, 'short_name': 'b8a_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 8A 865 nm

    units :

    digital_counts

    Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r60m_b01
    (measurements_r60m_y, measurements_r60m_x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 01 443 nm', 'scale_factor': 0.0001, 'short_name': 'b01_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 01 443 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r60m_b09
    (measurements_r60m_y, measurements_r60m_x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 09 940 nm', 'scale_factor': 0.0001, 'short_name': 'b09_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 09 940 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_r60m_b10
    (measurements_r60m_y, measurements_r60m_x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 10 1375 nm', 'scale_factor': 0.0001, 'short_name': 'b10_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 10 1375 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_r10m_b02
    (quality_r10m_y, quality_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b02'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r10m_b03
    (quality_r10m_y, quality_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b03'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r10m_b04
    (quality_r10m_y, quality_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b04'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r10m_b08
    (quality_r10m_y, quality_r10m_x)
    uint8
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b08'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r20m_b05
    (quality_r20m_y, quality_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b05'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r20m_b06
    (quality_r20m_y, quality_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b06'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r20m_b07
    (quality_r20m_y, quality_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b07'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r20m_b11
    (quality_r20m_y, quality_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b11'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r20m_b12
    (quality_r20m_y, quality_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b12'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r20m_b8a
    (quality_r20m_y, quality_r20m_x)
    uint8
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b8a'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r60m_b01
    (quality_r60m_y, quality_r60m_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b01'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r60m_b09
    (quality_r60m_y, quality_r60m_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b09'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • quality_r60m_b10
    (quality_r60m_y, quality_r60m_x)
    uint8
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b10'}

    dtype :

    <u1

    flag_masks :

    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

    flag_meanings :

    ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

    long_name :

    quality mask provided in the final reference frame (ground geometry)

    Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

• Indexes: (29)
  • conditions_geometry_angle
    PandasIndex

    PandasIndex(Index(['zenith', 'azimuth'], dtype='object', name='conditions_geometry_angle'))

  • conditions_geometry_band
    PandasIndex

    PandasIndex(Index(['b00', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07', 'b08', 'b09',
           'b10', 'b11', 'b12'],
          dtype='object', name='conditions_geometry_band'))

  • conditions_geometry_detector
    PandasIndex

    PandasIndex(Index(['d01', 'd02', 'd03', 'd04'], dtype='object', name='conditions_geometry_detector'))

  • conditions_geometry_x
    PandasIndex

    PandasIndex(Index([300000.0, 305000.0, 310000.0, 315000.0, 320000.0, 325000.0, 330000.0,
           335000.0, 340000.0, 345000.0, 350000.0, 355000.0, 360000.0, 365000.0,
           370000.0, 375000.0, 380000.0, 385000.0, 390000.0, 395000.0, 400000.0,
           405000.0, 410000.0],
          dtype='float32', name='conditions_geometry_x'))

  • conditions_geometry_y
    PandasIndex

    PandasIndex(Index([5000040.0, 4995040.0, 4990040.0, 4985040.0, 4980040.0, 4975040.0,
           4970040.0, 4965040.0, 4960040.0, 4955040.0, 4950040.0, 4945040.0,
           4940040.0, 4935040.0, 4930040.0, 4925040.0, 4920040.0, 4915040.0,
           4910040.0, 4905040.0, 4900040.0, 4895040.0, 4890040.0],
          dtype='float32', name='conditions_geometry_y'))

  • conditions_mask_detector_footprint_r10m_x
    PandasIndex

    PandasIndex(Index([300005.0, 300015.0, 300025.0, 300035.0, 300045.0, 300055.0, 300065.0,
           300075.0, 300085.0, 300095.0,
           ...
           409705.0, 409715.0, 409725.0, 409735.0, 409745.0, 409755.0, 409765.0,
           409775.0, 409785.0, 409795.0],
          dtype='float32', name='conditions_mask_detector_footprint_r10m_x', length=10980))

  • conditions_mask_detector_footprint_r10m_y
    PandasIndex

    PandasIndex(Index([5000035.0, 5000025.0, 5000015.0, 5000005.0, 4999995.0, 4999985.0,
           4999975.0, 4999965.0, 4999955.0, 4999945.0,
           ...
           4890335.0, 4890325.0, 4890315.0, 4890305.0, 4890295.0, 4890285.0,
           4890275.0, 4890265.0, 4890255.0, 4890245.0],
          dtype='float32', name='conditions_mask_detector_footprint_r10m_y', length=10980))

  • conditions_mask_detector_footprint_r20m_x
    PandasIndex

    PandasIndex(Index([300010.0, 300030.0, 300050.0, 300070.0, 300090.0, 300110.0, 300130.0,
           300150.0, 300170.0, 300190.0,
           ...
           409610.0, 409630.0, 409650.0, 409670.0, 409690.0, 409710.0, 409730.0,
           409750.0, 409770.0, 409790.0],
          dtype='float32', name='conditions_mask_detector_footprint_r20m_x', length=5490))

  • conditions_mask_detector_footprint_r20m_y
    PandasIndex

    PandasIndex(Index([5000030.0, 5000010.0, 4999990.0, 4999970.0, 4999950.0, 4999930.0,
           4999910.0, 4999890.0, 4999870.0, 4999850.0,
           ...
           4890430.0, 4890410.0, 4890390.0, 4890370.0, 4890350.0, 4890330.0,
           4890310.0, 4890290.0, 4890270.0, 4890250.0],
          dtype='float32', name='conditions_mask_detector_footprint_r20m_y', length=5490))

  • conditions_mask_detector_footprint_r60m_x
    PandasIndex

    PandasIndex(Index([300030.0, 300090.0, 300150.0, 300210.0, 300270.0, 300330.0, 300390.0,
           300450.0, 300510.0, 300570.0,
           ...
           409230.0, 409290.0, 409350.0, 409410.0, 409470.0, 409530.0, 409590.0,
           409650.0, 409710.0, 409770.0],
          dtype='float32', name='conditions_mask_detector_footprint_r60m_x', length=1830))

  • conditions_mask_detector_footprint_r60m_y
    PandasIndex

    PandasIndex(Index([5000010.0, 4999950.0, 4999890.0, 4999830.0, 4999770.0, 4999710.0,
           4999650.0, 4999590.0, 4999530.0, 4999470.0,
           ...
           4890810.0, 4890750.0, 4890690.0, 4890630.0, 4890570.0, 4890510.0,
           4890450.0, 4890390.0, 4890330.0, 4890270.0],
          dtype='float32', name='conditions_mask_detector_footprint_r60m_y', length=1830))

  • conditions_mask_l1c_classification_x
    PandasIndex

    PandasIndex(Index([300030.0, 300090.0, 300150.0, 300210.0, 300270.0, 300330.0, 300390.0,
           300450.0, 300510.0, 300570.0,
           ...
           409230.0, 409290.0, 409350.0, 409410.0, 409470.0, 409530.0, 409590.0,
           409650.0, 409710.0, 409770.0],
          dtype='float32', name='conditions_mask_l1c_classification_x', length=1830))

  • conditions_mask_l1c_classification_y
    PandasIndex

    PandasIndex(Index([5000010.0, 4999950.0, 4999890.0, 4999830.0, 4999770.0, 4999710.0,
           4999650.0, 4999590.0, 4999530.0, 4999470.0,
           ...
           4890810.0, 4890750.0, 4890690.0, 4890630.0, 4890570.0, 4890510.0,
           4890450.0, 4890390.0, 4890330.0, 4890270.0],
          dtype='float32', name='conditions_mask_l1c_classification_y', length=1830))

  • conditions_meteorology_cams_latitude
    PandasIndex

    PandasIndex(Index([45.125999450683594, 45.005001068115234,  44.88399887084961,
            44.76300048828125, 44.641998291015625, 44.520999908447266,
           44.400001525878906,  44.27899932861328,  44.15999984741211],
          dtype='float32', name='conditions_meteorology_cams_latitude'))

  • conditions_meteorology_cams_longitude
    PandasIndex

    PandasIndex(Index([ 6.456999778747559,  6.633874893188477, 6.8107500076293945,
           6.9876251220703125, 7.1645002365112305,   7.34137487411499,
            7.518249988555908,  7.695125102996826,  7.872000217437744],
          dtype='float32', name='conditions_meteorology_cams_longitude'))

  • conditions_meteorology_ecmwf_latitude
    PandasIndex

    PandasIndex(Index([45.125999450683594, 45.005001068115234,  44.88399887084961,
            44.76300048828125, 44.641998291015625, 44.520999908447266,
           44.400001525878906,  44.27899932861328,  44.15999984741211],
          dtype='float32', name='conditions_meteorology_ecmwf_latitude'))

  • conditions_meteorology_ecmwf_longitude
    PandasIndex

    PandasIndex(Index([ 6.456999778747559,  6.633874893188477, 6.8107500076293945,
           6.9876251220703125, 7.1645002365112305,   7.34137487411499,
            7.518249988555908,  7.695125102996826,  7.872000217437744],
          dtype='float32', name='conditions_meteorology_ecmwf_longitude'))

  • measurements_r10m_x
    PandasIndex

    PandasIndex(Index([300005.0, 300015.0, 300025.0, 300035.0, 300045.0, 300055.0, 300065.0,
           300075.0, 300085.0, 300095.0,
           ...
           409705.0, 409715.0, 409725.0, 409735.0, 409745.0, 409755.0, 409765.0,
           409775.0, 409785.0, 409795.0],
          dtype='float32', name='measurements_r10m_x', length=10980))

  • measurements_r10m_y
    PandasIndex

    PandasIndex(Index([5000035.0, 5000025.0, 5000015.0, 5000005.0, 4999995.0, 4999985.0,
           4999975.0, 4999965.0, 4999955.0, 4999945.0,
           ...
           4890335.0, 4890325.0, 4890315.0, 4890305.0, 4890295.0, 4890285.0,
           4890275.0, 4890265.0, 4890255.0, 4890245.0],
          dtype='float32', name='measurements_r10m_y', length=10980))

  • measurements_r20m_x
    PandasIndex

    PandasIndex(Index([300010.0, 300030.0, 300050.0, 300070.0, 300090.0, 300110.0, 300130.0,
           300150.0, 300170.0, 300190.0,
           ...
           409610.0, 409630.0, 409650.0, 409670.0, 409690.0, 409710.0, 409730.0,
           409750.0, 409770.0, 409790.0],
          dtype='float32', name='measurements_r20m_x', length=5490))

  • measurements_r20m_y
    PandasIndex

    PandasIndex(Index([5000030.0, 5000010.0, 4999990.0, 4999970.0, 4999950.0, 4999930.0,
           4999910.0, 4999890.0, 4999870.0, 4999850.0,
           ...
           4890430.0, 4890410.0, 4890390.0, 4890370.0, 4890350.0, 4890330.0,
           4890310.0, 4890290.0, 4890270.0, 4890250.0],
          dtype='float32', name='measurements_r20m_y', length=5490))

  • measurements_r60m_x
    PandasIndex

    PandasIndex(Index([300030.0, 300090.0, 300150.0, 300210.0, 300270.0, 300330.0, 300390.0,
           300450.0, 300510.0, 300570.0,
           ...
           409230.0, 409290.0, 409350.0, 409410.0, 409470.0, 409530.0, 409590.0,
           409650.0, 409710.0, 409770.0],
          dtype='float32', name='measurements_r60m_x', length=1830))

  • measurements_r60m_y
    PandasIndex

    PandasIndex(Index([5000010.0, 4999950.0, 4999890.0, 4999830.0, 4999770.0, 4999710.0,
           4999650.0, 4999590.0, 4999530.0, 4999470.0,
           ...
           4890810.0, 4890750.0, 4890690.0, 4890630.0, 4890570.0, 4890510.0,
           4890450.0, 4890390.0, 4890330.0, 4890270.0],
          dtype='float32', name='measurements_r60m_y', length=1830))

  • quality_r10m_x
    PandasIndex

    PandasIndex(Index([300005.0, 300015.0, 300025.0, 300035.0, 300045.0, 300055.0, 300065.0,
           300075.0, 300085.0, 300095.0,
           ...
           409705.0, 409715.0, 409725.0, 409735.0, 409745.0, 409755.0, 409765.0,
           409775.0, 409785.0, 409795.0],
          dtype='float32', name='quality_r10m_x', length=10980))

  • quality_r10m_y
    PandasIndex

    PandasIndex(Index([5000035.0, 5000025.0, 5000015.0, 5000005.0, 4999995.0, 4999985.0,
           4999975.0, 4999965.0, 4999955.0, 4999945.0,
           ...
           4890335.0, 4890325.0, 4890315.0, 4890305.0, 4890295.0, 4890285.0,
           4890275.0, 4890265.0, 4890255.0, 4890245.0],
          dtype='float32', name='quality_r10m_y', length=10980))

  • quality_r20m_x
    PandasIndex

    PandasIndex(Index([300010.0, 300030.0, 300050.0, 300070.0, 300090.0, 300110.0, 300130.0,
           300150.0, 300170.0, 300190.0,
           ...
           409610.0, 409630.0, 409650.0, 409670.0, 409690.0, 409710.0, 409730.0,
           409750.0, 409770.0, 409790.0],
          dtype='float32', name='quality_r20m_x', length=5490))

  • quality_r20m_y
    PandasIndex

    PandasIndex(Index([5000030.0, 5000010.0, 4999990.0, 4999970.0, 4999950.0, 4999930.0,
           4999910.0, 4999890.0, 4999870.0, 4999850.0,
           ...
           4890430.0, 4890410.0, 4890390.0, 4890370.0, 4890350.0, 4890330.0,
           4890310.0, 4890290.0, 4890270.0, 4890250.0],
          dtype='float32', name='quality_r20m_y', length=5490))

  • quality_r60m_x
    PandasIndex

    PandasIndex(Index([300030.0, 300090.0, 300150.0, 300210.0, 300270.0, 300330.0, 300390.0,
           300450.0, 300510.0, 300570.0,
           ...
           409230.0, 409290.0, 409350.0, 409410.0, 409470.0, 409530.0, 409590.0,
           409650.0, 409710.0, 409770.0],
          dtype='float32', name='quality_r60m_x', length=1830))

  • quality_r60m_y
    PandasIndex

    PandasIndex(Index([5000010.0, 4999950.0, 4999890.0, 4999830.0, 4999770.0, 4999710.0,
           4999650.0, 4999590.0, 4999530.0, 4999470.0,
           ...
           4890810.0, 4890750.0, 4890690.0, 4890630.0, 4890570.0, 4890510.0,
           4890450.0, 4890390.0, 4890330.0, 4890270.0],
          dtype='float32', name='quality_r60m_y', length=1830))

• Attributes: (3)

  other_metadata :

  {'L0_ancillary_data_quality': 'null', 'L0_ephemeris_data_quality': 'null', 'NUC_table_ID': 3, 'SWIR_rearrangement_flag': 'null', 'UTM_zone_identification': 'UTM zone 32N', 'absolute_location_assessment_from_AOCS': 'null', 'band_description': {'01': {'bandwidth': '20.0', 'central_wavelength': 442.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.4473767, 'physical_gain': 4.0887333, 'spectral_response_step': 1, 'spectral_response_values': '0.001775742 0.004073061 0.003626143 0.003515199 0.005729163 0.003780292 0.002636732 0.001262113 0.001987583 0.001368913 0.001250444 0.000463454 0.000814293 0.001376431 0.001485086 0.001823735 0.001626817 0.004392062 0.029008099 0.11874593 0.32387506 0.57281921 0.71472749 0.76196778 0.78929702 0.80862387 0.81089382 0.82419876 0.85415811 0.87079088 0.88731097 0.92619924 0.98228149 1 0.9752382 0.93596338 0.88997148 0.85021048 0.82569453 0.78390239 0.61417422 0.33007109 0.12410831 0.04365694 0.014749595', 'units': 'nm', 'wavelength_max': 456.0, 'wavelength_min': 412.0}, '02': {'bandwidth': '65.0', 'central_wavelength': 492.7, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2821506, 'physical_gain': 3.74715511, 'spectral_response_step': 1, 'spectral_response_values': '0.04255531 0.0722983 0.15374322 0.32799225 0.55336788 0.71011166 0.75285179 0.75232691 0.75668081 0.76326948 0.76239425 0.7852515 0.81546669 0.86179176 0.89282599 0.9195221 0.91900649 0.91315754 0.90035366 0.88989693 0.8823246 0.87606118 0.88429987 0.90695544 0.93232085 0.93947252 0.94383543 0.92204086 0.8860231 0.84743609 0.81251687 0.7823971 0.7731087 0.77209054 0.78742652 0.81217177 0.84605052 0.88767996 0.92793997 0.95069235 0.96573311 0.96938253 0.96570294 0.95832003 0.95405064 0.95178268 0.95699722 0.96556515 0.9770514 0.97709574 0.97436606 0.95903183 0.93506318 0.90190134 0.87165792 0.84402444 0.82280852 0.81536043 0.82057639 0.8395149 0.86992171 0.91526205 0.96067028 0.99163699 1 0.98356097 0.91130763 0.74018256 0.50395858 0.3050155 0.18004605 0.10738342 0.06593592 0.04207746 0.02662129 0.0143396 0.00265779 0.00081822', 'units': 'nm', 'wavelength_max': 533.0, 'wavelength_min': 456.0}, '03': {'bandwidth': '35.0', 'central_wavelength': 559.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3230393, 'physical_gain': 4.18026291, 'spectral_response_step': 1, 'spectral_response_values': '0.01448181 0.03422251 0.07346335 0.15444843 0.31661425 0.55322279 0.74859406 0.84890306 0.89772216 0.9215368 0.92572845 0.91122688 0.88818924 0.86523756 0.84718187 0.8387572 0.84459081 0.86219653 0.88838714 0.92443236 0.96017974 0.98685516 1 0.9986008 0.98076472 0.94522089 0.8981778 0.85580323 0.81841734 0.78862048 0.76460653 0.74963745 0.75055111 0.76137888 0.78244479 0.79890086 0.81016957 0.81408886 0.77358596 0.62881065 0.40397555 0.21542098 0.10715281 0.04792877 0.01848693 0.00108588', 'units': 'nm', 'wavelength_max': 583.0, 'wavelength_min': 538.0}, '04': {'bandwidth': '30.0', 'central_wavelength': 664.6, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3872929, 'physical_gain': 4.51634367, 'spectral_response_step': 1, 'spectral_response_values': '0.00141521 0.02590238 0.11651178 0.39088616 0.74959342 0.94485805 0.98011173 0.99406309 1 0.99545475 0.99052772 0.97733476 0.94055988 0.87894956 0.81629384 0.77345952 0.75448766 0.75991531 0.7826343 0.8101689 0.83612975 0.86125424 0.88609106 0.91138767 0.93405146 0.95042063 0.9592573 0.96039555 0.95913395 0.95809013 0.95527459 0.94376465 0.89490799 0.74426308 0.476777 0.22960399 0.08009118 0.02617076 0.00415242', 'units': 'nm', 'wavelength_max': 684.0, 'wavelength_min': 646.0}, '05': {'bandwidth': '15.0', 'central_wavelength': 704.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.844058, 'physical_gain': 5.20124492, 'spectral_response_step': 1, 'spectral_response_values': '0.02835786 0.12369337 0.39378774 0.76113071 0.97108502 0.99889523 1 0.99412258 0.98321789 0.96704093 0.94847389 0.92714833 0.90372458 0.88614713 0.86723745 0.79075319 0.58840332 0.26334833 0.05675422 0.00618833', 'units': 'nm', 'wavelength_max': 714.0, 'wavelength_min': 695.0}, '06': {'bandwidth': '13.0', 'central_wavelength': 740.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7251472, 'physical_gain': 4.87027032, 'spectral_response_step': 1, 'spectral_response_values': '0.00171088 0.05467153 0.25806676 0.64722098 0.89218999 0.90232877 0.91508768 0.94115846 0.96299993 0.97510481 0.9770217 0.98736251 1 0.98880277 0.97179916 0.90126739 0.60672391 0.20520227 0.0267569', 'units': 'nm', 'wavelength_max': 749.0, 'wavelength_min': 731.0}, '07': {'bandwidth': '19.0', 'central_wavelength': 782.8, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7489293, 'physical_gain': 4.53413059, 'spectral_response_step': 1, 'spectral_response_values': '0.00045899 0.0117201 0.05219715 0.16561733 0.36903355 0.63685453 0.86119638 0.97002897 0.99119602 0.99897921 1 0.97725155 0.92572385 0.86605804 0.81969611 0.79407674 0.79111029 0.80431552 0.81902721 0.82571292 0.82011829 0.79222195 0.72054559 0.58767794 0.41430355 0.23088817 0.09850282 0.02736551 0.00516235', 'units': 'nm', 'wavelength_max': 797.0, 'wavelength_min': 769.0}, '08': {'bandwidth': '105.0', 'central_wavelength': 832.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2704681, 'physical_gain': 6.16307432, 'spectral_response_step': 1, 'spectral_response_values': '0.00067259 0.00388856 0 0 0 0 0 0 0 0 0 0 0 0.00028956 0.00702964 0.01752391 0.03231111 0.05328661 0.08299885 0.12748502 0.19591065 0.30246323 0.43553954 0.57141637 0.69766701 0.80303852 0.89115744 0.95284584 0.98894161 1 0.98840653 0.96389216 0.94207967 0.93694643 0.94227343 0.95395718 0.96828896 0.97966549 0.9854444 0.98592681 0.98391181 0.97793903 0.97722771 0.97810609 0.98144486 0.98764558 0.98857708 0.9862422 0.98070921 0.97078624 0.95721089 0.93865821 0.91672388 0.89620759 0.872888 0.85160331 0.8246394 0.80078117 0.7823386 0.76360274 0.74962771 0.7387221 0.73079407 0.72271237 0.72507708 0.72563856 0.72304217 0.72229211 0.71616364 0.71159446 0.70826954 0.70157205 0.69924532 0.70093762 0.70692733 0.71824001 0.73124634 0.7484061 0.76818541 0.78394807 0.7968381 0.80260206 0.8045194 0.80240918 0.79699072 0.78920304 0.77691621 0.76518406 0.75119717 0.73700357 0.72262399 0.70412578 0.68410805 0.66474528 0.64736891 0.63005125 0.61564222 0.60249557 0.58988992 0.57993399 0.57136506 0.56094242 0.55235105 0.54568236 0.53958052 0.53510215 0.53093675 0.53016508 0.52984662 0.53036682 0.53211463 0.53271918 0.53246806 0.53331158 0.5319278 0.53051055 0.52951499 0.52996848 0.53253373 0.53705085 0.54235344 0.54912497 0.55523055 0.56011135 0.55767999 0.54821984 0.53144613 0.50763528 0.47811224 0.45092793 0.42798466 0.41051405 0.40039139 0.40087302 0.40829375 0.42086556 0.43007022 0.42456692 0.39136817 0.33009008 0.25720509 0.18189031 0.11650668 0.07031579 0.04275381 0.02593154 0.01574394 0.00394326', 'units': 'nm', 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'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B06', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131049_V20150703T000000_21000101T000000_B02', 'S2A_OPER_GIP_VIEDIR_MPC__20151117T131050_V20150703T000000_21000101T000000_B09'], 'facility': 'ESA', 'inputs': 'S02MSIL1B_etc', 'outputs': ['S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.SAFE'], 'processor': 'Sentinel-2 IPF', 'time': '2026-03-13T15:38:53.000000Z', 'version': '05.12'}, {'facility': 'CS-SopraSteria', 'inputs': ['S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.SAFE'], 'outputs': ['S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853.zarr.zarr'], 'processor': 'EOPF-CPM', 'time': '2026-03-13T19:59:51.696657Z', 'version': '2.6.2'}]}

  stac_discovery :

  {'assets': {'aod1240': {'href': '/conditions/meteorology/cams/aod1240', 'title': 'aod1240'}, 'aod469': {'href': '/conditions/meteorology/cams/aod469', 'title': 'aod469'}, 'aod550': {'href': '/conditions/meteorology/cams/aod550', 'title': 'aod550'}, 'aod670': {'href': '/conditions/meteorology/cams/aod670', 'title': 'aod670'}, 'aod865': {'href': '/conditions/meteorology/cams/aod865', 'title': 'aod865'}, 'b01_60m': {'href': '/measurements/reflectance/r60m/b01', 'title': 'b01_60m'}, 'b02_10m': {'href': '/measurements/reflectance/r10m/b02', 'title': 'b02_10m'}, 'b03_10m': {'href': '/measurements/reflectance/r10m/b03', 'title': 'b03_10m'}, 'b04_10m': {'href': '/measurements/reflectance/r10m/b04', 'title': 'b04_10m'}, 'b05_20m': {'href': '/measurements/reflectance/r20m/b05', 'title': 'b05_20m'}, 'b06_20m': {'href': '/measurements/reflectance/r20m/b06', 'title': 'b06_20m'}, 'b07_20m': {'href': '/measurements/reflectance/r20m/b07', 'title': 'b07_20m'}, 'b08_10m': {'href': '/measurements/reflectance/r10m/b08', 'title': 'b08_10m'}, 'b09_r60m': {'href': '/measurements/reflectance/r60m/b09', 'title': 'b09_r60m'}, 'b10_60m': {'href': '/measurements/reflectance/r60m/b10', 'title': 'b10_60m'}, 'b11_20m': {'href': '/measurements/reflectance/r20m/b11', 'title': 'b11_20m'}, 'b12_20m': {'href': '/measurements/reflectance/r20m/b12', 'title': 'b12_20m'}, 'b8a_20m': {'href': '/measurements/reflectance/r20m/b8a', 'title': 'b8a_20m'}, 'bcaod550': {'href': '/conditions/meteorology/cams/bcaod550', 'title': 'bcaod550'}, 'duaod550': {'href': '/conditions/meteorology/cams/duaod550', 'title': 'duaod550'}, 'mean_sun_angles': {'href': '/conditions/geometry/mean_sun_angles', 'title': 'mean_sun_angles'}, 'mean_viewing_incidence_angles': {'href': '/conditions/geometry/mean_viewing_incidence_angles', 'title': 'mean_viewing_incidence_angles'}, 'msk_cls_b00': {'href': '/conditions/mask/l1c_classification/r60m/b00', 'title': 'msk_cls_b00'}, 'msk_detfoo_b01': {'href': '/conditions/mask/detector_footprint/r60m/b01', 'title': 'msk_detfoo_b01'}, 'msk_detfoo_b02': {'href': '/conditions/mask/detector_footprint/r10m/b02', 'title': 'msk_detfoo_b02'}, 'msk_detfoo_b03': {'href': '/conditions/mask/detector_footprint/r10m/b03', 'title': 'msk_detfoo_b03'}, 'msk_detfoo_b04': {'href': '/conditions/mask/detector_footprint/r10m/b04', 'title': 'msk_detfoo_b04'}, 'msk_detfoo_b05': {'href': '/conditions/mask/detector_footprint/r20m/b05', 'title': 'msk_detfoo_b05'}, 'msk_detfoo_b06': {'href': '/conditions/mask/detector_footprint/r20m/b06', 'title': 'msk_detfoo_b06'}, 'msk_detfoo_b07': {'href': '/conditions/mask/detector_footprint/r20m/b07', 'title': 'msk_detfoo_b07'}, 'msk_detfoo_b08': {'href': '/conditions/mask/detector_footprint/r10m/b08', 'title': 'msk_detfoo_b08'}, 'msk_detfoo_b09': {'href': '/conditions/mask/detector_footprint/r60m/b09', 'title': 'msk_detfoo_b09'}, 'msk_detfoo_b10': {'href': '/conditions/mask/detector_footprint/r60m/b10', 'title': 'msk_detfoo_b10'}, 'msk_detfoo_b11': {'href': '/conditions/mask/detector_footprint/r20m/b11', 'title': 'msk_detfoo_b11'}, 'msk_detfoo_b12': {'href': '/conditions/mask/detector_footprint/r20m/b12', 'title': 'msk_detfoo_b12'}, 'msk_detfoo_b8a': {'href': '/conditions/mask/detector_footprint/r20m/b8a', 'title': 'msk_detfoo_b8a'}, 'msk_quality_b01': {'href': '/quality/mask/r60m/b01', 'title': 'msk_quality_b01'}, 'msk_quality_b02': {'href': '/quality/mask/r10m/b02', 'title': 'msk_quality_b02'}, 'msk_quality_b03': {'href': '/quality/mask/r10m/b03', 'title': 'msk_quality_b03'}, 'msk_quality_b04': {'href': '/quality/mask/r10m/b04', 'title': 'msk_quality_b04'}, 'msk_quality_b05': {'href': '/quality/mask/r20m/b05', 'title': 'msk_quality_b05'}, 'msk_quality_b06': {'href': '/quality/mask/r20m/b06', 'title': 'msk_quality_b06'}, 'msk_quality_b07': {'href': '/quality/mask/r20m/b07', 'title': 'msk_quality_b07'}, 'msk_quality_b08': {'href': '/quality/mask/r10m/b08', 'title': 'msk_quality_b08'}, 'msk_quality_b09': {'href': '/quality/mask/r60m/b09', 'title': 'msk_quality_b09'}, 'msk_quality_b10': {'href': '/quality/mask/r60m/b10', 'title': 'msk_quality_b10'}, 'msk_quality_b11': {'href': '/quality/mask/r20m/b11', 'title': 'msk_quality_b11'}, 'msk_quality_b12': {'href': '/quality/mask/r20m/b12', 'title': 'msk_quality_b12'}, 'msk_quality_b8a': {'href': '/quality/mask/r20m/b8a', 'title': 'msk_quality_b8a'}, 'msl': {'href': '/conditions/meteorology/ecmwf/msl', 'title': 'msl'}, 'omaod550': {'href': '/conditions/meteorology/cams/omaod550', 'title': 'omaod550'}, 'r': {'href': '/conditions/meteorology/ecmwf/r', 'title': 'r'}, 'ssaod550': {'href': '/conditions/meteorology/cams/ssaod550', 'title': 'ssaod550'}, 'suaod550': {'href': '/conditions/meteorology/cams/suaod550', 'title': 'suaod550'}, 'sun_angles': {'href': '/conditions/geometry/sun_angles', 'title': 'sun_angles'}, 'tco3': {'href': '/conditions/meteorology/ecmwf/tco3', 'title': 'tco3'}, 'tcwv': {'href': '/conditions/meteorology/ecmwf/tcwv', 'title': 'tcwv'}, 'u10': {'href': '/conditions/meteorology/ecmwf/u10', 'title': 'u10'}, 'v10': {'href': '/conditions/meteorology/ecmwf/v10', 'title': 'v10'}, 'viewing_incidence_angles': {'href': '/conditions/geometry/viewing_incidence_angles', 'title': 'viewing_incidence_angles'}, 'z': {'href': '/conditions/meteorology/cams/z', 'title': 'z'}}, 'bbox': [7.871918704100081, 44.14299463915654, 6.814260663879186, 45.148073182871244], 'geometry': {'coordinates': [[[6.814260663879186, 44.14299463915654], [6.862849633524148, 44.28854980816078], [6.913542734971571, 44.434750348537925], [6.962313790019774, 44.58155306058976], [7.010955757503, 44.728499728638816], [7.060405299364974, 44.875326118891536], [7.107587454389481, 45.02285613715742], [7.146790489526708, 45.1366650993253], [7.852592618382666, 45.148073182871244], [7.871918704100081, 44.15979663371134], [6.814260663879186, 44.14299463915654]]], 'type': 'Polygon'}, 'id': 'S02MSIL1C_20260313T101741_0000_A065_T003', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4427, 'eo:common_name': 'coastal', 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1884.69, 'name': '01', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 0.49269999999999997, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.065, 'eo:solar_illumination': 1959.66, 'name': '02', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.5598, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.035, 'eo:solar_illumination': 1823.24, 'name': '03', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.6646000000000001, 'eo:common_name': 'red', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 1512.06, 'name': '04', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.7041000000000001, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1424.64, 'name': '05', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7405, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1287.61, 'name': '06', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7827999999999999, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1162.08, 'name': '07', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.8328, 'eo:common_name': 'nir', 'eo:full_width_half_max': 0.105, 'eo:solar_illumination': 1041.63, 'name': '08', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.8647, 'eo:full_width_half_max': 0.02, 'name': '8A', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.9451, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 812.92, 'name': '09', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.3735, 'eo:common_name': 'cirrus', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 367.15, 'name': '10', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.6137000000000001, 'eo:common_name': 'swir16', 'eo:full_width_half_max': 0.09, 'eo:solar_illumination': 245.59, 'name': '11', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 2.2024000000000004, 'eo:common_name': 'swir22', 'eo:full_width_half_max': 0.18, 'name': '12', 'raster:spatial_resolution': '20'}], 'constellation': 'sentinel-2', 'created': '2026-03-13T15:38:53.000000Z', 'datetime': '2026-03-13T10:17:41.024000Z', 'end_datetime': '2026-03-13T10:17:41.024Z', 'eo:cloud_cover': 13.9006004077606, 'eo:snow_cover': 24.7909244257559, 'eopf:datastrip_id': 'S2A_OPER_MSI_L1C_DS_2APS_20260313T153853_S20260313T102456_N05.12', 'eopf:datatake_id': 'GS2A_20260313T101741_056004_N05.12', 'eopf:instrument_mode': 'INS-NOBS', 'gsd': '10', 'instruments': ['msi'], 'platform': 'sentinel-2a', 'processing:facility': 'ESA', 'processing:level': 'L1C', 'processing:lineage': 'systematic', 'processing:version': '05.12', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT', 'product:type': 'S02MSIL1C', 'providers': [{'name': 'L1C processor', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 56004, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2015-028A', 'sat:relative_orbit': 65, 'sci:doi': '10.5270/S2_-742ikth', 'start_datetime': '2026-03-13T10:17:41.024Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/raster/v2.0.0/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/product/v0.1.0/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 [11]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    op_mode="native",
    chunks={},
    group_sep="/",
    variables=["measurements/r60m/b09", "measurements/r60m/b10"],
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", op_mode="native", chunks={}, group_sep="/", variables=["measurements/r60m/b09", "measurements/r60m/b10"], ) ds

Out[11]:

<xarray.Dataset> Size: 54MB
Dimensions:                (measurements/r60m/y: 1830, measurements/r60m/x: 1830)
Coordinates:
  * measurements/r60m/x    (measurements/r60m/x) float32 7kB 3e+05 ... 4.098e+05
  * measurements/r60m/y    (measurements/r60m/y) float32 7kB 5e+06 ... 4.89e+06
Data variables:
    measurements/r60m/b09  (measurements/r60m/y, measurements/r60m/x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    measurements/r60m/b10  (measurements/r60m/y, measurements/r60m/x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
Attributes: (3)

xarray.Dataset

• Dimensions:
  • measurements/r60m/y: 1830
  • measurements/r60m/x: 1830
• Coordinates: (2)
  • measurements/r60m/x
    (measurements/r60m/x)
    float32
    3e+05 3.001e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
          shape=(1830,), dtype=float32)

  • measurements/r60m/y
    (measurements/r60m/y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of image at 60m in meters from up-left pixel

    units :

    m

    array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
          shape=(1830,), dtype=float32)

• Data variables: (2)
  • measurements/r60m/b09
    (measurements/r60m/y, measurements/r60m/x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 09 940 nm', 'scale_factor': 0.0001, 'short_name': 'b09_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 09 940 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/r60m/b10
    (measurements/r60m/y, measurements/r60m/x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'TOA reflectance from MSI acquisition at spectral band 10 1375 nm', 'scale_factor': 0.0001, 'short_name': 'b10_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 10 1375 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • measurements/r60m/x
    PandasIndex

    PandasIndex(Index([300030.0, 300090.0, 300150.0, 300210.0, 300270.0, 300330.0, 300390.0,
           300450.0, 300510.0, 300570.0,
           ...
           409230.0, 409290.0, 409350.0, 409410.0, 409470.0, 409530.0, 409590.0,
           409650.0, 409710.0, 409770.0],
          dtype='float32', name='measurements/r60m/x', length=1830))

  • measurements/r60m/y
    PandasIndex

    PandasIndex(Index([5000010.0, 4999950.0, 4999890.0, 4999830.0, 4999770.0, 4999710.0,
           4999650.0, 4999590.0, 4999530.0, 4999470.0,
           ...
           4890810.0, 4890750.0, 4890690.0, 4890630.0, 4890570.0, 4890510.0,
           4890450.0, 4890390.0, 4890330.0, 4890270.0],
          dtype='float32', name='measurements/r60m/y', length=1830))

• Attributes: (3)

  other_metadata :

  {'L0_ancillary_data_quality': 'null', 'L0_ephemeris_data_quality': 'null', 'NUC_table_ID': 3, 'SWIR_rearrangement_flag': 'null', 'UTM_zone_identification': 'UTM zone 32N', 'absolute_location_assessment_from_AOCS': 'null', 'band_description': {'01': {'bandwidth': '20.0', 'central_wavelength': 442.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.4473767, 'physical_gain': 4.0887333, 'spectral_response_step': 1, 'spectral_response_values': '0.001775742 0.004073061 0.003626143 0.003515199 0.005729163 0.003780292 0.002636732 0.001262113 0.001987583 0.001368913 0.001250444 0.000463454 0.000814293 0.001376431 0.001485086 0.001823735 0.001626817 0.004392062 0.029008099 0.11874593 0.32387506 0.57281921 0.71472749 0.76196778 0.78929702 0.80862387 0.81089382 0.82419876 0.85415811 0.87079088 0.88731097 0.92619924 0.98228149 1 0.9752382 0.93596338 0.88997148 0.85021048 0.82569453 0.78390239 0.61417422 0.33007109 0.12410831 0.04365694 0.014749595', 'units': 'nm', 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  stac_discovery :

  {'assets': {'aod1240': {'href': '/conditions/meteorology/cams/aod1240', 'title': 'aod1240'}, 'aod469': {'href': '/conditions/meteorology/cams/aod469', 'title': 'aod469'}, 'aod550': {'href': '/conditions/meteorology/cams/aod550', 'title': 'aod550'}, 'aod670': {'href': '/conditions/meteorology/cams/aod670', 'title': 'aod670'}, 'aod865': {'href': '/conditions/meteorology/cams/aod865', 'title': 'aod865'}, 'b01_60m': {'href': '/measurements/reflectance/r60m/b01', 'title': 'b01_60m'}, 'b02_10m': {'href': '/measurements/reflectance/r10m/b02', 'title': 'b02_10m'}, 'b03_10m': {'href': '/measurements/reflectance/r10m/b03', 'title': 'b03_10m'}, 'b04_10m': {'href': '/measurements/reflectance/r10m/b04', 'title': 'b04_10m'}, 'b05_20m': {'href': '/measurements/reflectance/r20m/b05', 'title': 'b05_20m'}, 'b06_20m': {'href': '/measurements/reflectance/r20m/b06', 'title': 'b06_20m'}, 'b07_20m': {'href': '/measurements/reflectance/r20m/b07', 'title': 'b07_20m'}, 'b08_10m': {'href': '/measurements/reflectance/r10m/b08', 'title': 'b08_10m'}, 'b09_r60m': {'href': '/measurements/reflectance/r60m/b09', 'title': 'b09_r60m'}, 'b10_60m': {'href': '/measurements/reflectance/r60m/b10', 'title': 'b10_60m'}, 'b11_20m': {'href': '/measurements/reflectance/r20m/b11', 'title': 'b11_20m'}, 'b12_20m': {'href': '/measurements/reflectance/r20m/b12', 'title': 'b12_20m'}, 'b8a_20m': {'href': '/measurements/reflectance/r20m/b8a', 'title': 'b8a_20m'}, 'bcaod550': {'href': '/conditions/meteorology/cams/bcaod550', 'title': 'bcaod550'}, 'duaod550': {'href': '/conditions/meteorology/cams/duaod550', 'title': 'duaod550'}, 'mean_sun_angles': {'href': '/conditions/geometry/mean_sun_angles', 'title': 'mean_sun_angles'}, 'mean_viewing_incidence_angles': {'href': '/conditions/geometry/mean_viewing_incidence_angles', 'title': 'mean_viewing_incidence_angles'}, 'msk_cls_b00': {'href': '/conditions/mask/l1c_classification/r60m/b00', 'title': 'msk_cls_b00'}, 'msk_detfoo_b01': {'href': '/conditions/mask/detector_footprint/r60m/b01', 'title': 'msk_detfoo_b01'}, 'msk_detfoo_b02': {'href': '/conditions/mask/detector_footprint/r10m/b02', 'title': 'msk_detfoo_b02'}, 'msk_detfoo_b03': {'href': '/conditions/mask/detector_footprint/r10m/b03', 'title': 'msk_detfoo_b03'}, 'msk_detfoo_b04': {'href': '/conditions/mask/detector_footprint/r10m/b04', 'title': 'msk_detfoo_b04'}, 'msk_detfoo_b05': {'href': '/conditions/mask/detector_footprint/r20m/b05', 'title': 'msk_detfoo_b05'}, 'msk_detfoo_b06': {'href': '/conditions/mask/detector_footprint/r20m/b06', 'title': 'msk_detfoo_b06'}, 'msk_detfoo_b07': {'href': '/conditions/mask/detector_footprint/r20m/b07', 'title': 'msk_detfoo_b07'}, 'msk_detfoo_b08': {'href': '/conditions/mask/detector_footprint/r10m/b08', 'title': 'msk_detfoo_b08'}, 'msk_detfoo_b09': {'href': '/conditions/mask/detector_footprint/r60m/b09', 'title': 'msk_detfoo_b09'}, 'msk_detfoo_b10': {'href': '/conditions/mask/detector_footprint/r60m/b10', 'title': 'msk_detfoo_b10'}, 'msk_detfoo_b11': {'href': '/conditions/mask/detector_footprint/r20m/b11', 'title': 'msk_detfoo_b11'}, 'msk_detfoo_b12': {'href': '/conditions/mask/detector_footprint/r20m/b12', 'title': 'msk_detfoo_b12'}, 'msk_detfoo_b8a': {'href': '/conditions/mask/detector_footprint/r20m/b8a', 'title': 'msk_detfoo_b8a'}, 'msk_quality_b01': {'href': '/quality/mask/r60m/b01', 'title': 'msk_quality_b01'}, 'msk_quality_b02': {'href': '/quality/mask/r10m/b02', 'title': 'msk_quality_b02'}, 'msk_quality_b03': {'href': '/quality/mask/r10m/b03', 'title': 'msk_quality_b03'}, 'msk_quality_b04': {'href': '/quality/mask/r10m/b04', 'title': 'msk_quality_b04'}, 'msk_quality_b05': {'href': '/quality/mask/r20m/b05', 'title': 'msk_quality_b05'}, 'msk_quality_b06': {'href': '/quality/mask/r20m/b06', 'title': 'msk_quality_b06'}, 'msk_quality_b07': {'href': '/quality/mask/r20m/b07', 'title': 'msk_quality_b07'}, 'msk_quality_b08': {'href': '/quality/mask/r10m/b08', 'title': 'msk_quality_b08'}, 'msk_quality_b09': {'href': '/quality/mask/r60m/b09', 'title': 'msk_quality_b09'}, 'msk_quality_b10': {'href': '/quality/mask/r60m/b10', 'title': 'msk_quality_b10'}, 'msk_quality_b11': {'href': '/quality/mask/r20m/b11', 'title': 'msk_quality_b11'}, 'msk_quality_b12': {'href': '/quality/mask/r20m/b12', 'title': 'msk_quality_b12'}, 'msk_quality_b8a': {'href': '/quality/mask/r20m/b8a', 'title': 'msk_quality_b8a'}, 'msl': {'href': '/conditions/meteorology/ecmwf/msl', 'title': 'msl'}, 'omaod550': {'href': '/conditions/meteorology/cams/omaod550', 'title': 'omaod550'}, 'r': {'href': '/conditions/meteorology/ecmwf/r', 'title': 'r'}, 'ssaod550': {'href': '/conditions/meteorology/cams/ssaod550', 'title': 'ssaod550'}, 'suaod550': {'href': '/conditions/meteorology/cams/suaod550', 'title': 'suaod550'}, 'sun_angles': {'href': '/conditions/geometry/sun_angles', 'title': 'sun_angles'}, 'tco3': {'href': '/conditions/meteorology/ecmwf/tco3', 'title': 'tco3'}, 'tcwv': {'href': '/conditions/meteorology/ecmwf/tcwv', 'title': 'tcwv'}, 'u10': {'href': '/conditions/meteorology/ecmwf/u10', 'title': 'u10'}, 'v10': {'href': '/conditions/meteorology/ecmwf/v10', 'title': 'v10'}, 'viewing_incidence_angles': {'href': '/conditions/geometry/viewing_incidence_angles', 'title': 'viewing_incidence_angles'}, 'z': {'href': '/conditions/meteorology/cams/z', 'title': 'z'}}, 'bbox': [7.871918704100081, 44.14299463915654, 6.814260663879186, 45.148073182871244], 'geometry': {'coordinates': [[[6.814260663879186, 44.14299463915654], [6.862849633524148, 44.28854980816078], [6.913542734971571, 44.434750348537925], [6.962313790019774, 44.58155306058976], [7.010955757503, 44.728499728638816], [7.060405299364974, 44.875326118891536], [7.107587454389481, 45.02285613715742], [7.146790489526708, 45.1366650993253], [7.852592618382666, 45.148073182871244], [7.871918704100081, 44.15979663371134], [6.814260663879186, 44.14299463915654]]], 'type': 'Polygon'}, 'id': 'S02MSIL1C_20260313T101741_0000_A065_T003', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4427, 'eo:common_name': 'coastal', 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1884.69, 'name': '01', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 0.49269999999999997, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.065, 'eo:solar_illumination': 1959.66, 'name': '02', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.5598, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.035, 'eo:solar_illumination': 1823.24, 'name': '03', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.6646000000000001, 'eo:common_name': 'red', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 1512.06, 'name': '04', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.7041000000000001, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1424.64, 'name': '05', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7405, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1287.61, 'name': '06', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7827999999999999, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1162.08, 'name': '07', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.8328, 'eo:common_name': 'nir', 'eo:full_width_half_max': 0.105, 'eo:solar_illumination': 1041.63, 'name': '08', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.8647, 'eo:full_width_half_max': 0.02, 'name': '8A', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.9451, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 812.92, 'name': '09', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.3735, 'eo:common_name': 'cirrus', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 367.15, 'name': '10', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.6137000000000001, 'eo:common_name': 'swir16', 'eo:full_width_half_max': 0.09, 'eo:solar_illumination': 245.59, 'name': '11', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 2.2024000000000004, 'eo:common_name': 'swir22', 'eo:full_width_half_max': 0.18, 'name': '12', 'raster:spatial_resolution': '20'}], 'constellation': 'sentinel-2', 'created': '2026-03-13T15:38:53.000000Z', 'datetime': '2026-03-13T10:17:41.024000Z', 'end_datetime': '2026-03-13T10:17:41.024Z', 'eo:cloud_cover': 13.9006004077606, 'eo:snow_cover': 24.7909244257559, 'eopf:datastrip_id': 'S2A_OPER_MSI_L1C_DS_2APS_20260313T153853_S20260313T102456_N05.12', 'eopf:datatake_id': 'GS2A_20260313T101741_056004_N05.12', 'eopf:instrument_mode': 'INS-NOBS', 'gsd': '10', 'instruments': ['msi'], 'platform': 'sentinel-2a', 'processing:facility': 'ESA', 'processing:level': 'L1C', 'processing:lineage': 'systematic', 'processing:version': '05.12', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT', 'product:type': 'S02MSIL1C', 'providers': [{'name': 'L1C processor', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 56004, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2015-028A', 'sat:relative_orbit': 65, 'sci:doi': '10.5270/S2_-742ikth', 'start_datetime': '2026-03-13T10:17:41.024Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/raster/v2.0.0/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/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

Also here, we can plot one spectral band as an example.

In [12]:

ds["measurements/r60m/b09"].plot(vmin=0., vmax=1.)

ds["measurements/r60m/b09"].plot(vmin=0., vmax=1.)

Out[12]:

<matplotlib.collections.QuadMesh at 0x7dff2c134410>

---

Open a Sentinel-2 Level-2A¶

We now access a Sentinel-2 Level-2A product in native mode. The data access methods shown above apply equally to Level-2A products.

Find a Sentinel-2 Level-2A Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for available Sentinel-2 Level-2A tiles.

In [13]:

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-2-l2a"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-2-l2a"], bbox=[7.2, 44.5, 7.4, 44.7], datetime=["2026-03-13", "2026-03-13"], ).items() ) items

Out[13]:

[<Item id=S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916>]

In [14]:

item = items[0]
item

item = items[0] item

Out[14]:

<Item id=S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 13 items

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

  • 1 "https://stac-extensions.github.io/eo/v2.0.0/schema.json"

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

  • 3 "https://stac-extensions.github.io/projection/v2.0.0/schema.json"

  • 4 "https://stac-extensions.github.io/mgrs/v1.0.0/schema.json"

  • 5 "https://stac-extensions.github.io/grid/v1.1.0/schema.json"

  • 6 "https://stac-extensions.github.io/view/v1.0.0/schema.json"

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

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

  • 9 "https://stac-extensions.github.io/scientific/v1.0.0/schema.json"

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

  • 11 "https://stac-extensions.github.io/version/v1.2.0/schema.json"

  • 12 "https://stac-extensions.github.io/raster/v2.0.0/schema.json"
• id "S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 11 items

      • 0[] 2 items

        • 0 6.814260663879186

        • 1 44.14299463915654

      • 1[] 2 items

        • 0 6.862849633524148

        • 1 44.28854980816078

      • 2[] 2 items

        • 0 6.913542734971571

        • 1 44.434750348537925

      • 3[] 2 items

        • 0 6.962313790019774

        • 1 44.58155306058976

      • 4[] 2 items

        • 0 7.010955757503

        • 1 44.728499728638816

      • 5[] 2 items

        • 0 7.060405299364974

        • 1 44.875326118891536

      • 6[] 2 items

        • 0 7.107587454389481

        • 1 45.02285613715742

      • 7[] 2 items

        • 0 7.146790489526708

        • 1 45.1366650993253

      • 8[] 2 items

        • 0 7.852592618382666

        • 1 45.148073182871244

      • 9[] 2 items

        • 0 7.871918704100081

        • 1 44.15979663371134

      • 10[] 2 items

        • 0 6.814260663879186

        • 1 44.14299463915654
• bbox[] 4 items

  • 0 6.814260663879186

  • 1 44.14299463915654

  • 2 7.871918704100081

  • 3 45.148073182871244
• properties

  • gsd 10.0
  • created "2026-03-13T23:25:58.728006Z"
  • mission "Sentinel-2"
  • sci:doi "10.5270/S2_-znk9xsj"
  • updated "2026-03-13T23:25:58.728006Z"
  • datetime "2026-03-13T10:17:41.024000Z"
  • platform "sentinel-2a"
  • grid:code "MGRS-32TLQ"
  • proj:bbox[] 4 items

    • 0 6.814260663879186

    • 1 44.14299463915654

    • 2 7.871918704100081

    • 3 45.148073182871244
  • proj:code "EPSG:32632"
  • 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-2"
      • 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 "2026-03-13T23:25:58.728006Z"
  • deprecated False
  • instruments[] 1 items

    • 0 "msi"
  • end_datetime "2026-03-13T10:17:41.024000Z"
  • product:type "S02MSIL2A"
  • constellation "sentinel-2"
  • eo:snow_cover 21.842474
  • mgrs:utm_zone 32
  • proj:centroid

    • lat 44.61398
    • lon 7.41649
  • eo:cloud_cover 16.11875
  • start_datetime "2026-03-13T10:17:41.024000Z"
  • sat:orbit_state "descending"
  • eopf:datatake_id "GS2A_20260313T101741_056004_N05.12"
  • mgrs:grid_square "LQ"
  • processing:level "L2A"
  • view:sun_azimuth 156.346195853372
  • eopf:datastrip_id "S2A_OPER_MSI_L2A_DS_2APS_20260313T171916_S20260313T102456_N05.12"
  • mgrs:latitude_band "T"
  • processing:lineage "systematic"
  • processing:version "05.12"
  • product:timeliness "PT3H"
  • sat:absolute_orbit 56004
  • sat:relative_orbit 65
  • view:sun_elevation 50.1170463180154
  • processing:facility "ESA"
  • processing:software

    • EOPF-CPM "2.6.2"
  • eopf:instrument_mode "INS-NOBS"
  • product:timeliness_category "NRT"
  • sat:platform_international_designator "2015-028A"
• links[] 6 items

  • 0

    • rel "collection"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-2-l2a"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-2-l2a"
    • 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-2-l2a/items/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916"
    • type "application/geo+json"

  • 4

    • rel "cite-as"
    • href "https://doi.org/10.5270/S2_-znk9xsj"

  • 5

    • 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

  • SR_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r10m"
    • type "application/vnd+zarr"
    • title "Surface Reflectance - 10m"
    • gsd 10
    • bands[] 4 items

      • 0

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098

      • 1

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 2

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 3

        • name "B08"
        • description "NIR 1 (band 8)"
        • eo:common_name "nir"
        • eo:center_wavelength 0.842
        • eo:full_width_half_max 0.145
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

      • 2 "dataset"
  • SR_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m"
    • type "application/vnd+zarr"
    • title "Surface Reflectance - 20m"
    • gsd 20
    • bands[] 10 items

      • 0

        • name "B01"
        • description "Coastal aerosol (band 1)"
        • eo:common_name "coastal"
        • eo:center_wavelength 0.443
        • eo:full_width_half_max 0.027

      • 1

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098

      • 2

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 3

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 4

        • name "B05"
        • description "Red edge 1 (band 5)"
        • eo:common_name "rededge071"
        • eo:center_wavelength 0.704
        • eo:full_width_half_max 0.019

      • 5

        • name "B06"
        • description "Red edge 2 (band 6)"
        • eo:common_name "rededge075"
        • eo:center_wavelength 0.74
        • eo:full_width_half_max 0.018

      • 6

        • name "B07"
        • description "Red edge 3 (band 7)"
        • eo:common_name "rededge078"
        • eo:center_wavelength 0.783
        • eo:full_width_half_max 0.028

      • 7

        • name "B8A"
        • description "NIR 2 (band 8A)"
        • eo:common_name "nir08"
        • eo:center_wavelength 0.865
        • eo:full_width_half_max 0.033

      • 8

        • name "B11"
        • description "SWIR 1 (band 11)"
        • eo:common_name "swir16"
        • eo:center_wavelength 1.61
        • eo:full_width_half_max 0.143

      • 9

        • name "B12"
        • description "SWIR 2 (band 12)"
        • eo:common_name "swir22"
        • eo:center_wavelength 2.19
        • eo:full_width_half_max 0.242
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

      • 2 "dataset"
  • SR_60m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r60m"
    • type "application/vnd+zarr"
    • title "Surface Reflectance - 60m"
    • gsd 60
    • bands[] 11 items

      • 0

        • name "B01"
        • description "Coastal aerosol (band 1)"
        • eo:common_name "coastal"
        • eo:center_wavelength 0.443
        • eo:full_width_half_max 0.027

      • 1

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098

      • 2

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 3

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 4

        • name "B05"
        • description "Red edge 1 (band 5)"
        • eo:common_name "rededge071"
        • eo:center_wavelength 0.704
        • eo:full_width_half_max 0.019

      • 5

        • name "B06"
        • description "Red edge 2 (band 6)"
        • eo:common_name "rededge075"
        • eo:center_wavelength 0.74
        • eo:full_width_half_max 0.018

      • 6

        • name "B07"
        • description "Red edge 3 (band 7)"
        • eo:common_name "rededge078"
        • eo:center_wavelength 0.783
        • eo:full_width_half_max 0.028

      • 7

        • name "B8A"
        • description "NIR 2 (band 8A)"
        • eo:common_name "nir08"
        • eo:center_wavelength 0.865
        • eo:full_width_half_max 0.033

      • 8

        • name "B09"
        • description "NIR 3 (band 9)"
        • eo:common_name "nir09"
        • eo:center_wavelength 0.945
        • eo:full_width_half_max 0.026

      • 9

        • name "B11"
        • description "SWIR 1 (band 11)"
        • eo:common_name "swir16"
        • eo:center_wavelength 1.61
        • eo:full_width_half_max 0.143

      • 10

        • name "B12"
        • description "SWIR 2 (band 12)"
        • eo:common_name "swir22"
        • eo:center_wavelength 2.19
        • eo:full_width_half_max 0.242
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

      • 2 "dataset"
  • AOT_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/quality/atmosphere/r10m/aot"
    • type "application/vnd+zarr"
    • title "Aerosol optical thickness (AOT)"
    • description "Aerosol Optical Thickness map at 10m (for 550nm) resampled from 20m AOT map"
    • gsd 10
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.001
    • raster:offset 0
    • roles[] 1 items

      • 0 "data"
  • B01_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b01"
    • type "application/vnd+zarr"
    • title "Coastal aerosol (band 1) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 01 443 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B01"
        • description "Coastal aerosol (band 1)"
        • eo:common_name "coastal"
        • eo:center_wavelength 0.443
        • eo:full_width_half_max 0.027
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B02_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r10m/b02"
    • type "application/vnd+zarr"
    • title "Blue (band 2) - 10m"
    • description "BOA reflectance from MSI acquisition at spectral band 02 490 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B03_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r10m/b03"
    • type "application/vnd+zarr"
    • title "Green (band 3) - 10m"
    • description "BOA reflectance from MSI acquisition at spectral band 03 560 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B04_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r10m/b04"
    • type "application/vnd+zarr"
    • title "Red (band 4) - 10m"
    • description "BOA reflectance from MSI acquisition at spectral band 04 665 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B05_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b05"
    • type "application/vnd+zarr"
    • title "Red edge 1 (band 5) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 05 705 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B05"
        • description "Red edge 1 (band 5)"
        • eo:common_name "rededge071"
        • eo:center_wavelength 0.704
        • eo:full_width_half_max 0.019
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B06_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b06"
    • type "application/vnd+zarr"
    • title "Red edge 2 (band 6) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 06 740 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B06"
        • description "Red edge 2 (band 6)"
        • eo:common_name "rededge075"
        • eo:center_wavelength 0.74
        • eo:full_width_half_max 0.018
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B07_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b07"
    • type "application/vnd+zarr"
    • title "Red edge 3 (band 7) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 07 783 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B07"
        • description "Red edge 3 (band 7)"
        • eo:common_name "rededge078"
        • eo:center_wavelength 0.783
        • eo:full_width_half_max 0.028
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B08_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r10m/b08"
    • type "application/vnd+zarr"
    • title "NIR 1 (band 8) - 10m"
    • description "BOA reflectance from MSI acquisition at spectral band 08 842 nm"
    • gsd 10
    • bands[] 1 items

      • 0

        • name "B08"
        • description "NIR 1 (band 8)"
        • eo:common_name "nir"
        • eo:center_wavelength 0.842
        • eo:full_width_half_max 0.145
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B09_60m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r60m/b09"
    • type "application/vnd+zarr"
    • title "NIR 3 (band 9) - 60m"
    • description "BOA reflectance from MSI acquisition at spectral band 09 940 nm"
    • gsd 60
    • bands[] 1 items

      • 0

        • name "B09"
        • description "NIR 3 (band 9)"
        • eo:common_name "nir09"
        • eo:center_wavelength 0.945
        • eo:full_width_half_max 0.026
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B11_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b11"
    • type "application/vnd+zarr"
    • title "SWIR 1 (band 11) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 11 1610 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B11"
        • description "SWIR 1 (band 11)"
        • eo:common_name "swir16"
        • eo:center_wavelength 1.61
        • eo:full_width_half_max 0.143
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B12_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b12"
    • type "application/vnd+zarr"
    • title "SWIR 2 (band 12) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 12 2190 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B12"
        • description "SWIR 2 (band 12)"
        • eo:common_name "swir22"
        • eo:center_wavelength 2.19
        • eo:full_width_half_max 0.242
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • B8A_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/measurements/reflectance/r20m/b8a"
    • type "application/vnd+zarr"
    • title "NIR 2 (band 8A) - 20m"
    • description "BOA reflectance from MSI acquisition at spectral band 8A 865 nm"
    • gsd 20
    • bands[] 1 items

      • 0

        • name "B8A"
        • description "NIR 2 (band 8A)"
        • eo:common_name "nir08"
        • eo:center_wavelength 0.865
        • eo:full_width_half_max 0.033
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.0001
    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
  • SCL_20m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/conditions/mask/l2a_classification/r20m/scl"
    • type "application/vnd+zarr"
    • title "Scene classification map (SCL)"
    • description "scene classification map at 20m"
    • gsd 20
    • nodata 0
    • data_type "uint8"
    • raster:scale 1
    • raster:offset 0
    • roles[] 1 items

      • 0 "data"
  • TCI_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/quality/l2a_quicklook/r10m/tci"
    • type "application/vnd+zarr"
    • title "True color image"
    • gsd 10
    • bands[] 3 items

      • 0

        • name "B04"
        • description "Red (band 4)"
        • eo:common_name "red"
        • eo:center_wavelength 0.665
        • eo:full_width_half_max 0.038

      • 1

        • name "B03"
        • description "Green (band 3)"
        • eo:common_name "green"
        • eo:center_wavelength 0.56
        • eo:full_width_half_max 0.045

      • 2

        • name "B02"
        • description "Blue (band 2)"
        • eo:common_name "blue"
        • eo:center_wavelength 0.49
        • eo:full_width_half_max 0.098
    • roles[] 1 items

      • 0 "data"
  • WVP_10m

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/quality/atmosphere/r10m/wvp"
    • type "application/vnd+zarr"
    • title "Water vapour (WVP)"
    • description "Water Vapour Content map at 10m"
    • gsd 10
    • nodata 0
    • data_type "uint16"
    • raster:scale 0.001
    • raster:offset 0
    • roles[] 1 items

      • 0 "data"
  • product

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.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"
  • zipped_product

    • href "https://download.user.eopf.eodc.eu/zip/collections/sentinel-2-l2a/items/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.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-s02msil2a-eu/13/products/cpm_v262/S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr/.zmetadata"
    • type "application/json"
    • title "Consolidated Metadata"
    • description "Consolidated metadata of the EOPF product"
    • roles[] 1 items

      • 0 "metadata"
• collection "sentinel-2-l2a"

Open Sentinel-2 Level-2A as xarray.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 [15]:

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

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

Out[15]:

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

xarray.DataTree

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

  conditions

  • Groups: (3)
    <xarray.DatasetView> Size: 449kB
    Dimensions:                        (angle: 2, band: 13, y: 23, x: 23,
                                        detector: 4)
    Coordinates:
      * angle                          (angle) <U7 56B 'zenith' 'azimuth'
      * band                           (band) <U3 156B 'b00' 'b01' ... 'b11' 'b12'
      * detector                       (detector) <U3 48B 'd01' 'd02' 'd03' 'd04'
      * x                              (x) float32 92B 3e+05 3.05e+05 ... 4.1e+05
      * y                              (y) float32 92B 5e+06 4.995e+06 ... 4.89e+06
    Data variables:
        mean_sun_angles                (angle) float64 16B dask.array<chunksize=(2,), meta=np.ndarray>
        mean_viewing_incidence_angles  (band, angle) float64 208B dask.array<chunksize=(13, 2), meta=np.ndarray>
        sun_angles                     (angle, y, x) float64 8kB dask.array<chunksize=(2, 23, 23), meta=np.ndarray>
        viewing_incidence_angles       (band, detector, angle, y, x) float64 440kB dask.array<chunksize=(13, 4, 2, 23, 23), meta=np.ndarray>

    geometry

    • Groups: (0)
    • Dimensions:
      • angle: 2
      • band: 13
      • y: 23
      • x: 23
      • detector: 4
    • Coordinates: (5)
      • angle
        (angle)
        <U7
        'zenith' 'azimuth'

        dimensions :

        ['angle']

        dtype :

        <U7

        long_name :

        zenith and azimuth angles

        array(['zenith', 'azimuth'], dtype='<U7')

      • band
        (band)
        <U3
        'b00' 'b01' 'b02' ... 'b11' 'b12'

        dimensions :

        ['band']

        dtype :

        <U3

        flag_meanings :

        B01 B02 B03 B04 B05 B06 B07 B08 B8A B09 B10 B11 B12

        flag_values :

        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

        long_name :

        spectral band number

        array(['b00', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07', 'b08', 'b09',
               'b10', 'b11', 'b12'], dtype='<U3')

      • detector
        (detector)
        <U3
        'd01' 'd02' 'd03' 'd04'

        dimensions :

        ['detector']

        dtype :

        <U3

        fill_value :

        0

        long_name :

        detector module number

        valid_max :

        12

        valid_min :

        1

        array(['d01', 'd02', 'd03', 'd04'], dtype='<U3')

      • x
        (x)
        float32
        3e+05 3.05e+05 ... 4.05e+05 4.1e+05

        dimensions :

        ['x']

        dtype :

        <f4

        long_name :

        x coordinates of angles grid, in steps of 5km from up-left pixel

        units :

        m

        array([300000., 305000., 310000., 315000., 320000., 325000., 330000., 335000.,
               340000., 345000., 350000., 355000., 360000., 365000., 370000., 375000.,
               380000., 385000., 390000., 395000., 400000., 405000., 410000.],
              dtype=float32)

      • y
        (y)
        float32
        5e+06 4.995e+06 ... 4.89e+06

        dimensions :

        ['y']

        dtype :

        <f4

        long_name :

        y coordinates of angles grid, in steps of 5km from up-left pixel

        units :

        m

        array([5000040., 4995040., 4990040., 4985040., 4980040., 4975040., 4970040.,
               4965040., 4960040., 4955040., 4950040., 4945040., 4940040., 4935040.,
               4930040., 4925040., 4920040., 4915040., 4910040., 4905040., 4900040.,
               4895040., 4890040.], dtype=float32)

    • Data variables: (4)
      • mean_sun_angles
        (angle)
        float64
        dask.array<chunksize=(2,), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle'], 'dimensions': ['angle'], 'dtype': '<f8', 'long_name': 'mean solar angles (all detectors, all bands)', 'short_name': 'mean_sun_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        mean solar angles (all detectors, all bands)

        units :

        degrees

        Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • mean_viewing_incidence_angles
        (band, angle)
        float64
        dask.array<chunksize=(13, 2), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle', 'band'], 'dimensions': ['band', 'angle'], 'dtype': '<f8', 'long_name': 'mean viewing incidence angles', 'short_name': 'mean_viewing_incidence_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        mean viewing incidence angles

        units :

        degrees

        Array Chunk Bytes 208 B 208 B Shape (13, 2) (13, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • sun_angles
        (angle, y, x)
        float64
        dask.array<chunksize=(2, 23, 23), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle', 'y', 'x'], 'dimensions': ['angle', 'y', 'x'], 'dtype': '<f8', 'long_name': 'solar angles', 'short_name': 'sun_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        solar angles

        units :

        degrees

        Array Chunk Bytes 8.27 kiB 8.27 kiB Shape (2, 23, 23) (2, 23, 23) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • viewing_incidence_angles
        (band, detector, angle, y, x)
        float64
        dask.array<chunksize=(13, 4, 2, 23, 23), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['angle', 'y', 'x', 'band', 'detector'], 'dimensions': ['band', 'detector', 'angle', 'y', 'x'], 'dtype': '<f8', 'long_name': 'viewing incidence angles', 'short_name': 'viewing_incidence_angles', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        viewing incidence angles

        units :

        degrees

        Array Chunk Bytes 429.81 kiB 429.81 kiB Shape (13, 4, 2, 23, 23) (13, 4, 2, 23, 23) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

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

    mask

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

      detector_footprint

      • Groups: (3)
        <xarray.DatasetView> Size: 482MB
        Dimensions:  (y: 10980, x: 10980)
        Coordinates:
          * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
          * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
        Data variables:
            b02      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
            b03      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
            b04      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
            b08      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

        r10m

        • Groups: (0)
        • Dimensions:
          • y: 10980
          • x: 10980
        • Coordinates: (2)
          • x
            (x)
            float32
            3e+05 3e+05 ... 4.098e+05 4.098e+05

            dimensions :

            ['x']

            dtype :

            <f4

            long_name :

            x coordinates of the image at 10m in meters from up-left pixel

            units :

            m

            array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
                  shape=(10980,), dtype=float32)

          • y
            (y)
            float32
            5e+06 5e+06 ... 4.89e+06 4.89e+06

            dimensions :

            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of the image at 10m in meters from up-left pixel

            units :

            m

            array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
                  shape=(10980,), dtype=float32)

        • Data variables: (4)
          • b02
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b02'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b03
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b03'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b04
            (y, x)
            uint8
            dask.array<chunksize=(1830, 1830), meta=np.ndarray>

            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b04'}

            dtype :

            <u1

            flag_meanings :

            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

            Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

          • b08
            (y, x)
            uint8
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            detector footprint mask provided in the final reference frame (ground geometry)

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            detector footprint mask provided in the final reference frame (ground geometry)

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            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

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            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

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            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

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            _eopf_attrs :

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            dtype :

            <u1

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            D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

            flag_values :

            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

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            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_meanings': 'D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12', 'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_detfoo_b8a'}

            dtype :

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            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

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        <xarray.DatasetView> Size: 10MB
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        r60m

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            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of the image at 60m in meters from up-left pixel

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        • Data variables: (3)
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            long_name :

            detector footprint mask provided in the final reference frame (ground geometry)

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          • b09
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            detector footprint mask provided in the final reference frame (ground geometry)

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          • b10
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            _eopf_attrs :

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            detector footprint mask provided in the final reference frame (ground geometry)

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      <xarray.DatasetView> Size: 0B
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      l1c_classification

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        Dimensions:  (y: 1830, x: 1830)
        Coordinates:
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        Data variables:
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        r60m

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        • Coordinates: (2)
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            dimensions :

            ['x']

            dtype :

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            long_name :

            x coordinates of the image at 60m in meters from up-left pixel

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          • y
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            dtype :

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        • Data variables: (1)
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            _eopf_attrs :

            {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4], 'flag_meanings': 'opaque cirrus snow_ice', 'long_name': 'cloud classification mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_cls_b00'}

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      l2a_classification

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          * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
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        r20m

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          • x: 5490
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            dtype :

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            long_name :

            x coordinates of the image at 20m in meters from up-left pixel

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          • y
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            dimensions :

            ['y']

            dtype :

            <f4

            long_name :

            y coordinates of the image at 20m in meters from up-left pixel

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            m

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        • Data variables: (1)
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            dtype :

            <u1

            long_name :

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        <xarray.DatasetView> Size: 27MB
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        r60m

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            3e+05 3.001e+05 ... 4.098e+05

            dimensions :

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            dtype :

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            long_name :

            x coordinates of the image at 60m in meters from up-left pixel

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          • y
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            long_name :

            y coordinates of the image at 60m in meters from up-left pixel

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    Data variables:
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    meteorology

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        * longitude  (longitude) float32 36B 6.457 6.634 6.811 ... 7.518 7.695 7.872
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          aod469     (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
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          omaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          ssaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          suaod550   (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          z          (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>

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          dtype :

          <f4

          long_name :

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          standard_name :

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          dimensions :

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          dtype :

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          long_name :

          longitudes of ECMWF grid

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          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 1240nm', 'short_name': 'aod1240'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 1240nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod469
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 469nm', 'short_name': 'aod469'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 469nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 550nm', 'short_name': 'aod550'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod670
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 670nm', 'short_name': 'aod670'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 670nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • aod865
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total aerosol optical thickness at 865nm', 'short_name': 'aod865'}

          dtype :

          <f4

          long_name :

          total aerosol optical thickness at 865nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • bcaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'black carbon aerosol optical thickness at 550nm', 'short_name': 'bcaod550'}

          dtype :

          <f4

          long_name :

          black carbon aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • duaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'dust aerosol optical thickness at 550nm', 'short_name': 'duaod550'}

          dtype :

          <f4

          long_name :

          dust aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • omaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'organic matter aerosol optical thickness at 550nm', 'short_name': 'omaod550'}

          dtype :

          <f4

          long_name :

          organic matter aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • ssaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'sea salt aerosol optical thickness at 550nm', 'short_name': 'ssaod550'}

          dtype :

          <f4

          long_name :

          sea salt aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • suaod550
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'sulphate aerosol optical thickness at 550nm', 'short_name': 'suaod550'}

          dtype :

          <f4

          long_name :

          sulphate aerosol optical thickness at 550nm

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • z
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'geopotential surface z', 'short_name': 'z', 'units': 'm2/s2'}

          dtype :

          <f4

          long_name :

          geopotential surface z

          units :

          m2/s2

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 2kB
      Dimensions:    (latitude: 9, longitude: 9)
      Coordinates:
        * latitude   (latitude) float32 36B 45.13 45.01 44.88 ... 44.4 44.28 44.16
        * longitude  (longitude) float32 36B 6.457 6.634 6.811 ... 7.518 7.695 7.872
      Data variables:
          msl        (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          r          (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          tco3       (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          tcwv       (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          u10        (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>
          v10        (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>

      ecmwf

      • Groups: (0)
      • Dimensions:
        • latitude: 9
        • longitude: 9
      • Coordinates: (2)
        • latitude
          (latitude)
          float32
          45.13 45.01 44.88 ... 44.28 44.16

          dimensions :

          ['latitude']

          dtype :

          <f4

          long_name :

          latitudes of ECMWF grid

          standard_name :

          latitude

          units :

          degrees_north

          array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4  , 44.279, 44.16 ],
                dtype=float32)

        • longitude
          (longitude)
          float32
          6.457 6.634 6.811 ... 7.695 7.872

          dimensions :

          ['longitude']

          dtype :

          <f4

          long_name :

          longitudes of ECMWF grid

          standard_name :

          longitude

          units :

          degrees_east

          array([6.457   , 6.633875, 6.81075 , 6.987625, 7.1645  , 7.341375, 7.51825 ,
                 7.695125, 7.872   ], dtype=float32)

      • Data variables: (6)
        • msl
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'mean sea level pressure', 'short_name': 'msl', 'units': 'hPa'}

          dtype :

          <f4

          long_name :

          mean sea level pressure

          units :

          hPa

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • r
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'relative humidity at surface pressure', 'short_name': 'r', 'units': '%'}

          dtype :

          <f4

          long_name :

          relative humidity at surface pressure

          units :

          %

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • tco3
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total column ozone', 'short_name': 'tco3', 'units': 'kg/m2'}

          dtype :

          <f4

          long_name :

          total column ozone

          units :

          kg/m2

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • tcwv
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': 'total column vertically-integrated water vapour', 'short_name': 'tcwv', 'units': 'kg/m2'}

          dtype :

          <f4

          long_name :

          total column vertically-integrated water vapour

          units :

          kg/m2

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • u10
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': '10m eastward wind component', 'short_name': 'u10', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          10m eastward wind component

          units :

          m/s

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

        • v10
          (latitude, longitude)
          float32
          dask.array<chunksize=(9, 9), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['latitude', 'longitude'], 'dimensions': ['latitude', 'longitude'], 'dtype': '<f4', 'long_name': '10m northward wind component', 'short_name': 'v10', 'units': 'm/s'}

          dtype :

          <f4

          long_name :

          10m northward wind component

          units :

          m/s

          Array Chunk Bytes 324 B 324 B Shape (9, 9) (9, 9) 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: (0)

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

  measurements

  • Groups: (1)
    <xarray.DatasetView> Size: 0B
    Dimensions:  ()
    Data variables:
        *empty*

    reflectance

    • Groups: (3)
      <xarray.DatasetView> Size: 4GB
      Dimensions:  (y: 10980, x: 10980)
      Coordinates:
        * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
          b02      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b03      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b04      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b08      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

      r10m

      • Groups: (0)
      • Dimensions:
        • y: 10980
        • x: 10980
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3e+05 ... 4.098e+05 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 10m in meters from up-left pixel

          units :

          m

          array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
                shape=(10980,), dtype=float32)

        • y
          (y)
          float32
          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 10m in meters from up-left pixel

          units :

          m

          array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
                shape=(10980,), dtype=float32)

      • Data variables: (4)
        • b02
          (y, x)
          float64
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 02 490 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b03
          (y, x)
          float64
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_r10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 03 560 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b04
          (y, x)
          float64
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_r10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 04 665 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b08
          (y, x)
          float64
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 08 842 nm', 'scale_factor': 0.0001, 'short_name': 'b08_r10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 08 842 nm

          units :

          digital_counts

          Array Chunk Bytes 919.80 MiB 25.55 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 2GB
      Dimensions:  (y: 5490, x: 5490)
      Coordinates:
        * x        (x) float32 22kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
          b01      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b02      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b03      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b04      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b05      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b06      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b07      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b11      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b12      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b8a      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

      • Groups: (0)
      • Dimensions:
        • y: 5490
        • x: 5490
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3e+05 ... 4.098e+05 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 20m in meters from up-left pixel

          units :

          m

          array([300010., 300030., 300050., ..., 409750., 409770., 409790.],
                shape=(5490,), dtype=float32)

        • y
          (y)
          float32
          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 20m in meters from up-left pixel

          units :

          m

          array([5000030., 5000010., 4999990., ..., 4890290., 4890270., 4890250.],
                shape=(5490,), dtype=float32)

      • Data variables: (10)
        • b01
          (y, x)
          float64
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 01 443 nm', 'scale_factor': 0.0001, 'short_name': 'b01_r20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 01 443 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b02
          (y, x)
          float64
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_r20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 02 490 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b03
          (y, x)
          float64
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_r20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 03 560 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b04
          (y, x)
          float64
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_r20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 04 665 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b05
          (y, x)
          float64
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 05 705 nm', 'scale_factor': 0.0001, 'short_name': 'b05_r20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 05 705 nm

          units :

          digital_counts

          Array Chunk Bytes 229.95 MiB 6.39 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b06
          (y, x)
          float64
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          _eopf_attrs :

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          dtype :

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          long_name :

          BOA reflectance from MSI acquisition at spectral band 07 783 nm

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          _eopf_attrs :

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          dtype :

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          long_name :

          BOA reflectance from MSI acquisition at spectral band 11 1610 nm

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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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          _eopf_attrs :

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          dtype :

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      • Attributes: (0)

      <xarray.DatasetView> Size: 295MB
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          b02      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b03      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b04      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b05      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b06      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b07      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
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          b8a      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>

      r60m

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          dimensions :

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          dtype :

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          long_name :

          BOA reflectance from MSI acquisition at spectral band 01 443 nm

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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

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          units :

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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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          units :

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        • b04
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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

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          units :

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          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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        • b06
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          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 06 740 nm', 'scale_factor': 0.0001, 'short_name': 'b06_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 06 740 nm

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          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • b07
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          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 07 783 nm', 'scale_factor': 0.0001, 'short_name': 'b07_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

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          _eopf_attrs :

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          dtype :

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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

          BOA reflectance from MSI acquisition at spectral band 11 1610 nm

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        • b12
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          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 12 2190 nm', 'scale_factor': 0.0001, 'short_name': 'b12_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

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        • b8a
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          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 8A 842 nm', 'scale_factor': 0.0001, 'short_name': 'b8a_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

          dtype :

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          long_name :

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      • Attributes: (0)

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

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

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

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

    • Groups: (3)
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      Coordinates:
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        * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
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          wvp      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

      r10m

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      • Dimensions:
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        • x: 10980
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          dimensions :

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          dtype :

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        • y
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          dimensions :

          ['y']

          dtype :

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          long_name :

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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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          _eopf_attrs :

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          dtype :

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          long_name :

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      • Attributes: (0)

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        * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
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          wvp      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

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      • Coordinates: (2)
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          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 20m in meters from up-left pixel

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        • y
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          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

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      • Data variables: (2)
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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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        • wvp
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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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      • Attributes: (0)

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      Coordinates:
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          wvp      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>

      r60m

      • Groups: (0)
      • Dimensions:
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        • x: 1830
      • Coordinates: (2)
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          (x)
          float32
          3e+05 3.001e+05 ... 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 60m in meters from up-left pixel

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          m

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                shape=(1830,), dtype=float32)

        • y
          (y)
          float32
          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 60m in meters from up-left pixel

          units :

          m

          array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
                shape=(1830,), dtype=float32)

      • Data variables: (2)
        • aot
          (y, x)
          float64
          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'Aerosol Optical Thickness map (at 550nm)', 'scale_factor': 0.001, 'short_name': 'aot_r60m', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          Aerosol Optical Thickness map (at 550nm)

          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

        • wvp
          (y, x)
          float64
          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'Water Vapour Content map at 60m', 'scale_factor': 0.001, 'short_name': 'wvp_r60m', 'units': 'cm', 'valid_max': 65535, 'valid_min': 1}

          dtype :

          <u2

          long_name :

          Water Vapour Content map at 60m

          units :

          cm

          Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

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

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

    • Groups: (3)
      <xarray.DatasetView> Size: 482MB
      Dimensions:  (y: 10980, x: 10980)
      Coordinates:
        * x        (x) float32 44kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 44kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
          b02      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b03      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b04      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
          b08      (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

      r10m

      • Groups: (0)
      • Dimensions:
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        • x: 10980
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3e+05 ... 4.098e+05 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 10m in meters from up-left pixel

          units :

          m

          array([300005., 300015., 300025., ..., 409775., 409785., 409795.],
                shape=(10980,), dtype=float32)

        • y
          (y)
          float32
          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 10m in meters from up-left pixel

          units :

          m

          array([5000035., 5000025., 5000015., ..., 4890265., 4890255., 4890245.],
                shape=(10980,), dtype=float32)

      • Data variables: (4)
        • b02
          (y, x)
          uint8
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b02'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b03
          (y, x)
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          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b03'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b04
          (y, x)
          uint8
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b04'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b08
          (y, x)
          uint8
          dask.array<chunksize=(1830, 1830), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b08'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (10980, 10980) (1830, 1830) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 181MB
      Dimensions:  (y: 5490, x: 5490)
      Coordinates:
        * x        (x) float32 22kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
          b05      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b06      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b07      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b11      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b12      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
          b8a      (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

      • Groups: (0)
      • Dimensions:
        • y: 5490
        • x: 5490
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3e+05 ... 4.098e+05 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 20m in meters from up-left pixel

          units :

          m

          array([300010., 300030., 300050., ..., 409750., 409770., 409790.],
                shape=(5490,), dtype=float32)

        • y
          (y)
          float32
          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 20m in meters from up-left pixel

          units :

          m

          array([5000030., 5000010., 4999990., ..., 4890290., 4890270., 4890250.],
                shape=(5490,), dtype=float32)

      • Data variables: (6)
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          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b05'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b06
          (y, x)
          uint8
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b06'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b07
          (y, x)
          uint8
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b07'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b11
          (y, x)
          uint8
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b11'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b12
          (y, x)
          uint8
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b12'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b8a
          (y, x)
          uint8
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b8a'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 28.74 MiB 817.60 kiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

      • Attributes: (0)

      <xarray.DatasetView> Size: 10MB
      Dimensions:  (y: 1830, x: 1830)
      Coordinates:
        * x        (x) float32 7kB 3e+05 3.001e+05 3.002e+05 ... 4.097e+05 4.098e+05
        * y        (y) float32 7kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
      Data variables:
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          b09      (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
          b10      (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>

      r60m

      • Groups: (0)
      • Dimensions:
        • y: 1830
        • x: 1830
      • Coordinates: (2)
        • x
          (x)
          float32
          3e+05 3.001e+05 ... 4.098e+05

          dimensions :

          ['x']

          dtype :

          <f4

          long_name :

          x coordinates of the image at 60m in meters from up-left pixel

          units :

          m

          array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
                shape=(1830,), dtype=float32)

        • y
          (y)
          float32
          5e+06 5e+06 ... 4.89e+06 4.89e+06

          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 60m in meters from up-left pixel

          units :

          m

          array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
                shape=(1830,), dtype=float32)

      • Data variables: (3)
        • b01
          (y, x)
          uint8
          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b01'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b09
          (y, x)
          uint8
          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b09'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • b10
          (y, x)
          uint8
          dask.array<chunksize=(305, 305), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], 'flag_meanings': 'ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD', 'long_name': 'quality mask provided in the final reference frame (ground geometry)', 'short_name': 'msk_quality_b10'}

          dtype :

          <u1

          flag_masks :

          [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

          flag_meanings :

          ANC_LOST ANC_DEG MSI_LOST MSI_DEG DEFECTIVE_PIXEL NODATA_PIXEL PARTIALLY_CORRECTED_PIXEL SATURATED_PIXEL_L1A SATURATED_PIXEL_L1B CLOUD

          long_name :

          quality mask provided in the final reference frame (ground geometry)

          Array Chunk Bytes 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

      • Attributes: (0)

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

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

    probability

    • Groups: (1)
      <xarray.DatasetView> Size: 241MB
      Dimensions:  (y: 5490, x: 5490)
      Coordinates:
        * x        (x) float32 22kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
        * y        (y) float32 22kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
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          snw      (y, x) float32 121MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

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      • Dimensions:
        • y: 5490
        • x: 5490
      • Coordinates: (2)
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          dimensions :

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          dtype :

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          x coordinates of the image at 20m in meters from up-left pixel

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        • y
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          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

          y coordinates of the image at 20m in meters from up-left pixel

          units :

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      • Data variables: (2)
        • cld
          (y, x)
          float32
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          _eopf_attrs :

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          dtype :

          <f4

          long_name :

          cloud probability mask at 20m

          units :

          percentage

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        • snw
          (y, x)
          float32
          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

          {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 101, 'long_name': 'snow probability mask at 20m', 'short_name': 'snw', 'units': 'percentage', 'valid_max': 100, 'valid_min': 0}

          dtype :

          <f4

          long_name :

          snow probability mask at 20m

          units :

          percentage

          Array Chunk Bytes 114.98 MiB 3.19 MiB Shape (5490, 5490) (915, 915) Dask graph 36 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

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

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

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

  other_metadata :

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  processing_history :

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'S2__OPER_GIP_L2ACAC_MPC__20220121T000004_V20220125T022000_21000101T000000_B00', 'S2__OPER_GIP_PROBA2_MPC__20260129T000512_V20260204T053000_21000101T000000_B00', 'GlobalSnowMap.tiff', 'ESACCI-LC-L4-WB-Map-150m-P13Y-2000-v4.0.tif', 'ESACCI-LC-L4-LCCS-Map-300m-P1Y-2015-v2.0.7.tif', 'ESACCI-LC-L4-Snow-Cond-500m-MONTHLY-2000-2012-v2.4'], 'facility': 'ESA', 'inputs': 'S02MSIL1C_etc', 'outputs': ['S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.SAFE'], 'processor': 'Sentinel-2 IPF', 'time': '2026-03-13T17:19:16.000000Z', 'version': '05.12'}, {'facility': 'CS-SopraSteria', 'inputs': ['S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.SAFE'], 'outputs': ['S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916.zarr.zarr'], 'processor': 'EOPF-CPM', 'time': '2026-03-13T23:24:30.329506Z', 'version': '2.6.2'}]}

  stac_discovery :

  {'assets': {'aod1240': {'href': '/conditions/meteorology/cams/aod1240', 'title': 'aod1240'}, 'aod469': {'href': '/conditions/meteorology/cams/aod469', 'title': 'aod469'}, 'aod550': {'href': '/conditions/meteorology/cams/aod550', 'title': 'aod550'}, 'aod670': {'href': '/conditions/meteorology/cams/aod670', 'title': 'aod670'}, 'aod865': {'href': '/conditions/meteorology/cams/aod865', 'title': 'aod865'}, 'aot': {'href': '/quality/atmosphere/r10m/aot', 'title': 'aot'}, 'aot_r20m': {'href': '/quality/atmosphere/r20m/aot', 'title': 'aot_r20m'}, 'aot_r60m': {'href': '/quality/atmosphere/r60m/aot', 'title': 'aot_r60m'}, 'b01_60m': {'href': '/measurements/reflectance/r60m/b01', 'title': 'b01_60m'}, 'b01_r20m': {'href': '/measurements/reflectance/r20m/b01', 'title': 'b01_r20m'}, 'b02_10m': {'href': '/measurements/reflectance/r10m/b02', 'title': 'b02_10m'}, 'b02_60m': {'href': '/measurements/reflectance/r60m/b02', 'title': 'b02_60m'}, 'b02_r20m': {'href': '/measurements/reflectance/r20m/b02', 'title': 'b02_r20m'}, 'b03_60m': {'href': '/measurements/reflectance/r60m/b03', 'title': 'b03_60m'}, 'b03_r10m': {'href': '/measurements/reflectance/r10m/b03', 'title': 'b03_r10m'}, 'b03_r20m': {'href': '/measurements/reflectance/r20m/b03', 'title': 'b03_r20m'}, 'b04_60m': {'href': '/measurements/reflectance/r60m/b04', 'title': 'b04_60m'}, 'b04_r10m': {'href': '/measurements/reflectance/r10m/b04', 'title': 'b04_r10m'}, 'b04_r20m': {'href': '/measurements/reflectance/r20m/b04', 'title': 'b04_r20m'}, 'b05_60m': {'href': '/measurements/reflectance/r60m/b05', 'title': 'b05_60m'}, 'b05_r20m': {'href': '/measurements/reflectance/r20m/b05', 'title': 'b05_r20m'}, 'b06_60m': {'href': '/measurements/reflectance/r60m/b06', 'title': 'b06_60m'}, 'b06_r20m': {'href': '/measurements/reflectance/r20m/b06', 'title': 'b06_r20m'}, 'b07_60m': {'href': '/measurements/reflectance/r60m/b07', 'title': 'b07_60m'}, 'b07_r20m': {'href': '/measurements/reflectance/r20m/b07', 'title': 'b07_r20m'}, 'b08_r10m': {'href': '/measurements/reflectance/r10m/b08', 'title': 'b08_r10m'}, 'b09_r60m': {'href': '/measurements/reflectance/r60m/b09', 'title': 'b09_r60m'}, 'b11_60m': {'href': '/measurements/reflectance/r60m/b11', 'title': 'b11_60m'}, 'b11_r20m': {'href': '/measurements/reflectance/r20m/b11', 'title': 'b11_r20m'}, 'b12_r20m': {'href': '/measurements/reflectance/r20m/b12', 'title': 'b12_r20m'}, 'b12_r60m': {'href': '/measurements/reflectance/r60m/b12', 'title': 'b12_r60m'}, 'b8a_r20m': {'href': '/measurements/reflectance/r20m/b8a', 'title': 'b8a_r20m'}, 'b8a_r60m': {'href': '/measurements/reflectance/r60m/b8a', 'title': 'b8a_r60m'}, 'bcaod550': {'href': '/conditions/meteorology/cams/bcaod550', 'title': 'bcaod550'}, 'cld': {'href': '/quality/probability/r20m/cld', 'title': 'cld'}, 'duaod550': {'href': '/conditions/meteorology/cams/duaod550', 'title': 'duaod550'}, 'mean_sun_angles': {'href': '/conditions/geometry/mean_sun_angles', 'title': 'mean_sun_angles'}, 'mean_viewing_incidence_angles': {'href': '/conditions/geometry/mean_viewing_incidence_angles', 'title': 'mean_viewing_incidence_angles'}, 'msk_cls_b00': {'href': '/conditions/mask/l1c_classification/r60m/b00', 'title': 'msk_cls_b00'}, 'msk_detfoo_b01': {'href': '/conditions/mask/detector_footprint/r60m/b01', 'title': 'msk_detfoo_b01'}, 'msk_detfoo_b02': {'href': '/conditions/mask/detector_footprint/r10m/b02', 'title': 'msk_detfoo_b02'}, 'msk_detfoo_b03': {'href': '/conditions/mask/detector_footprint/r10m/b03', 'title': 'msk_detfoo_b03'}, 'msk_detfoo_b04': {'href': '/conditions/mask/detector_footprint/r10m/b04', 'title': 'msk_detfoo_b04'}, 'msk_detfoo_b05': {'href': '/conditions/mask/detector_footprint/r20m/b05', 'title': 'msk_detfoo_b05'}, 'msk_detfoo_b06': {'href': '/conditions/mask/detector_footprint/r20m/b06', 'title': 'msk_detfoo_b06'}, 'msk_detfoo_b07': {'href': '/conditions/mask/detector_footprint/r20m/b07', 'title': 'msk_detfoo_b07'}, 'msk_detfoo_b08': {'href': '/conditions/mask/detector_footprint/r10m/b08', 'title': 'msk_detfoo_b08'}, 'msk_detfoo_b09': {'href': '/conditions/mask/detector_footprint/r60m/b09', 'title': 'msk_detfoo_b09'}, 'msk_detfoo_b10': {'href': '/conditions/mask/detector_footprint/r60m/b10', 'title': 'msk_detfoo_b10'}, 'msk_detfoo_b11': {'href': '/conditions/mask/detector_footprint/r20m/b11', 'title': 'msk_detfoo_b11'}, 'msk_detfoo_b12': {'href': '/conditions/mask/detector_footprint/r20m/b12', 'title': 'msk_detfoo_b12'}, 'msk_detfoo_b8a': {'href': '/conditions/mask/detector_footprint/r20m/b8a', 'title': 'msk_detfoo_b8a'}, 'msk_quality_b01': {'href': '/quality/mask/r60m/b01', 'title': 'msk_quality_b01'}, 'msk_quality_b02': {'href': '/quality/mask/r10m/b02', 'title': 'msk_quality_b02'}, 'msk_quality_b03': {'href': '/quality/mask/r10m/b03', 'title': 'msk_quality_b03'}, 'msk_quality_b04': {'href': '/quality/mask/r10m/b04', 'title': 'msk_quality_b04'}, 'msk_quality_b05': {'href': '/quality/mask/r20m/b05', 'title': 'msk_quality_b05'}, 'msk_quality_b06': {'href': '/quality/mask/r20m/b06', 'title': 'msk_quality_b06'}, 'msk_quality_b07': {'href': '/quality/mask/r20m/b07', 'title': 'msk_quality_b07'}, 'msk_quality_b08': {'href': '/quality/mask/r10m/b08', 'title': 'msk_quality_b08'}, 'msk_quality_b09': {'href': '/quality/mask/r60m/b09', 'title': 'msk_quality_b09'}, 'msk_quality_b10': {'href': '/quality/mask/r60m/b10', 'title': 'msk_quality_b10'}, 'msk_quality_b11': {'href': '/quality/mask/r20m/b11', 'title': 'msk_quality_b11'}, 'msk_quality_b12': {'href': '/quality/mask/r20m/b12', 'title': 'msk_quality_b12'}, 'msk_quality_b8a': {'href': '/quality/mask/r20m/b8a', 'title': 'msk_quality_b8a'}, 'msl': {'href': '/conditions/meteorology/ecmwf/msl', 'title': 'msl'}, 'omaod550': {'href': '/conditions/meteorology/cams/omaod550', 'title': 'omaod550'}, 'r': {'href': '/conditions/meteorology/ecmwf/r', 'title': 'r'}, 'scl_r20m': {'href': '/conditions/mask/l2a_classification/r20m/scl', 'title': 'scl_r20m'}, 'scl_r60m': {'href': '/conditions/mask/l2a_classification/r60m/scl', 'title': 'scl_r60m'}, 'snw': {'href': '/quality/probability/r20m/snw', 'title': 'snw'}, 'ssaod550': {'href': '/conditions/meteorology/cams/ssaod550', 'title': 'ssaod550'}, 'suaod550': {'href': '/conditions/meteorology/cams/suaod550', 'title': 'suaod550'}, 'sun_angles': {'href': '/conditions/geometry/sun_angles', 'title': 'sun_angles'}, 'tco3': {'href': '/conditions/meteorology/ecmwf/tco3', 'title': 'tco3'}, 'tcwv': {'href': '/conditions/meteorology/ecmwf/tcwv', 'title': 'tcwv'}, 'u10': {'href': '/conditions/meteorology/ecmwf/u10', 'title': 'u10'}, 'v10': {'href': '/conditions/meteorology/ecmwf/v10', 'title': 'v10'}, 'viewing_incidence_angles': {'href': '/conditions/geometry/viewing_incidence_angles', 'title': 'viewing_incidence_angles'}, 'wvc': {'href': '/quality/atmosphere/r10m/wvp', 'title': 'wvc'}, 'wvp_r20m': {'href': '/quality/atmosphere/r20m/wvp', 'title': 'wvp_r20m'}, 'wvp_r60m': {'href': '/quality/atmosphere/r60m/wvp', 'title': 'wvp_r60m'}, 'z': {'href': '/conditions/meteorology/cams/z', 'title': 'z'}}, 'bbox': [7.871918704100081, 44.14299463915654, 6.814260663879186, 45.148073182871244], 'geometry': {'coordinates': [[[6.814260663879186, 44.14299463915654], [6.862849633524148, 44.28854980816078], [6.913542734971571, 44.434750348537925], [6.962313790019774, 44.58155306058976], [7.010955757503, 44.728499728638816], [7.060405299364974, 44.875326118891536], [7.107587454389481, 45.02285613715742], [7.146790489526708, 45.1366650993253], [7.852592618382666, 45.148073182871244], [7.871918704100081, 44.15979663371134], [6.814260663879186, 44.14299463915654]]], 'type': 'Polygon'}, 'id': 'S02MSIL2A_20260313T101741_0000_A065_T081', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4427, 'eo:common_name': 'coastal', 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1884.69, 'name': '01', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 0.49269999999999997, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.065, 'eo:solar_illumination': 1959.66, 'name': '02', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.5598, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.035, 'eo:solar_illumination': 1823.24, 'name': '03', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.6646000000000001, 'eo:common_name': 'red', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 1512.06, 'name': '04', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.7041000000000001, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1424.64, 'name': '05', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7405, 'eo:full_width_half_max': 0.015, 'eo:solar_illumination': 1287.61, 'name': '06', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.7827999999999999, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 1162.08, 'name': '07', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.8328, 'eo:common_name': 'nir', 'eo:full_width_half_max': 0.105, 'eo:solar_illumination': 1041.63, 'name': '08', 'raster:spatial_resolution': '10'}, {'eo:center_wavelength': 0.8647, 'eo:full_width_half_max': 0.02, 'name': '8A', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 0.9451, 'eo:full_width_half_max': 0.02, 'eo:solar_illumination': 812.92, 'name': '09', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.3735, 'eo:common_name': 'cirrus', 'eo:full_width_half_max': 0.03, 'eo:solar_illumination': 367.15, 'name': '10', 'raster:spatial_resolution': '60'}, {'eo:center_wavelength': 1.6137000000000001, 'eo:common_name': 'swir16', 'eo:full_width_half_max': 0.09, 'eo:solar_illumination': 245.59, 'name': '11', 'raster:spatial_resolution': '20'}, {'eo:center_wavelength': 2.2024000000000004, 'eo:common_name': 'swir22', 'eo:full_width_half_max': 0.18, 'name': '12', 'raster:spatial_resolution': '20'}], 'constellation': 'sentinel-2', 'created': '2026-03-13T17:19:16.000000Z', 'datetime': '2026-03-13T10:17:41.024000Z', 'end_datetime': '2026-03-13T10:17:41.024Z', 'eo:cloud_cover': 16.11875, 'eo:snow_cover': 21.842474, 'eopf:datastrip_id': 'S2A_OPER_MSI_L2A_DS_2APS_20260313T171916_S20260313T102456_N05.12', 'eopf:datatake_id': 'GS2A_20260313T101741_056004_N05.12', 'eopf:instrument_mode': 'INS-NOBS', 'instruments': ['msi'], 'platform': 'sentinel-2a', 'processing:facility': 'ESA', 'processing:level': 'L2A', 'processing:lineage': 'systematic', 'processing:version': '05.12', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT', 'product:type': 'S02MSIL2A', 'providers': [{'name': 'L2A processor', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 56004, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2015-028A', 'sat:relative_orbit': 65, 'sci:doi': '10.5270/S2_-znk9xsj', 'start_datetime': '2026-03-13T10:17:41.024Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/raster/v2.0.0/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/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

We can plot the RGB image, the SCL (Scene Classification Layer), the AOT (Aerosol Optical Thickness) map, and the WVP (Water Vapor Content) map at 60 m resolution.

In [16]:

fig, ax = plt.subplots(1, 4, figsize=(20, 5))
dt.measurements.reflectance.r60m.b04.plot.imshow(ax=ax[0], vmin=0., vmax=1.)
dt.conditions.mask.l2a_classification.r60m.scl.plot.imshow(ax=ax[1])
dt.quality.atmosphere.r60m.aot.plot.imshow(ax=ax[2])
dt.quality.atmosphere.r60m.wvp.plot.imshow(ax=ax[3])
plt.tight_layout()

fig, ax = plt.subplots(1, 4, figsize=(20, 5)) dt.measurements.reflectance.r60m.b04.plot.imshow(ax=ax[0], vmin=0., vmax=1.) dt.conditions.mask.l2a_classification.r60m.scl.plot.imshow(ax=ax[1]) dt.quality.atmosphere.r60m.aot.plot.imshow(ax=ax[2]) dt.quality.atmosphere.r60m.wvp.plot.imshow(ax=ax[3]) plt.tight_layout()

Open Sentinel-2 Level-2A Reflectance Groups as xarray.Dataset¶

Similarly, we can open the individual reflectance groups at 10 m (SR_10m), 20 m (SR_20m), and 60 m (SR_60m) resolution as xarray.Dataset objects.

The opening parameters are stored in the asset’s extra field "xarray:open_dataset_kwargs". Note that in Level-2A, some of the native bands are resampled to coarser resoltion. This is idendical to the previous SAFE format.

In [17]:

ds = xr.open_dataset(
    item.assets["SR_60m"].href,
    **item.assets["SR_60m"].extra_fields["xarray:open_dataset_kwargs"]
)
ds

ds = xr.open_dataset( item.assets["SR_60m"].href, **item.assets["SR_60m"].extra_fields["xarray:open_dataset_kwargs"] ) ds

Out[17]:

<xarray.Dataset> Size: 295MB
Dimensions:  (y: 1830, x: 1830)
Coordinates:
  * x        (x) float32 7kB 3e+05 3.001e+05 3.002e+05 ... 4.097e+05 4.098e+05
  * y        (y) float32 7kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06 4.89e+06
Data variables:
    b01      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b02      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b03      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b04      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b05      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b06      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b07      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b09      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b11      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b12      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    b8a      (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • y: 1830
  • x: 1830
• Coordinates: (2)
  • x
    (x)
    float32
    3e+05 3.001e+05 ... 4.098e+05

    dimensions :

    ['x']

    dtype :

    <f4

    long_name :

    x coordinates of the image at 60m in meters from up-left pixel

    units :

    m

    array([300030., 300090., 300150., ..., 409650., 409710., 409770.],
          shape=(1830,), dtype=float32)

  • y
    (y)
    float32
    5e+06 5e+06 ... 4.89e+06 4.89e+06

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

    y coordinates of the image at 60m in meters from up-left pixel

    units :

    m

    array([5000010., 4999950., 4999890., ..., 4890390., 4890330., 4890270.],
          shape=(1830,), dtype=float32)

• Data variables: (11)
  • b01
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 01 443 nm', 'scale_factor': 0.0001, 'short_name': 'b01_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 01 443 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b02
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b03
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b04
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b05
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 05 705 nm', 'scale_factor': 0.0001, 'short_name': 'b05_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 05 705 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b06
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 06 740 nm', 'scale_factor': 0.0001, 'short_name': 'b06_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 06 740 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b07
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 07 783 nm', 'scale_factor': 0.0001, 'short_name': 'b07_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 07 783 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b09
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 09 940 nm', 'scale_factor': 0.0001, 'short_name': 'b09_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 09 940 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b11
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 11 1610 nm', 'scale_factor': 0.0001, 'short_name': 'b11_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 11 1610 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b12
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 12 2190 nm', 'scale_factor': 0.0001, 'short_name': 'b12_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 12 2190 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b8a
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.1, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 0, 'long_name': 'BOA reflectance from MSI acquisition at spectral band 8A 842 nm', 'scale_factor': 0.0001, 'short_name': 'b8a_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA reflectance from MSI acquisition at spectral band 8A 842 nm

    units :

    digital_counts

    Array Chunk Bytes 25.55 MiB 726.76 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([300030.0, 300090.0, 300150.0, 300210.0, 300270.0, 300330.0, 300390.0,
           300450.0, 300510.0, 300570.0,
           ...
           409230.0, 409290.0, 409350.0, 409410.0, 409470.0, 409530.0, 409590.0,
           409650.0, 409710.0, 409770.0],
          dtype='float32', name='x', length=1830))

  • y
    PandasIndex

    PandasIndex(Index([5000010.0, 4999950.0, 4999890.0, 4999830.0, 4999770.0, 4999710.0,
           4999650.0, 4999590.0, 4999530.0, 4999470.0,
           ...
           4890810.0, 4890750.0, 4890690.0, 4890630.0, 4890570.0, 4890510.0,
           4890450.0, 4890390.0, 4890330.0, 4890270.0],
          dtype='float32', name='y', length=1830))

• Attributes: (0)

All other data access methods shown for Sentinel Level-1C products apply equally to Level-2A products.

---

Conclusion¶

This notebook demonstrates how to access Sentinel-2 EOPF Zarr samples in native mode using the xarray-eopf plugin. Key takeaways are:

• Access Sentinel-2 Level-1C and Level-2A products.
• Open the full Zarr store as an xr.DataTree using xr.open_dataset and the asset "product".
• Open subgroups (e.g., spectral reflectance at 10 m, 20 m, or 60 m) 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.
• Opening parameters are integrated in STAC assets.

Note: This notebook only covers the native mode, which presents the data as close as possible to the original product.
For an analysis-ready view, see the Sentinel-2 analysis mode notebook.