Intoduction to the xarray EOPF backend¶

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.

This notebook demonstrates how to use the xarray-eopf backend to access EOPF Zarr datasets. It highlights the key features currently supported by the backend.

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

---

Install the xarray-eopf Backend¶

The backend is implemented as an xarray plugin and can be installed using either pip or conda/mamba from the conda-forge channel.

• 📦 PyPI: xarray-eopf on PyPI pip install xarray-eopf

• 🐍 Conda (conda-forge): xarray-eopf on Anaconda conda install -c conda-forge xarray-eopf

  You can also use Mamba as a faster alternative to Conda: mamba install -c conda-forge xarray-eopf

---

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 pystac_client
import xarray as xr

import pystac_client import xarray as xr

---

Main Features of the xarray-eopf Backend¶

The xarray-eopf backend for EOPF data products can be selecterd by setting engine="eopf-zarr" in xarray.open_dataset(..) and xarray.open_datatree(..) method. All data access is lazy, meaning that data is only loaded when required—for example, during plotting or when writing to storage. It supports two modes of operation:

• Analysis Mode (default)
• Native Mode

Native Mode¶

Represents EOPF products without modification using xarray's DataTree and Dataset.

• open_dataset(path, engine="eopf-zarr", op_mode="native", chunks={}) — Returns a flattened version of the data tree
• open_datatree(path, engine="eopf-zarr", op_mode="native", chunks={}) — Returns the full DataTree, same as xr.open_datatree(.., engine="zarr")

Analysis Mode¶

Provides an analysis-ready, resampled view of the data (currently for Sentinel-2 only).

• open_dataset(path, engine="eopf-zarr", op_mode="analysis", chunks={}) — Loads Sentinel-2 products in a harmonized, analysis-ready format
• open_datatree(path, engine="eopf-zarr", op_mode="analysis", chunks={}) — Not implemented in this mode (NotImplementedError)

In the following sections, we demonstrate these functions using a Sentinel-2 L2A product.

For additional examples, refer to the notebooks provided for each Sentinel-1, Sentinel-2, and Sentinel-3 product, available in both native and analysis modes.

The native mode operates identically across all Sentinel missions. In contrast, the analysis mode is customized for each individual mission, as illustrated in the respective notebook examples.

Further information about the analysis mode for each Sentinel mission can be found in the xarray-eopf Guide.

---

Find a Sentinel Zarr Sample via STAC¶

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

In [2]:

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[2]:

[<Item id=S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916>]

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

In [3]:

item = items[0]
item

item = items[0] item

Out[3]:

<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

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        • 0 6.814260663879186

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• bbox[] 4 items

  • 0 6.814260663879186

  • 1 44.14299463915654

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  • 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"
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      • name "European Commission"
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      • url "https://zarr.eopf.copernicus.eu/"
      • name "EOPF Sentinel Zarr Samples Service"
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        • 1 "processor"
  • published "2026-03-13T23:25:58.728006Z"
  • deprecated False
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    • 0 "msi"
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    • lat 44.61398
    • lon 7.41649
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  • 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

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    • 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

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    • type "application/vnd+zarr"
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      • 0

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      • 1

        • name "B03"
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        • name "B04"
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      • 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
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        • 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
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      • 6

        • name "B07"
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        • name "B8A"
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        • name "B11"
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        • eo:center_wavelength 2.19
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    • xarray:open_dataset_kwargs

      • chunks
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      • op_mode "native"
    • roles[] 3 items

      • 0 "data"

      • 1 "reflectance"

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    • 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
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    • data_type "uint16"
    • raster:scale 0.001
    • raster:offset 0
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      • 0 "data"
  • B01_20m

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    • 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
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      • 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"

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    • 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"
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    • raster:offset -0.1
    • roles[] 2 items

      • 0 "data"

      • 1 "reflectance"
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    • 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"
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    • 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"
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    • raster:offset -0.1
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      • 0 "data"

      • 1 "reflectance"
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    • 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 in Native Mode as DataTree¶

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

In [4]:

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']

        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
            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

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        • Dimensions:
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          • x: 5490
        • Coordinates: (2)
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            (x)
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            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)
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            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.],
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        • Data variables: (6)
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            _eopf_attrs :

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            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:
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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_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 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: (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 mask 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 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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      • Attributes: (0)

      <xarray.DatasetView> Size: 0B
      Dimensions:  ()
      Data variables:
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      l2a_classification

      • Groups: (2)
        <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
        Data variables:
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        r20m

        • Groups: (0)
        • Dimensions:
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          • 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: (1)
          • scl
            (y, x)
            float64
            dask.array<chunksize=(915, 915), meta=np.ndarray>

            _eopf_attrs :

            {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<i8', 'fill_value': 0, 'long_name': 'scene classification map at 20m', 'scale_factor': 1, 'short_name': 'scl_r20m', 'valid_max': 65535, 'valid_min': 1}

            dtype :

            <u1

            long_name :

            scene classification map at 20m

            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: 27MB
        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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        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 :

            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: (1)
          • scl
            (y, x)
            float64
            dask.array<chunksize=(305, 305), meta=np.ndarray>

            _eopf_attrs :

            {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<i8', 'fill_value': 0, 'long_name': 'scene classification map', 'scale_factor': 1, 'short_name': 'scl_r60m', 'valid_max': 65535, 'valid_min': 1}

            dtype :

            <u1

            long_name :

            scene classification map

            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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      • Attributes: (0)

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

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

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        • 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 ],
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        • 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

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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)
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          (y, x)
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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_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

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        • 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)
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          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:
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          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)
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          (y, x)
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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 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 :

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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

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

          _eopf_attrs :

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          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
          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 06 740 nm', 'scale_factor': 0.0001, 'short_name': 'b06_r20m', '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 :

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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

        • b07
          (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': 'BOA reflectance from MSI acquisition at spectral band 07 783 nm', 'scale_factor': 0.0001, 'short_name': 'b07_r20m', '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 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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          dtype :

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          dtype :

          <u2

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          dtype :

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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>
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      r60m

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          dtype :

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          long_name :

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          _eopf_attrs :

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          dtype :

          <u2

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          dtype :

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          dtype :

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          dtype :

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          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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          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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          _eopf_attrs :

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          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)
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  <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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          wvp      (y, x) float64 964MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>

      r10m

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      • Dimensions:
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        • y
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          dimensions :

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          dtype :

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          _eopf_attrs :

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          dtype :

          <u2

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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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          wvp      (y, x) float64 241MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

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          dimensions :

          ['x']

          dtype :

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          long_name :

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        • y
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          dimensions :

          ['y']

          dtype :

          <f4

          long_name :

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          units :

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      • Data variables: (2)
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          dask.array<chunksize=(915, 915), meta=np.ndarray>

          _eopf_attrs :

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          dtype :

          <u2

          long_name :

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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

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      • Dimensions:
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        • x: 1830
      • Coordinates: (2)
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          dimensions :

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          dtype :

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        • y
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          dtype :

          <u2

          long_name :

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        • wvp
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          dtype :

          <u2

          long_name :

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          units :

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    mask

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      r10m

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          dimensions :

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          long_name :

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          dimensions :

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          dtype :

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          long_name :

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

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        • b03
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          dtype :

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          long_name :

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

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        • b04
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          long_name :

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

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        • b08
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          dtype :

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          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

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          long_name :

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

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        • b06
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          long_name :

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

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        • b07
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          long_name :

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

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          long_name :

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

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          dtype :

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          long_name :

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

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          dtype :

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          long_name :

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      r60m

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          dimensions :

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          dtype :

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          long_name :

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        • y
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          dimensions :

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          dtype :

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

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          dtype :

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          long_name :

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

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        • b10
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          _eopf_attrs :

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          long_name :

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

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    Data variables:
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    probability

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          snw      (y, x) float32 121MB dask.array<chunksize=(915, 915), meta=np.ndarray>

      r20m

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        • y
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      • Data variables: (2)
        • cld
          (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': 'cloud probability mask at 20m', 'short_name': 'cld', 'units': 'percentage', 'valid_max': 100, 'valid_min': 0}

          dtype :

          <f4

          long_name :

          cloud 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

        • 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:
    • Coordinates: (0)
    • 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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  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'}

As an example, we plot the red band (b04) at 60 meters resolution, which will trigger loading and visualization of the data.

In [5]:

dt.measurements.reflectance.r60m.b04.plot.imshow(vmin=0, vmax=1)

dt.measurements.reflectance.r60m.b04.plot.imshow(vmin=0, vmax=1)

Out[5]:

<matplotlib.image.AxesImage at 0x7bb320441160>

Open Sentinel-2 Level-2A Reflectance Groups in Native Mode as Dataset¶

Similarly, we can open the individual reflectance groups at 10 m (asset "SR_10m"), 20 m (asset "SR_20m"), and 60 m (asset "SR_60m") to access each group as an xarray.Dataset, as shown below:

In [6]:

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[6]:

<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)

We can plot an RGB image as an example, using the red (b04), green (b03), and blue (b02) spectral bands.

In [7]:

ax = (ds[["b04", "b03", "b02"]].to_dataarray(dim="band") / 0.3).clip(0, 1).plot.imshow(rgb="band")
ax.axes.set_aspect("equal")

ax = (ds[["b04", "b03", "b02"]].to_dataarray(dim="band") / 0.3).clip(0, 1).plot.imshow(rgb="band") ax.axes.set_aspect("equal")

Open Sentinel-2 Level-2A in Native Mode as Dataset¶

The xarray.DataTree data model is relatively new, introduced in xarray v2024.10.0 (October 2024). To support compatibility with existing workflows that rely on the traditional xr.Dataset model, we provide the function xarray.open_dataset(path, engine="eopf-zarr", op_mode="native", **kwargs). This function flattens the DataTree structure and returns a single xr.Dataset.

In this process, hierarchical groups within the Zarr product are removed by converting their contents into standalone datasets and merging them into one. To ensure uniqueness, variable and dimension names are prefixed with their original group paths, using an underscore (_) as the default separator. For example, a variable named b02 located in the group measurements/reflectance/r10m will be renamed to measurements_reflectance_r10m_b02 in the returned dataset.

In [8]:

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[8]:

<xarray.Dataset> Size: 11GB
Dimensions:                                            (
                                                        conditions_geometry_angle: 2,
                                                        conditions_geometry_band: 13,
                                                        conditions_geometry_y: 23,
                                                        conditions_geometry_x: 23,
                                                        conditions_geometry_detector: 4,
                                                        ...
                                                        quality_mask_r20m_y: 5490,
                                                        quality_mask_r20m_x: 5490,
                                                        quality_mask_r60m_y: 1830,
                                                        quality_mask_r60m_x: 1830,
                                                        quality_probability_y: 5490,
                                                        quality_probability_x: 5490)
Coordinates: (12/41)
  * 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_mask_r20m_x                                (quality_mask_r20m_x) float32 22kB ...
  * quality_mask_r20m_y                                (quality_mask_r20m_y) float32 22kB ...
  * quality_mask_r60m_x                                (quality_mask_r60m_x) float32 7kB ...
  * quality_mask_r60m_y                                (quality_mask_r60m_y) float32 7kB ...
  * quality_probability_x                              (quality_probability_x) float32 22kB ...
  * quality_probability_y                              (quality_probability_y) float32 22kB ...
Data variables: (12/83)
    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_mask_r20m_b8a                              (quality_mask_r20m_y, quality_mask_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>
    quality_mask_r60m_b01                              (quality_mask_r60m_y, quality_mask_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    quality_mask_r60m_b09                              (quality_mask_r60m_y, quality_mask_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    quality_mask_r60m_b10                              (quality_mask_r60m_y, quality_mask_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    quality_probability_cld                            (quality_probability_y, quality_probability_x) float32 121MB dask.array<chunksize=(915, 915), meta=np.ndarray>
    quality_probability_snw                            (quality_probability_y, quality_probability_x) float32 121MB dask.array<chunksize=(915, 915), 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_mask_l2a_classification_r20m_y: 5490
  • conditions_mask_l2a_classification_r20m_x: 5490
  • conditions_mask_l2a_classification_r60m_y: 1830
  • conditions_mask_l2a_classification_r60m_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_atmosphere_r10m_y: 10980
  • quality_atmosphere_r10m_x: 10980
  • quality_atmosphere_r20m_y: 5490
  • quality_atmosphere_r20m_x: 5490
  • quality_atmosphere_r60m_y: 1830
  • quality_atmosphere_r60m_x: 1830
  • quality_mask_r10m_y: 10980
  • quality_mask_r10m_x: 10980
  • quality_mask_r20m_y: 5490
  • quality_mask_r20m_x: 5490
  • quality_mask_r60m_y: 1830
  • quality_mask_r60m_x: 1830
  • quality_probability_y: 5490
  • quality_probability_x: 5490
• Coordinates: (41)
  • conditions_geometry_angle
    (conditions_geometry_angle)
    <U7
    'zenith' 'azimuth'

    dimensions :

    ['angle']

    dtype :

    <U7

    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

    fill_value :

    0

    long_name :

    detector module number

    valid_max :

    12

    valid_min :

    1

    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 the 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 the 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 the 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 the 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 the 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 the 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 the 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 the image at 60m in meters from up-left pixel

    units :

    m

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

  • conditions_mask_l2a_classification_r20m_x
    (conditions_mask_l2a_classification_r20m_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)

  • conditions_mask_l2a_classification_r20m_y
    (conditions_mask_l2a_classification_r20m_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)

  • conditions_mask_l2a_classification_r60m_x
    (conditions_mask_l2a_classification_r60m_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)

  • conditions_mask_l2a_classification_r60m_y
    (conditions_mask_l2a_classification_r60m_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)

  • 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 the 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 the 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 the 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 the 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 the 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 the image at 60m in meters from up-left pixel

    units :

    m

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

  • quality_atmosphere_r10m_x
    (quality_atmosphere_r10m_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)

  • quality_atmosphere_r10m_y
    (quality_atmosphere_r10m_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)

  • quality_atmosphere_r20m_x
    (quality_atmosphere_r20m_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)

  • quality_atmosphere_r20m_y
    (quality_atmosphere_r20m_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)

  • quality_atmosphere_r60m_x
    (quality_atmosphere_r60m_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)

  • quality_atmosphere_r60m_y
    (quality_atmosphere_r60m_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)

  • quality_mask_r10m_x
    (quality_mask_r10m_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)

  • quality_mask_r10m_y
    (quality_mask_r10m_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)

  • quality_mask_r20m_x
    (quality_mask_r20m_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)

  • quality_mask_r20m_y
    (quality_mask_r20m_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)

  • quality_mask_r60m_x
    (quality_mask_r60m_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)

  • quality_mask_r60m_y
    (quality_mask_r60m_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)

  • quality_probability_x
    (quality_probability_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)

  • quality_probability_y
    (quality_probability_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: (83)
  • 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': ['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

  • 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 mask 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 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_l2a_classification_r20m_scl
    (conditions_mask_l2a_classification_r20m_y, conditions_mask_l2a_classification_r20m_x)
    float64
    dask.array<chunksize=(915, 915), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<i8', 'fill_value': 0, 'long_name': 'scene classification map at 20m', 'scale_factor': 1, 'short_name': 'scl_r20m', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u1

    long_name :

    scene classification map at 20m

    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

  • conditions_mask_l2a_classification_r60m_scl
    (conditions_mask_l2a_classification_r60m_y, conditions_mask_l2a_classification_r60m_x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<i8', 'fill_value': 0, 'long_name': 'scene classification map', 'scale_factor': 1, 'short_name': 'scl_r60m', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u1

    long_name :

    scene classification map

    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

  • 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', '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

  • 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', '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

  • 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', '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

  • 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': '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

  • 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': '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

  • 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': '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

  • 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': '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

  • measurements_r20m_b01
    (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': '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

  • measurements_r20m_b02
    (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': '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

  • measurements_r20m_b03
    (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': '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

  • measurements_r20m_b04
    (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': '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

  • 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': '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

  • 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': 'BOA reflectance from MSI acquisition at spectral band 06 740 nm', 'scale_factor': 0.0001, 'short_name': 'b06_r20m', '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 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': 'BOA reflectance from MSI acquisition at spectral band 07 783 nm', 'scale_factor': 0.0001, 'short_name': 'b07_r20m', '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 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': 'BOA reflectance from MSI acquisition at spectral band 11 1610 nm', 'scale_factor': 0.0001, 'short_name': 'b11_r20m', '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 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': 'BOA reflectance from MSI acquisition at spectral band 12 2190 nm', 'scale_factor': 0.0001, 'short_name': 'b12_r20m', '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 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': 'BOA reflectance from MSI acquisition at spectral band 8A 865 nm', 'scale_factor': 0.0001, 'short_name': 'b8a_r20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    BOA 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': '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

  • measurements_r60m_b02
    (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': '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

  • measurements_r60m_b03
    (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': '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

  • measurements_r60m_b04
    (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': '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

  • measurements_r60m_b05
    (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': '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

  • measurements_r60m_b06
    (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': '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

  • measurements_r60m_b07
    (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': '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

  • 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': '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

  • measurements_r60m_b11
    (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': '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

  • measurements_r60m_b12
    (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': '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

  • measurements_r60m_b8a
    (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': '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

  • quality_atmosphere_r10m_aot
    (quality_atmosphere_r10m_y, quality_atmosphere_r10m_x)
    float64
    dask.array<chunksize=(1830, 1830), 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 10m (for 550nm) resampled from 20m AOT map', 'scale_factor': 0.001, 'short_name': 'aot', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    Aerosol Optical Thickness map at 10m (for 550nm) resampled from 20m AOT map

    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

  • quality_atmosphere_r10m_wvp
    (quality_atmosphere_r10m_y, quality_atmosphere_r10m_x)
    float64
    dask.array<chunksize=(1830, 1830), 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 10m', 'scale_factor': 0.001, 'short_name': 'wvc', 'units': 'cm', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    Water Vapour Content map at 10m

    units :

    cm

    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

  • quality_atmosphere_r20m_aot
    (quality_atmosphere_r20m_y, quality_atmosphere_r20m_x)
    float64
    dask.array<chunksize=(915, 915), 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 20m (for 550nm)', 'scale_factor': 0.001, 'short_name': 'aot_r20m', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    Aerosol Optical Thickness map at 20m (for 550nm)

    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

  • quality_atmosphere_r20m_wvp
    (quality_atmosphere_r20m_y, quality_atmosphere_r20m_x)
    float64
    dask.array<chunksize=(915, 915), 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 20m', 'scale_factor': 0.001, 'short_name': 'wvp_r20m', 'units': 'cm', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    Water Vapour Content map at 20m

    units :

    cm

    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

  • quality_atmosphere_r60m_aot
    (quality_atmosphere_r60m_y, quality_atmosphere_r60m_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

  • quality_atmosphere_r60m_wvp
    (quality_atmosphere_r60m_y, quality_atmosphere_r60m_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

  • quality_mask_r10m_b02
    (quality_mask_r10m_y, quality_mask_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_mask_r10m_b03
    (quality_mask_r10m_y, quality_mask_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_mask_r10m_b04
    (quality_mask_r10m_y, quality_mask_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_mask_r10m_b08
    (quality_mask_r10m_y, quality_mask_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_mask_r20m_b05
    (quality_mask_r20m_y, quality_mask_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_mask_r20m_b06
    (quality_mask_r20m_y, quality_mask_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_mask_r20m_b07
    (quality_mask_r20m_y, quality_mask_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_mask_r20m_b11
    (quality_mask_r20m_y, quality_mask_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_mask_r20m_b12
    (quality_mask_r20m_y, quality_mask_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_mask_r20m_b8a
    (quality_mask_r20m_y, quality_mask_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_mask_r60m_b01
    (quality_mask_r60m_y, quality_mask_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_mask_r60m_b09
    (quality_mask_r60m_y, quality_mask_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_mask_r60m_b10
    (quality_mask_r60m_y, quality_mask_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

  • quality_probability_cld
    (quality_probability_y, quality_probability_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': 'cloud probability mask at 20m', 'short_name': 'cld', 'units': 'percentage', 'valid_max': 100, 'valid_min': 0}

    dtype :

    <f4

    long_name :

    cloud 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

  • quality_probability_snw
    (quality_probability_y, quality_probability_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

• Indexes: (41)
  • 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_mask_l2a_classification_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_l2a_classification_r20m_x', length=5490))

  • conditions_mask_l2a_classification_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_l2a_classification_r20m_y', length=5490))

  • conditions_mask_l2a_classification_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_l2a_classification_r60m_x', length=1830))

  • conditions_mask_l2a_classification_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_l2a_classification_r60m_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_atmosphere_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_atmosphere_r10m_x', length=10980))

  • quality_atmosphere_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_atmosphere_r10m_y', length=10980))

  • quality_atmosphere_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_atmosphere_r20m_x', length=5490))

  • quality_atmosphere_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_atmosphere_r20m_y', length=5490))

  • quality_atmosphere_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_atmosphere_r60m_x', length=1830))

  • quality_atmosphere_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_atmosphere_r60m_y', length=1830))

  • quality_mask_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_mask_r10m_x', length=10980))

  • quality_mask_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_mask_r10m_y', length=10980))

  • quality_mask_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_mask_r20m_x', length=5490))

  • quality_mask_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_mask_r20m_y', length=5490))

  • quality_mask_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_mask_r60m_x', length=1830))

  • quality_mask_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_mask_r60m_y', length=1830))

  • quality_probability_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_probability_x', length=5490))

  • quality_probability_y
    PandasIndex

    PandasIndex(Index([5000030.0, 5000010.0, 4999990.0, 4999970.0, 4999950.0, 4999930.0,
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           4890310.0, 4890290.0, 4890270.0, 4890250.0],
          dtype='float32', name='quality_probability_y', length=5490))

• Attributes: (3)

  other_metadata :

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0.00108149', 'units': 'nm', 'wavelength_max': 881.0, 'wavelength_min': 837.0}}, 'declared_accuracy_of_AOT_model': 0.0, 'declared_accuracy_of_radiative_transfer_model': 0.0, 'declared_accuracy_of_water_vapour_model': 0.0, 'electronic_crosstalk_correction_flag': 'null', 'eopf_category': 'eoproduct', 'geometric_refinement': {'mean_value_of_residual_displacements_at_all_tie_points_after_refinement_m': {'x_mean': 'null', 'y_mean': 'null'}, 'spacecraft_rotation': {'X': {'coefficients': 'null', 'degree': 'null'}, 'Y': {'coefficients': 'null', 'degree': 'null'}, 'Z': {'coefficients': 'null', 'degree': 'null'}}, 'standard_deviation_of_residual_displacements_at_all_tie_points_after_refinement_m': {'x_stdv': 'null', 'y_stdv': 'null'}}, 'horizontal_CRS_code': 'EPSG:32632', 'horizontal_CRS_name': 'WGS84 / UTM zone 32N', 'mean_sun_azimuth_angle_in_deg_for_all_bands_all_detectors': 156.346195853372, 'mean_sun_zenith_angle_in_deg_for_all_bands_all_detectors': 50.1170463180154, 'mean_value_of_aerosol_optical_thickness': 0.189598, 'mean_value_of_total_water_vapour_content': 0.737065, 'onboard_compression_flag': 'true', 'onboard_equalization_flag': 'null', 'optical_crosstalk_correction_flag': 'null', 'ozone_source': 'AUX_ECMWFT', 'ozone_value': 332.943152, 'product_quality_status': ['PASSED', 'PASSED', 'PASSED', 'PASSED', 'PASSED', 'PASSED'], 'reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date': 1.01479136898228}

  processing_history :

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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'}, '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'}

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 [9]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    op_mode="native",
    chunks={},
    group_sep="/",
    variables="measurements/r60m/b0[234]",
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", op_mode="native", chunks={}, group_sep="/", variables="measurements/r60m/b0[234]", ) ds

Out[9]:

<xarray.Dataset> Size: 80MB
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/b02  (measurements/r60m/y, measurements/r60m/x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    measurements/r60m/b03  (measurements/r60m/y, measurements/r60m/x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    measurements/r60m/b04  (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 the 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 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)
  • measurements/r60m/b02
    (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': '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

  • measurements/r60m/b03
    (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': '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

  • measurements/r60m/b04
    (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': '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

• 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 :

  {'AOT_retrieval_model': 'SEN2COR_DDV', 'NUC_table_ID': 3, 'SWIR_rearrangement_flag': 'null', 'UTM_zone_identification': 'UTM zone 32N', '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 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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'}

Following the previous steps, we can now select a data variable and display the RGB image as an example.

In [10]:

array = ds[["measurements/r60m/b04", "measurements/r60m/b03", "measurements/r60m/b02"]].to_dataarray(dim="band")
ax = (array / 0.3).clip(0, 1).plot.imshow(rgb="band")
ax.axes.set_aspect("equal")

array = ds[["measurements/r60m/b04", "measurements/r60m/b03", "measurements/r60m/b02"]].to_dataarray(dim="band") ax = (array / 0.3).clip(0, 1).plot.imshow(rgb="band") ax.axes.set_aspect("equal")

Open Sentinel-2 Level-2A in Analysis Mode as Dataset¶

Next, we use the "product" asset to open the Sentinel-2 product in analysis mode, which presents EOPF products as an analysis-ready xarray Dataset with a single, unified grid mapping. For Sentinel-2, all variables are automatically upscaled or downscaled so that the dataset shares one consistent pair of x and y coordinates. Note that analysis mode is the default in the xarray EOPF plugin.

We use 60 m resolution to keep plotting fast, as full-resolution data can be slow to render in Matplotlib. For full details on xarray.open_dataset configuration options, see the xarray-eopf documentation.

In [11]:

ds = xr.open_dataset(
    items[0].assets["product"].href, 
    engine="eopf-zarr", 
    resolution=60, 
    chunks={}
)
ds

ds = xr.open_dataset( items[0].assets["product"].href, engine="eopf-zarr", resolution=60, chunks={} ) ds

Out[11]:

<xarray.Dataset> Size: 375MB
Dimensions:      (y: 1830, x: 1830)
Coordinates:
    spatial_ref  int64 8B ...
  * x            (x) float32 7kB 3e+05 3.001e+05 ... 4.097e+05 4.098e+05
  * y            (y) float32 7kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06
Data variables: (12/15)
    b08          (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    cld          (y, x) float32 13MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    snw          (y, x) float32 13MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    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>
    ...           ...
    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>
    scl          (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
Attributes: (24)

xarray.Dataset

• Dimensions:
  • y: 1830
  • x: 1830
• Coordinates: (3)
  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["WGS 84 / UTM zone 32N",BASEGEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]],CONVERSION["UTM zone 32N",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",9,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",0.9996,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",500000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Navigation and medium accuracy spatial referencing."],AREA["Between 6°E and 12°E, northern hemisphere between equator and 84°N, onshore and offshore. Algeria. Austria. Cameroon. Denmark. Equatorial Guinea. France. Gabon. Germany. Italy. Libya. Liechtenstein. Monaco. Netherlands. Niger. Nigeria. Norway. Sao Tome and Principe. Svalbard. Sweden. Switzerland. Tunisia. Vatican City State."],BBOX[0,6,84,12]],ID["EPSG",32632]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984 ensemble

    projected_crs_name :

    WGS 84 / UTM zone 32N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    9.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    [1 values with dtype=int64]

  • 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: (15)
  • b08
    (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 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

    grid_mapping :

    spatial_ref

    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

  • cld
    (y, x)
    float32
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 101, 'long_name': 'cloud probability mask at 20m', 'short_name': 'cld', 'units': 'percentage', 'valid_max': 100, 'valid_min': 0}

    dtype :

    <f4

    long_name :

    Cloud probability, based on Sen2Cor processor

    units :

    percentage

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 12.78 MiB 363.38 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snw
    (y, x)
    float32
    dask.array<chunksize=(305, 305), 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, based on Sen2Cor processor

    units :

    percentage

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 12.78 MiB 363.38 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

    grid_mapping :

    spatial_ref

    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

  • scl
    (y, x)
    float64
    dask.array<chunksize=(305, 305), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0, 'coordinates': ['y', 'x'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<i8', 'fill_value': 0, 'long_name': 'scene classification map', 'scale_factor': 1, 'short_name': 'scl_r60m', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u1

    long_name :

    Scene classification data, based on Sen2Cor processor

    grid_mapping :

    spatial_ref

    flag_values :

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

    flag_meanings :

    no_data sat_or_defect_pixel topo_casted_shadows cloud_shadows vegetation not_vegetation water unclassified cloud_medium_prob cloud_high_prob thin_cirrus snow_or_ice

    flag_colors :

    #000000 #ff0000 #2f2f2f #643200 #00a000 #ffe65a #0000ff #808080 #c0c0c0 #ffffff #64c8ff #ff96ff

    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: (24)

  AOT_retrieval_model :

  SEN2COR_DDV

  NUC_table_ID :

  3

  SWIR_rearrangement_flag :

  null

  UTM_zone_identification :

  UTM zone 32N

  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 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0.18029249 0.24484994 0.32834511 0.42749961 0.53576798 0.64570396 0.74245998 0.81447017 0.85866596 0.87924777 0.88665266 0.888727 0.89105732 0.89725046 0.90632982 0.91627527 0.9263751 0.93515828 0.94226446 0.94739906 0.95131987 0.95416808 0.95635128 0.95813297 0.96062738 0.96344083 0.96577764 0.96818134 0.97104025 0.97343195 0.97597444 0.97865413 0.97994672 0.98064126 0.98094979 0.98143338 0.98123856 0.98068083 0.98033995 0.98101894 0.98268503 0.98507875 0.98777658 0.9903608 0.99202087 0.9933069 0.99256744 0.99044883 0.98717314 0.98353656 0.9800432 0.97617287 0.97253451 0.96977033 0.96762556 0.9662626 0.96572411 0.96592079 0.96729798 0.96975438 0.97337748 0.97862858 0.98345358 0.98765317 0.9919238 0.99554959 0.99767411 0.99866451 0.99941783 0.99930984 0.99885298 0.99913515 0.99973164 0.99973592 1 0.9998438 0.9967639 0.99175576 0.9859206 0.97887302 0.97029262 0.96135891 0.95379752 0.94709017 0.94228614 0.93919512 0.93616637 0.92889205 0.9129921 0.88158383 0.82602164 0.74412949 0.64281662 0.53483955 0.42772166 0.32439525 0.23488131 0.16445229 0.11056237 0.07271886 0.04634859 0.02949618 0.01941871 0.0133487 0.00934594 0.00654231 0.00487921 0.00341903 0.00249864 0.00196431 0.00142754 0.00105878 0.00049978 0.00022833 0.00015999 3.415e-05 4.517e-05 1.313e-05', 'units': 'nm', 'wavelength_max': 1682.0, 'wavelength_min': 1539.0}, '12': {'bandwidth': '180.0', 'central_wavelength': 2202.4, 'onboard_compression_rate': 2.4, 'onboard_integration_time': 1.5003662, 'physical_gain': 105.95356105, 'spectral_response_step': 1, 'spectral_response_values': '0.00063835 0.00102286 0.00288712 0.00399879 0.00658916 0.00765458 0.00799918 0.00853524 0.00929493 0.00999614 0.01096645 0.01208363 0.01335837 0.01501119 0.01711931 0.01977307 0.02332743 0.02765779 0.03320435 0.04020464 0.04886709 0.0596238 0.07315348 0.09050885 0.11143964 0.13686671 0.16776886 0.20341457 0.24281992 0.28484195 0.32711894 0.36834301 0.40794043 0.4447145 0.47647207 0.50303896 0.52524762 0.54328057 0.55717994 0.5685619 0.57895708 0.58860881 0.59881758 0.60990899 0.62128986 0.63421311 0.64847648 0.66363778 0.67997936 0.69609688 0.71189957 0.7269499 0.74124079 0.75734734 0.77201504 0.78552587 0.79818641 0.80962939 0.81965718 0.82855741 0.83668178 0.84440292 0.85106862 0.85321701 0.85471321 0.8561428 0.85778963 0.8594989 0.86142876 0.86322831 0.86511218 0.8672932 0.86967076 0.87427502 0.87856212 0.88241466 0.88590611 0.8894516 0.89320419 0.8966738 0.89987484 0.90257636 0.90481219 0.90550545 0.90564491 0.90548208 0.90513822 0.90476379 0.90406427 0.90332978 0.90274309 0.90235795 0.90196488 0.90340528 0.90429478 0.90529761 0.90642862 0.90807348 0.91010493 0.91293181 0.91556686 0.91842631 0.92128288 0.92431702 0.92719913 0.92972159 0.93190455 0.93412538 0.93588954 0.93707083 0.93762594 0.93828534 0.93763643 0.94042634 0.94250397 0.94324531 0.94301861 0.94210283 0.94061808 0.93841726 0.93665003 0.93524569 0.93301102 0.92686708 0.92104485 0.91547175 0.91100989 0.90828339 0.9072733 0.90817907 0.91115631 0.91617845 0.92284525 0.92059829 0.91947472 0.91947973 0.92126575 0.92451632 0.92772589 0.93196884 0.93676408 0.94147739 0.94679545 0.95119533 0.95443018 0.95704142 0.95972628 0.9625372 0.96485326 0.96603599 0.96664138 0.96630455 0.96545713 0.96484036 0.96365512 0.96169531 0.95944859 0.95732078 0.95513625 0.95355574 0.95273072 0.95217795 0.95172542 0.9521403 0.95263595 0.95405248 0.95707559 0.96063594 0.96421772 0.96830187 0.97268597 0.97741944 0.98289489 0.9871429 0.99073348 0.99398244 0.99678431 0.99875181 1 0.9999284 0.9991523 0.99712951 0.99388228 0.98968273 0.98373274 0.97621057 0.96780985 0.95833495 0.94842856 0.93818752 0.9277078 0.91702104 0.90597951 0.89384371 0.88165575 0.86861704 0.85460324 0.84058628 0.82598123 0.80948042 0.79182917 0.7724052 0.74907137 0.72031195 0.68815487 0.65125598 0.6100244 0.56600904 0.52095058 0.47464344 0.42924778 0.38584718 0.34208462 0.30067509 0.26317221 0.22770037 0.19571781 0.16808736 0.14467686 0.12482737 0.10823403 0.09439655 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  declared_accuracy_of_AOT_model :

  0.0

  declared_accuracy_of_radiative_transfer_model :

  0.0

  declared_accuracy_of_water_vapour_model :

  0.0

  electronic_crosstalk_correction_flag :

  null

  eopf_category :

  eoproduct

  geometric_refinement :

  {'mean_value_of_residual_displacements_at_all_tie_points_after_refinement_m': {'x_mean': 'null', 'y_mean': 'null'}, 'spacecraft_rotation': {'X': {'coefficients': 'null', 'degree': 'null'}, 'Y': {'coefficients': 'null', 'degree': 'null'}, 'Z': {'coefficients': 'null', 'degree': 'null'}}, 'standard_deviation_of_residual_displacements_at_all_tie_points_after_refinement_m': {'x_stdv': 'null', 'y_stdv': 'null'}}

  horizontal_CRS_code :

  EPSG:32632

  horizontal_CRS_name :

  WGS84 / UTM zone 32N

  mean_sun_azimuth_angle_in_deg_for_all_bands_all_detectors :

  156.346195853372

  mean_sun_zenith_angle_in_deg_for_all_bands_all_detectors :

  50.1170463180154

  mean_value_of_aerosol_optical_thickness :

  0.189598

  mean_value_of_total_water_vapour_content :

  0.737065

  onboard_compression_flag :

  true

  onboard_equalization_flag :

  null

  optical_crosstalk_correction_flag :

  null

  ozone_source :

  AUX_ECMWFT

  ozone_value :

  332.943152

  product_quality_status :

  ['PASSED', 'PASSED', 'PASSED', 'PASSED', 'PASSED', 'PASSED']

  reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date :

  1.01479136898228

And we can plot one spectral band as example:

In [12]:

ds.b04.plot(vmin=0, vmax=1)

ds.b04.plot(vmin=0, vmax=1)

Out[12]:

<matplotlib.collections.QuadMesh at 0x7bb364120410>

Conclusion¶

This notebook highlighted the main features of the xarray-eopf backend, which enables seamless access to EOPF Zarr products using familiar Xarray methods (xr.open_dataset and xr.open_datatree) by specifying engine="eopf-zarr". Key takeaways:

• Two operation modes:
  • op_mode="native": Represents EOPF products without modification using Xarray's DataTree and Dataset structures.
  • op_mode="analysis": Provides an analysis-ready, resampled view of the data and is available for xarray.open_dataset only.
• Opening parameters are integrated into the STAC items, allowing streamlined data access and management.
• Subgroups of the data tree are resolved via individual STAC assets and can be accessed with xarray.open_dataset using the provided parameters.

Future Examples

For additional examples, see the notebooks for Sentinel-1, Sentinel-2, and Sentinel-3 products, in both native and analysis modes.

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