Access Sentinel-2 in Analysis Mode¶

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

In this notebook, we demonstrate how to use the xarray-eopf backend to access Sentinel-2 EOPF Zarr products in analysis mode. All data access is lazy, meaning that data is only loaded when required—for example, during plotting or when writing to storage.

For a general introduction to the xarray EOPF backend, see the introduction notebook.
For an example of the native mode, see the Sentinel-2 native mode notebook.

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

Main Features of the Analysis Mode for Sentinel-2¶

The analysis mode provides EOPF products as analysis-ready xarray.Dataset objects on a single, unified spatial grid. For Sentinel-2, this means all variables are automatically upscaled or downscaled so that they share a consistent pair of x and y coordinates.

Native resolution of Sentinel-2 spectral bands¶

• 10 m: B02, B03, B04, B08
• 20 m: B05, B06, B07, B8A, B11, B12
• 60 m: B01, B09, B10

Key properties of Sentinel-2 analysis mode¶

• Default mode for the "eopf-zarr" backend
• Selectable spatial resolution: 10 m (default), 20 m, or 60 m
• Automatic resampling to align bands of different resolutions:
  • Upsampling via interpolation (default is nearest for integer arrays (e.g. Sentinel-2 L2A SCL, else bilinear),
  • Downsampling via aggregation (default is center for integer arrays (e.g. Sentinel-2 L2A SCL, else mean)
• Spatial subsetting via the bbox argument
• Reprojection to different CRS via the crs argument
• Flexible variable selection using names or regex patterns

For full details on opening parameters, see the Analysis Mode documentation and, specifically for Sentinel-2, the Sentinel-2 Analysis Mode guide.

---

Import Modules¶

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

In [1]:

import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
import pystac_client
import xarray as xr

import matplotlib.colors as mcolors import matplotlib.pyplot as plt import numpy as np import pystac_client import xarray as xr

---

Open a Sentinel-2 Level-1C¶

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

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

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

In [2]:

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

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

Out[2]:

[<Item id=S2A_MSIL1C_20260313T101741_N0512_R065_T32TLQ_20260313T153853>]

In [3]:

item = items[0]

item = items[0]

Open Sentinel-2 Level-1C with default parameters¶

We can now open the Sentinel-2 product in analysis mode. Since this is the default, the op_mode parameter does not need to be specified.

The following cell returns a lazy dataset, with all bands resampled to 60 m resolution. This lower resolution is chosen to keep plotting fast, as rendering full-resolution data in Matplotlib can be slow.

In [4]:

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

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

Out[4]:

<xarray.Dataset> Size: 348MB
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: (12/13)
    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>
    b08          (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>
    ...           ...
    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>
    b01          (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>
    b10          (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 image at 60m in meters from up-left pixel

    units :

    m

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

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

    dimensions :

    ['y']

    dtype :

    <f4

    long_name :

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

    units :

    m

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

• Data variables: (13)
  • 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': 'TOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    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

  • 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': 'TOA reflectance from MSI acquisition at spectral band 08 842 nm', 'scale_factor': 0.0001, 'short_name': 'b08_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 08 842 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 05 705 nm', 'scale_factor': 0.0001, 'short_name': 'b05_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 05 705 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 06 740 nm', 'scale_factor': 0.0001, 'short_name': 'b06_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 06 740 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 07 783 nm', 'scale_factor': 0.0001, 'short_name': 'b07_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 07 783 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 11 1610 nm', 'scale_factor': 0.0001, 'short_name': 'b11_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 11 1610 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 12 2190 nm', 'scale_factor': 0.0001, 'short_name': 'b12_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 12 2190 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 8A 865 nm', 'scale_factor': 0.0001, 'short_name': 'b8a_20m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

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

    units :

    digital_counts

    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

  • 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': 'TOA reflectance from MSI acquisition at spectral band 01 443 nm', 'scale_factor': 0.0001, 'short_name': 'b01_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 01 443 nm

    units :

    digital_counts

    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': 'TOA reflectance from MSI acquisition at spectral band 09 940 nm', 'scale_factor': 0.0001, 'short_name': 'b09_r60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 09 940 nm

    units :

    digital_counts

    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

  • b10
    (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': 'TOA reflectance from MSI acquisition at spectral band 10 1375 nm', 'scale_factor': 0.0001, 'short_name': 'b10_60m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 10 1375 nm

    units :

    digital_counts

    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

• 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)

  L0_ancillary_data_quality :

  null

  L0_ephemeris_data_quality :

  null

  NUC_table_ID :

  3

  SWIR_rearrangement_flag :

  null

  UTM_zone_identification :

  UTM zone 32N

  absolute_location_assessment_from_AOCS :

  null

  band_description :

  {'01': {'bandwidth': '20.0', 'central_wavelength': 442.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.4473767, 'physical_gain': 4.0887333, 'spectral_response_step': 1, 'spectral_response_values': '0.001775742 0.004073061 0.003626143 0.003515199 0.005729163 0.003780292 0.002636732 0.001262113 0.001987583 0.001368913 0.001250444 0.000463454 0.000814293 0.001376431 0.001485086 0.001823735 0.001626817 0.004392062 0.029008099 0.11874593 0.32387506 0.57281921 0.71472749 0.76196778 0.78929702 0.80862387 0.81089382 0.82419876 0.85415811 0.87079088 0.88731097 0.92619924 0.98228149 1 0.9752382 0.93596338 0.88997148 0.85021048 0.82569453 0.78390239 0.61417422 0.33007109 0.12410831 0.04365694 0.014749595', 'units': 'nm', 'wavelength_max': 456.0, 'wavelength_min': 412.0}, '02': {'bandwidth': '65.0', 'central_wavelength': 492.7, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2821506, 'physical_gain': 3.74715511, 'spectral_response_step': 1, 'spectral_response_values': '0.04255531 0.0722983 0.15374322 0.32799225 0.55336788 0.71011166 0.75285179 0.75232691 0.75668081 0.76326948 0.76239425 0.7852515 0.81546669 0.86179176 0.89282599 0.9195221 0.91900649 0.91315754 0.90035366 0.88989693 0.8823246 0.87606118 0.88429987 0.90695544 0.93232085 0.93947252 0.94383543 0.92204086 0.8860231 0.84743609 0.81251687 0.7823971 0.7731087 0.77209054 0.78742652 0.81217177 0.84605052 0.88767996 0.92793997 0.95069235 0.96573311 0.96938253 0.96570294 0.95832003 0.95405064 0.95178268 0.95699722 0.96556515 0.9770514 0.97709574 0.97436606 0.95903183 0.93506318 0.90190134 0.87165792 0.84402444 0.82280852 0.81536043 0.82057639 0.8395149 0.86992171 0.91526205 0.96067028 0.99163699 1 0.98356097 0.91130763 0.74018256 0.50395858 0.3050155 0.18004605 0.10738342 0.06593592 0.04207746 0.02662129 0.0143396 0.00265779 0.00081822', 'units': 'nm', 'wavelength_max': 533.0, 'wavelength_min': 456.0}, '03': {'bandwidth': '35.0', 'central_wavelength': 559.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3230393, 'physical_gain': 4.18026291, 'spectral_response_step': 1, 'spectral_response_values': '0.01448181 0.03422251 0.07346335 0.15444843 0.31661425 0.55322279 0.74859406 0.84890306 0.89772216 0.9215368 0.92572845 0.91122688 0.88818924 0.86523756 0.84718187 0.8387572 0.84459081 0.86219653 0.88838714 0.92443236 0.96017974 0.98685516 1 0.9986008 0.98076472 0.94522089 0.8981778 0.85580323 0.81841734 0.78862048 0.76460653 0.74963745 0.75055111 0.76137888 0.78244479 0.79890086 0.81016957 0.81408886 0.77358596 0.62881065 0.40397555 0.21542098 0.10715281 0.04792877 0.01848693 0.00108588', 'units': 'nm', 'wavelength_max': 583.0, 'wavelength_min': 538.0}, '04': {'bandwidth': '30.0', 'central_wavelength': 664.6, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3872929, 'physical_gain': 4.51634367, 'spectral_response_step': 1, 'spectral_response_values': '0.00141521 0.02590238 0.11651178 0.39088616 0.74959342 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'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7251472, 'physical_gain': 4.87027032, 'spectral_response_step': 1, 'spectral_response_values': '0.00171088 0.05467153 0.25806676 0.64722098 0.89218999 0.90232877 0.91508768 0.94115846 0.96299993 0.97510481 0.9770217 0.98736251 1 0.98880277 0.97179916 0.90126739 0.60672391 0.20520227 0.0267569', 'units': 'nm', 'wavelength_max': 749.0, 'wavelength_min': 731.0}, '07': {'bandwidth': '19.0', 'central_wavelength': 782.8, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7489293, 'physical_gain': 4.53413059, 'spectral_response_step': 1, 'spectral_response_values': '0.00045899 0.0117201 0.05219715 0.16561733 0.36903355 0.63685453 0.86119638 0.97002897 0.99119602 0.99897921 1 0.97725155 0.92572385 0.86605804 0.81969611 0.79407674 0.79111029 0.80431552 0.81902721 0.82571292 0.82011829 0.79222195 0.72054559 0.58767794 0.41430355 0.23088817 0.09850282 0.02736551 0.00516235', 'units': 'nm', 'wavelength_max': 797.0, 'wavelength_min': 769.0}, '08': {'bandwidth': '105.0', 'central_wavelength': 832.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2704681, 'physical_gain': 6.16307432, 'spectral_response_step': 1, 'spectral_response_values': '0.00067259 0.00388856 0 0 0 0 0 0 0 0 0 0 0 0.00028956 0.00702964 0.01752391 0.03231111 0.05328661 0.08299885 0.12748502 0.19591065 0.30246323 0.43553954 0.57141637 0.69766701 0.80303852 0.89115744 0.95284584 0.98894161 1 0.98840653 0.96389216 0.94207967 0.93694643 0.94227343 0.95395718 0.96828896 0.97966549 0.9854444 0.98592681 0.98391181 0.97793903 0.97722771 0.97810609 0.98144486 0.98764558 0.98857708 0.9862422 0.98070921 0.97078624 0.95721089 0.93865821 0.91672388 0.89620759 0.872888 0.85160331 0.8246394 0.80078117 0.7823386 0.76360274 0.74962771 0.7387221 0.73079407 0.72271237 0.72507708 0.72563856 0.72304217 0.72229211 0.71616364 0.71159446 0.70826954 0.70157205 0.69924532 0.70093762 0.70692733 0.71824001 0.73124634 0.7484061 0.76818541 0.78394807 0.7968381 0.80260206 0.8045194 0.80240918 0.79699072 0.78920304 0.77691621 0.76518406 0.75119717 0.73700357 0.72262399 0.70412578 0.68410805 0.66474528 0.64736891 0.63005125 0.61564222 0.60249557 0.58988992 0.57993399 0.57136506 0.56094242 0.55235105 0.54568236 0.53958052 0.53510215 0.53093675 0.53016508 0.52984662 0.53036682 0.53211463 0.53271918 0.53246806 0.53331158 0.5319278 0.53051055 0.52951499 0.52996848 0.53253373 0.53705085 0.54235344 0.54912497 0.55523055 0.56011135 0.55767999 0.54821984 0.53144613 0.50763528 0.47811224 0.45092793 0.42798466 0.41051405 0.40039139 0.40087302 0.40829375 0.42086556 0.43007022 0.42456692 0.39136817 0.33009008 0.25720509 0.18189031 0.11650668 0.07031579 0.04275381 0.02593154 0.01574394 0.00394326', 'units': 'nm', 'wavelength_max': 907.0, 'wavelength_min': 760.0}, '09': {'bandwidth': '20.0', 'central_wavelength': 945.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.593408, 'physical_gain': 8.53843412, 'spectral_response_step': 1, 'spectral_response_values': '0.01662953 0.06111857 0.17407094 0.38946454 0.6645915 0.87454114 0.93695988 0.96751014 0.9893391 0.9951269 1 0.97845762 0.98069118 0.9922335 0.98798379 0.99428313 0.98348041 0.97820013 0.95023367 0.95299604 0.92240308 0.85573828 0.70970227 0.46429542 0.21538427 0.06534121 0.01625596', 'units': 'nm', 'wavelength_max': 958.0, 'wavelength_min': 932.0}, '10': {'bandwidth': '30.0', 'central_wavelength': 1373.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 5.6989655, 'physical_gain': 54.96215494, 'spectral_response_step': 1, 'spectral_response_values': '0.00024052 5.404e-05 3.052e-05 2.872e-05 7.632e-05 0.00010949 8.804e-05 0.00012356 0.00017424 0.0003317 0.00036891 0.0004467 0.00065919 0.0010913 0.00196903 0.00373668 0.00801754 0.01884719 0.04466732 0.10165546 0.20111776 0.34284841 0.50710992 0.6632068 0.78377143 0.86153862 0.91000261 0.94193255 0.96182259 0.97365119 0.98169786 0.98795826 0.99283342 0.99649788 0.99906011 1 0.99907734 0.99601604 0.9909083 0.98479854 0.97802142 0.97030114 0.96080954 0.94849765 0.93314108 0.91482336 0.8937997 0.86825426 0.83023193 0.76384193 0.65440009 0.50671604 0.35014737 0.21799972 0.12643091 0.06768988 0.0322709 0.013544 0.00544557 0.00237642 0.00111267 0.00053796 0.0003457 0.00017488 0.00021619 0.00019479 0.00010421 5.919e-05 5.109e-05 6.115e-05 5.527e-05 3.856e-05 3.147e-05 0.00012289 0.0001089 2.502e-05', 'units': 'nm', 'wavelength_max': 1412.0, 'wavelength_min': 1337.0}, '11': {'bandwidth': '90.0', 'central_wavelength': 1613.7, 'onboard_compression_rate': 2.4, 'onboard_integration_time': 1.4035684, 'physical_gain': 35.19950849, 'spectral_response_step': 1, 'spectral_response_values': '6.79e-06 6.66e-06 8e-06 2.734e-05 3.685e-05 8.851e-05 0.00014522 0.00024812 0.00047627 0.00056335 0.00065326 0.00089835 0.00114664 0.00165604 0.00241611 0.00350246 0.00524274 0.0081538 0.01237062 0.0186097 0.02721853 0.03879155 0.05379167 0.07353187 0.09932758 0.1334178 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 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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 0.08235799 0.07149445 0.0626855 0.05498009 0.04818852 0.04285814 0.03859244 0.03494044 0.03199172 0.02958044 0.02741084 0.02556884 0.02395058 0.02166741 0.0191457 0.01632139 0.0109837 0.00736032 0.00649061 0.00469736 0.00205874', 'units': 'nm', 'wavelength_max': 2320.0, 'wavelength_min': 2078.0}, '8A': {'bandwidth': '20.0', 'central_wavelength': 864.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.5586717, 'physical_gain': 5.13740108, 'spectral_response_step': 1, 'spectral_response_values': '0.00030097 0 0 0 0 0 0 0 0 0 0.00157217 0.00249886 0.01332037 0.02614866 0.05260479 0.10779709 0.22160755 0.39721628 0.60986885 0.81658883 0.9322445 0.97210033 0.97545482 0.97538048 0.97328205 0.97607828 0.98034955 0.98690928 0.99087465 0.99741818 0.99984673 0.99939141 0.99587928 0.99541228 1 0.99640762 0.92359433 0.74137684 0.48965971 0.25020643 0.11221246 0.04755984 0.02297815 0.01061438 0.00108149', 'units': 'nm', 'wavelength_max': 881.0, 'wavelength_min': 837.0}}

  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

  meteo :

  {'source': 'ECMWF', 'type': 'FORECAST'}

  multispectral_registration_assessment :

  null

  onboard_compression_flag :

  true

  onboard_equalization_flag :

  null

  optical_crosstalk_correction_flag :

  null

  percentage_of_degraded_MSI_data :

  0.0

  planimetric_stability_assessment_from_AOCS :

  null

  product_quality_status :

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

  reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date :

  1.01479136898228

  spectral_band_of_reference :

  3

As an example, we plot the red band (b04), which triggers the loading and visualization of the data. Note that the red band is only available at 10 m resolution in the Level-1C product. Therefore, when plotting, the array is first loaded at 10 m resolution and then aggregated before being displayed, which may take some time.

In [5]:

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

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

/home/konstantin/micromamba/envs/xcube-eopf/lib/python3.13/site-packages/xcube_resampling/coarsen.py:103: RuntimeWarning: Mean of empty slice
  return nan_reducer(block, axis)
/home/konstantin/micromamba/envs/xcube-eopf/lib/python3.13/site-packages/xcube_resampling/coarsen.py:103: RuntimeWarning: Mean of empty slice
  return nan_reducer(block, axis)

Out[5]:

<matplotlib.collections.QuadMesh at 0x7b0fa44682f0>

Spatial Resampling, Subsetting and Reprojection¶

We can also change the resolution and display only a subset of the data by specifying the bbox argument, as shown below.

In [6]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    resolution=10,
    bbox=[340000, 4900000, 360000, 4920000],
    chunks={},
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", resolution=10, bbox=[340000, 4900000, 360000, 4920000], chunks={}, ) ds

Out[6]:

<xarray.Dataset> Size: 416MB
Dimensions:      (y: 2000, x: 2000)
Coordinates:
    spatial_ref  int64 8B ...
  * x            (x) float64 16kB 3.4e+05 3.4e+05 3.4e+05 ... 3.6e+05 3.6e+05
  * y            (y) float64 16kB 4.92e+06 4.92e+06 4.92e+06 ... 4.9e+06 4.9e+06
Data variables: (12/13)
    b02          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b03          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b04          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b08          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b05          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b06          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    ...           ...
    b11          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b12          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b8a          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b01          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b09          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
    b10          (y, x) float64 32MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>
Attributes: (24)

xarray.Dataset

• Dimensions:
  • y: 2000
  • x: 2000
• 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)
    float64
    3.4e+05 3.4e+05 ... 3.6e+05 3.6e+05

    array([340005., 340015., 340025., ..., 359975., 359985., 359995.],
          shape=(2000,))

  • y
    (y)
    float64
    4.92e+06 4.92e+06 ... 4.9e+06

    array([4919995., 4919985., 4919975., ..., 4900025., 4900015., 4900005.],
          shape=(2000,))

• Data variables: (13)
  • b02
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 08 842 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b05
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 05 705 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b06
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 06 740 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b07
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 07 783 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b11
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 11 1610 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b12
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 12 2190 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b8a
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

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

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b01
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 01 443 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b09
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 09 940 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b10
    (y, x)
    float64
    dask.array<chunksize=(1830, 1830), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 10 1375 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 30.52 MiB 25.55 MiB Shape (2000, 2000) (1830, 1830) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([340005.0, 340015.0, 340025.0, 340035.0, 340045.0, 340055.0, 340065.0,
           340075.0, 340085.0, 340095.0,
           ...
           359905.0, 359915.0, 359925.0, 359935.0, 359945.0, 359955.0, 359965.0,
           359975.0, 359985.0, 359995.0],
          dtype='float64', name='x', length=2000))

  • y
    PandasIndex

    PandasIndex(Index([4919995.0, 4919985.0, 4919975.0, 4919965.0, 4919955.0, 4919945.0,
           4919935.0, 4919925.0, 4919915.0, 4919905.0,
           ...
           4900095.0, 4900085.0, 4900075.0, 4900065.0, 4900055.0, 4900045.0,
           4900035.0, 4900025.0, 4900015.0, 4900005.0],
          dtype='float64', name='y', length=2000))

• Attributes: (24)

  L0_ancillary_data_quality :

  null

  L0_ephemeris_data_quality :

  null

  NUC_table_ID :

  3

  SWIR_rearrangement_flag :

  null

  UTM_zone_identification :

  UTM zone 32N

  absolute_location_assessment_from_AOCS :

  null

  band_description :

  {'01': {'bandwidth': '20.0', 'central_wavelength': 442.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.4473767, 'physical_gain': 4.0887333, 'spectral_response_step': 1, 'spectral_response_values': '0.001775742 0.004073061 0.003626143 0.003515199 0.005729163 0.003780292 0.002636732 0.001262113 0.001987583 0.001368913 0.001250444 0.000463454 0.000814293 0.001376431 0.001485086 0.001823735 0.001626817 0.004392062 0.029008099 0.11874593 0.32387506 0.57281921 0.71472749 0.76196778 0.78929702 0.80862387 0.81089382 0.82419876 0.85415811 0.87079088 0.88731097 0.92619924 0.98228149 1 0.9752382 0.93596338 0.88997148 0.85021048 0.82569453 0.78390239 0.61417422 0.33007109 0.12410831 0.04365694 0.014749595', 'units': 'nm', 'wavelength_max': 456.0, 'wavelength_min': 412.0}, '02': {'bandwidth': '65.0', 'central_wavelength': 492.7, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2821506, 'physical_gain': 3.74715511, 'spectral_response_step': 1, 'spectral_response_values': '0.04255531 0.0722983 0.15374322 0.32799225 0.55336788 0.71011166 0.75285179 0.75232691 0.75668081 0.76326948 0.76239425 0.7852515 0.81546669 0.86179176 0.89282599 0.9195221 0.91900649 0.91315754 0.90035366 0.88989693 0.8823246 0.87606118 0.88429987 0.90695544 0.93232085 0.93947252 0.94383543 0.92204086 0.8860231 0.84743609 0.81251687 0.7823971 0.7731087 0.77209054 0.78742652 0.81217177 0.84605052 0.88767996 0.92793997 0.95069235 0.96573311 0.96938253 0.96570294 0.95832003 0.95405064 0.95178268 0.95699722 0.96556515 0.9770514 0.97709574 0.97436606 0.95903183 0.93506318 0.90190134 0.87165792 0.84402444 0.82280852 0.81536043 0.82057639 0.8395149 0.86992171 0.91526205 0.96067028 0.99163699 1 0.98356097 0.91130763 0.74018256 0.50395858 0.3050155 0.18004605 0.10738342 0.06593592 0.04207746 0.02662129 0.0143396 0.00265779 0.00081822', 'units': 'nm', 'wavelength_max': 533.0, 'wavelength_min': 456.0}, '03': {'bandwidth': '35.0', 'central_wavelength': 559.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3230393, 'physical_gain': 4.18026291, 'spectral_response_step': 1, 'spectral_response_values': '0.01448181 0.03422251 0.07346335 0.15444843 0.31661425 0.55322279 0.74859406 0.84890306 0.89772216 0.9215368 0.92572845 0.91122688 0.88818924 0.86523756 0.84718187 0.8387572 0.84459081 0.86219653 0.88838714 0.92443236 0.96017974 0.98685516 1 0.9986008 0.98076472 0.94522089 0.8981778 0.85580323 0.81841734 0.78862048 0.76460653 0.74963745 0.75055111 0.76137888 0.78244479 0.79890086 0.81016957 0.81408886 0.77358596 0.62881065 0.40397555 0.21542098 0.10715281 0.04792877 0.01848693 0.00108588', 'units': 'nm', 'wavelength_max': 583.0, 'wavelength_min': 538.0}, '04': {'bandwidth': '30.0', 'central_wavelength': 664.6, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3872929, 'physical_gain': 4.51634367, 'spectral_response_step': 1, 'spectral_response_values': '0.00141521 0.02590238 0.11651178 0.39088616 0.74959342 0.94485805 0.98011173 0.99406309 1 0.99545475 0.99052772 0.97733476 0.94055988 0.87894956 0.81629384 0.77345952 0.75448766 0.75991531 0.7826343 0.8101689 0.83612975 0.86125424 0.88609106 0.91138767 0.93405146 0.95042063 0.9592573 0.96039555 0.95913395 0.95809013 0.95527459 0.94376465 0.89490799 0.74426308 0.476777 0.22960399 0.08009118 0.02617076 0.00415242', 'units': 'nm', 'wavelength_max': 684.0, 'wavelength_min': 646.0}, '05': {'bandwidth': '15.0', 'central_wavelength': 704.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.844058, 'physical_gain': 5.20124492, 'spectral_response_step': 1, 'spectral_response_values': '0.02835786 0.12369337 0.39378774 0.76113071 0.97108502 0.99889523 1 0.99412258 0.98321789 0.96704093 0.94847389 0.92714833 0.90372458 0.88614713 0.86723745 0.79075319 0.58840332 0.26334833 0.05675422 0.00618833', 'units': 'nm', 'wavelength_max': 714.0, 'wavelength_min': 695.0}, '06': {'bandwidth': '13.0', 'central_wavelength': 740.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7251472, 'physical_gain': 4.87027032, 'spectral_response_step': 1, 'spectral_response_values': '0.00171088 0.05467153 0.25806676 0.64722098 0.89218999 0.90232877 0.91508768 0.94115846 0.96299993 0.97510481 0.9770217 0.98736251 1 0.98880277 0.97179916 0.90126739 0.60672391 0.20520227 0.0267569', 'units': 'nm', 'wavelength_max': 749.0, 'wavelength_min': 731.0}, '07': {'bandwidth': '19.0', 'central_wavelength': 782.8, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7489293, 'physical_gain': 4.53413059, 'spectral_response_step': 1, 'spectral_response_values': '0.00045899 0.0117201 0.05219715 0.16561733 0.36903355 0.63685453 0.86119638 0.97002897 0.99119602 0.99897921 1 0.97725155 0.92572385 0.86605804 0.81969611 0.79407674 0.79111029 0.80431552 0.81902721 0.82571292 0.82011829 0.79222195 0.72054559 0.58767794 0.41430355 0.23088817 0.09850282 0.02736551 0.00516235', 'units': 'nm', 'wavelength_max': 797.0, 'wavelength_min': 769.0}, '08': {'bandwidth': '105.0', 'central_wavelength': 832.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2704681, 'physical_gain': 6.16307432, 'spectral_response_step': 1, 'spectral_response_values': '0.00067259 0.00388856 0 0 0 0 0 0 0 0 0 0 0 0.00028956 0.00702964 0.01752391 0.03231111 0.05328661 0.08299885 0.12748502 0.19591065 0.30246323 0.43553954 0.57141637 0.69766701 0.80303852 0.89115744 0.95284584 0.98894161 1 0.98840653 0.96389216 0.94207967 0.93694643 0.94227343 0.95395718 0.96828896 0.97966549 0.9854444 0.98592681 0.98391181 0.97793903 0.97722771 0.97810609 0.98144486 0.98764558 0.98857708 0.9862422 0.98070921 0.97078624 0.95721089 0.93865821 0.91672388 0.89620759 0.872888 0.85160331 0.8246394 0.80078117 0.7823386 0.76360274 0.74962771 0.7387221 0.73079407 0.72271237 0.72507708 0.72563856 0.72304217 0.72229211 0.71616364 0.71159446 0.70826954 0.70157205 0.69924532 0.70093762 0.70692733 0.71824001 0.73124634 0.7484061 0.76818541 0.78394807 0.7968381 0.80260206 0.8045194 0.80240918 0.79699072 0.78920304 0.77691621 0.76518406 0.75119717 0.73700357 0.72262399 0.70412578 0.68410805 0.66474528 0.64736891 0.63005125 0.61564222 0.60249557 0.58988992 0.57993399 0.57136506 0.56094242 0.55235105 0.54568236 0.53958052 0.53510215 0.53093675 0.53016508 0.52984662 0.53036682 0.53211463 0.53271918 0.53246806 0.53331158 0.5319278 0.53051055 0.52951499 0.52996848 0.53253373 0.53705085 0.54235344 0.54912497 0.55523055 0.56011135 0.55767999 0.54821984 0.53144613 0.50763528 0.47811224 0.45092793 0.42798466 0.41051405 0.40039139 0.40087302 0.40829375 0.42086556 0.43007022 0.42456692 0.39136817 0.33009008 0.25720509 0.18189031 0.11650668 0.07031579 0.04275381 0.02593154 0.01574394 0.00394326', 'units': 'nm', 'wavelength_max': 907.0, 'wavelength_min': 760.0}, '09': {'bandwidth': '20.0', 'central_wavelength': 945.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.593408, 'physical_gain': 8.53843412, 'spectral_response_step': 1, 'spectral_response_values': '0.01662953 0.06111857 0.17407094 0.38946454 0.6645915 0.87454114 0.93695988 0.96751014 0.9893391 0.9951269 1 0.97845762 0.98069118 0.9922335 0.98798379 0.99428313 0.98348041 0.97820013 0.95023367 0.95299604 0.92240308 0.85573828 0.70970227 0.46429542 0.21538427 0.06534121 0.01625596', 'units': 'nm', 'wavelength_max': 958.0, 'wavelength_min': 932.0}, '10': {'bandwidth': '30.0', 'central_wavelength': 1373.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 5.6989655, 'physical_gain': 54.96215494, 'spectral_response_step': 1, 'spectral_response_values': '0.00024052 5.404e-05 3.052e-05 2.872e-05 7.632e-05 0.00010949 8.804e-05 0.00012356 0.00017424 0.0003317 0.00036891 0.0004467 0.00065919 0.0010913 0.00196903 0.00373668 0.00801754 0.01884719 0.04466732 0.10165546 0.20111776 0.34284841 0.50710992 0.6632068 0.78377143 0.86153862 0.91000261 0.94193255 0.96182259 0.97365119 0.98169786 0.98795826 0.99283342 0.99649788 0.99906011 1 0.99907734 0.99601604 0.9909083 0.98479854 0.97802142 0.97030114 0.96080954 0.94849765 0.93314108 0.91482336 0.8937997 0.86825426 0.83023193 0.76384193 0.65440009 0.50671604 0.35014737 0.21799972 0.12643091 0.06768988 0.0322709 0.013544 0.00544557 0.00237642 0.00111267 0.00053796 0.0003457 0.00017488 0.00021619 0.00019479 0.00010421 5.919e-05 5.109e-05 6.115e-05 5.527e-05 3.856e-05 3.147e-05 0.00012289 0.0001089 2.502e-05', 'units': 'nm', 'wavelength_max': 1412.0, 'wavelength_min': 1337.0}, '11': {'bandwidth': '90.0', 'central_wavelength': 1613.7, 'onboard_compression_rate': 2.4, 'onboard_integration_time': 1.4035684, 'physical_gain': 35.19950849, 'spectral_response_step': 1, 'spectral_response_values': '6.79e-06 6.66e-06 8e-06 2.734e-05 3.685e-05 8.851e-05 0.00014522 0.00024812 0.00047627 0.00056335 0.00065326 0.00089835 0.00114664 0.00165604 0.00241611 0.00350246 0.00524274 0.0081538 0.01237062 0.0186097 0.02721853 0.03879155 0.05379167 0.07353187 0.09932758 0.1334178 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 0.08235799 0.07149445 0.0626855 0.05498009 0.04818852 0.04285814 0.03859244 0.03494044 0.03199172 0.02958044 0.02741084 0.02556884 0.02395058 0.02166741 0.0191457 0.01632139 0.0109837 0.00736032 0.00649061 0.00469736 0.00205874', 'units': 'nm', 'wavelength_max': 2320.0, 'wavelength_min': 2078.0}, '8A': {'bandwidth': '20.0', 'central_wavelength': 864.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.5586717, 'physical_gain': 5.13740108, 'spectral_response_step': 1, 'spectral_response_values': '0.00030097 0 0 0 0 0 0 0 0 0 0.00157217 0.00249886 0.01332037 0.02614866 0.05260479 0.10779709 0.22160755 0.39721628 0.60986885 0.81658883 0.9322445 0.97210033 0.97545482 0.97538048 0.97328205 0.97607828 0.98034955 0.98690928 0.99087465 0.99741818 0.99984673 0.99939141 0.99587928 0.99541228 1 0.99640762 0.92359433 0.74137684 0.48965971 0.25020643 0.11221246 0.04755984 0.02297815 0.01061438 0.00108149', 'units': 'nm', 'wavelength_max': 881.0, 'wavelength_min': 837.0}}

  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

  meteo :

  {'source': 'ECMWF', 'type': 'FORECAST'}

  multispectral_registration_assessment :

  null

  onboard_compression_flag :

  true

  onboard_equalization_flag :

  null

  optical_crosstalk_correction_flag :

  null

  percentage_of_degraded_MSI_data :

  0.0

  planimetric_stability_assessment_from_AOCS :

  null

  product_quality_status :

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

  reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date :

  1.01479136898228

  spectral_band_of_reference :

  3

As an example, we plot again the red band (b04).

In [7]:

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

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

Out[7]:

<matplotlib.collections.QuadMesh at 0x7b0fd0106d50>

We can request the same area in WGS84 geographic coordinates by specifying crs="EPSG:4326". Note that both the resolution and bbox must be provided in the CRS units (i.e., degrees).

In [8]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    resolution=0.0002,
    crs="EPSG:4326",
    bbox=[7.0, 44.2, 7.3, 44.4],
    chunks={},
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", resolution=0.0002, crs="EPSG:4326", bbox=[7.0, 44.2, 7.3, 44.4], chunks={}, ) ds

Out[8]:

<xarray.Dataset> Size: 156MB
Dimensions:      (lon: 1500, lat: 1000)
Coordinates:
    spatial_ref  int64 8B ...
  * lon          (lon) float64 12kB 7.0 7.0 7.0 7.001 ... 7.299 7.3 7.3 7.3
  * lat          (lat) float64 8kB 44.4 44.4 44.4 44.4 ... 44.2 44.2 44.2 44.2
Data variables: (12/13)
    b02          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b03          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b04          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b08          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b05          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b06          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    ...           ...
    b11          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b12          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b8a          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b01          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b09          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
    b10          (lat, lon) float64 12MB dask.array<chunksize=(1000, 1500), meta=np.ndarray>
Attributes: (24)

xarray.Dataset

• Dimensions:
  • lon: 1500
  • lat: 1000
• Coordinates: (3)
  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["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]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    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

    grid_mapping_name :

    latitude_longitude

    [1 values with dtype=int64]

  • lon
    (lon)
    float64
    7.0 7.0 7.0 7.001 ... 7.3 7.3 7.3

    array([7.0001, 7.0003, 7.0005, ..., 7.2995, 7.2997, 7.2999], shape=(1500,))

  • lat
    (lat)
    float64
    44.4 44.4 44.4 ... 44.2 44.2 44.2

    array([44.3999, 44.3997, 44.3995, ..., 44.2005, 44.2003, 44.2001],
          shape=(1000,))

• Data variables: (13)
  • b02
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b03
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b04
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b08
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 08 842 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b05
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 05 705 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b06
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 06 740 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b07
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 07 783 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b11
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 11 1610 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b12
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 12 2190 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b8a
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

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

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b01
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 01 443 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b09
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 09 940 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b10
    (lat, lon)
    float64
    dask.array<chunksize=(1000, 1500), meta=np.ndarray>

    _eopf_attrs :

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

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 10 1375 nm

    units :

    digital_counts

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 11.44 MiB 11.44 MiB Shape (1000, 1500) (1000, 1500) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • lon
    PandasIndex

    PandasIndex(Index([7.0001, 7.0003, 7.0005, 7.0007, 7.0009, 7.0011, 7.0013, 7.0015, 7.0017,
           7.0019,
           ...
           7.2981, 7.2983, 7.2985, 7.2987, 7.2989, 7.2991, 7.2993, 7.2995, 7.2997,
           7.2999],
          dtype='float64', name='lon', length=1500))

  • lat
    PandasIndex

    PandasIndex(Index([           44.3999,            44.3997,            44.3995,
           44.399300000000004, 44.399100000000004, 44.398900000000005,
           44.398700000000005, 44.398500000000006,            44.3983,
                      44.3981,
           ...
                      44.2019,            44.2017, 44.201499999999996,
           44.201299999999996,            44.2011,            44.2009,
                      44.2007,            44.2005,            44.2003,
                      44.2001],
          dtype='float64', name='lat', length=1000))

• Attributes: (24)

  L0_ancillary_data_quality :

  null

  L0_ephemeris_data_quality :

  null

  NUC_table_ID :

  3

  SWIR_rearrangement_flag :

  null

  UTM_zone_identification :

  UTM zone 32N

  absolute_location_assessment_from_AOCS :

  null

  band_description :

  {'01': {'bandwidth': '20.0', 'central_wavelength': 442.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.4473767, 'physical_gain': 4.0887333, 'spectral_response_step': 1, 'spectral_response_values': '0.001775742 0.004073061 0.003626143 0.003515199 0.005729163 0.003780292 0.002636732 0.001262113 0.001987583 0.001368913 0.001250444 0.000463454 0.000814293 0.001376431 0.001485086 0.001823735 0.001626817 0.004392062 0.029008099 0.11874593 0.32387506 0.57281921 0.71472749 0.76196778 0.78929702 0.80862387 0.81089382 0.82419876 0.85415811 0.87079088 0.88731097 0.92619924 0.98228149 1 0.9752382 0.93596338 0.88997148 0.85021048 0.82569453 0.78390239 0.61417422 0.33007109 0.12410831 0.04365694 0.014749595', 'units': 'nm', 'wavelength_max': 456.0, 'wavelength_min': 412.0}, '02': {'bandwidth': '65.0', 'central_wavelength': 492.7, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2821506, 'physical_gain': 3.74715511, 'spectral_response_step': 1, 'spectral_response_values': '0.04255531 0.0722983 0.15374322 0.32799225 0.55336788 0.71011166 0.75285179 0.75232691 0.75668081 0.76326948 0.76239425 0.7852515 0.81546669 0.86179176 0.89282599 0.9195221 0.91900649 0.91315754 0.90035366 0.88989693 0.8823246 0.87606118 0.88429987 0.90695544 0.93232085 0.93947252 0.94383543 0.92204086 0.8860231 0.84743609 0.81251687 0.7823971 0.7731087 0.77209054 0.78742652 0.81217177 0.84605052 0.88767996 0.92793997 0.95069235 0.96573311 0.96938253 0.96570294 0.95832003 0.95405064 0.95178268 0.95699722 0.96556515 0.9770514 0.97709574 0.97436606 0.95903183 0.93506318 0.90190134 0.87165792 0.84402444 0.82280852 0.81536043 0.82057639 0.8395149 0.86992171 0.91526205 0.96067028 0.99163699 1 0.98356097 0.91130763 0.74018256 0.50395858 0.3050155 0.18004605 0.10738342 0.06593592 0.04207746 0.02662129 0.0143396 0.00265779 0.00081822', 'units': 'nm', 'wavelength_max': 533.0, 'wavelength_min': 456.0}, '03': {'bandwidth': '35.0', 'central_wavelength': 559.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3230393, 'physical_gain': 4.18026291, 'spectral_response_step': 1, 'spectral_response_values': '0.01448181 0.03422251 0.07346335 0.15444843 0.31661425 0.55322279 0.74859406 0.84890306 0.89772216 0.9215368 0.92572845 0.91122688 0.88818924 0.86523756 0.84718187 0.8387572 0.84459081 0.86219653 0.88838714 0.92443236 0.96017974 0.98685516 1 0.9986008 0.98076472 0.94522089 0.8981778 0.85580323 0.81841734 0.78862048 0.76460653 0.74963745 0.75055111 0.76137888 0.78244479 0.79890086 0.81016957 0.81408886 0.77358596 0.62881065 0.40397555 0.21542098 0.10715281 0.04792877 0.01848693 0.00108588', 'units': 'nm', 'wavelength_max': 583.0, 'wavelength_min': 538.0}, '04': {'bandwidth': '30.0', 'central_wavelength': 664.6, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3872929, 'physical_gain': 4.51634367, 'spectral_response_step': 1, 'spectral_response_values': '0.00141521 0.02590238 0.11651178 0.39088616 0.74959342 0.94485805 0.98011173 0.99406309 1 0.99545475 0.99052772 0.97733476 0.94055988 0.87894956 0.81629384 0.77345952 0.75448766 0.75991531 0.7826343 0.8101689 0.83612975 0.86125424 0.88609106 0.91138767 0.93405146 0.95042063 0.9592573 0.96039555 0.95913395 0.95809013 0.95527459 0.94376465 0.89490799 0.74426308 0.476777 0.22960399 0.08009118 0.02617076 0.00415242', 'units': 'nm', 'wavelength_max': 684.0, 'wavelength_min': 646.0}, '05': {'bandwidth': '15.0', 'central_wavelength': 704.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.844058, 'physical_gain': 5.20124492, 'spectral_response_step': 1, 'spectral_response_values': '0.02835786 0.12369337 0.39378774 0.76113071 0.97108502 0.99889523 1 0.99412258 0.98321789 0.96704093 0.94847389 0.92714833 0.90372458 0.88614713 0.86723745 0.79075319 0.58840332 0.26334833 0.05675422 0.00618833', 'units': 'nm', 'wavelength_max': 714.0, 'wavelength_min': 695.0}, '06': {'bandwidth': '13.0', 'central_wavelength': 740.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7251472, 'physical_gain': 4.87027032, 'spectral_response_step': 1, 'spectral_response_values': '0.00171088 0.05467153 0.25806676 0.64722098 0.89218999 0.90232877 0.91508768 0.94115846 0.96299993 0.97510481 0.9770217 0.98736251 1 0.98880277 0.97179916 0.90126739 0.60672391 0.20520227 0.0267569', 'units': 'nm', 'wavelength_max': 749.0, 'wavelength_min': 731.0}, '07': {'bandwidth': '19.0', 'central_wavelength': 782.8, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7489293, 'physical_gain': 4.53413059, 'spectral_response_step': 1, 'spectral_response_values': '0.00045899 0.0117201 0.05219715 0.16561733 0.36903355 0.63685453 0.86119638 0.97002897 0.99119602 0.99897921 1 0.97725155 0.92572385 0.86605804 0.81969611 0.79407674 0.79111029 0.80431552 0.81902721 0.82571292 0.82011829 0.79222195 0.72054559 0.58767794 0.41430355 0.23088817 0.09850282 0.02736551 0.00516235', 'units': 'nm', 'wavelength_max': 797.0, 'wavelength_min': 769.0}, '08': {'bandwidth': '105.0', 'central_wavelength': 832.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2704681, 'physical_gain': 6.16307432, 'spectral_response_step': 1, 'spectral_response_values': '0.00067259 0.00388856 0 0 0 0 0 0 0 0 0 0 0 0.00028956 0.00702964 0.01752391 0.03231111 0.05328661 0.08299885 0.12748502 0.19591065 0.30246323 0.43553954 0.57141637 0.69766701 0.80303852 0.89115744 0.95284584 0.98894161 1 0.98840653 0.96389216 0.94207967 0.93694643 0.94227343 0.95395718 0.96828896 0.97966549 0.9854444 0.98592681 0.98391181 0.97793903 0.97722771 0.97810609 0.98144486 0.98764558 0.98857708 0.9862422 0.98070921 0.97078624 0.95721089 0.93865821 0.91672388 0.89620759 0.872888 0.85160331 0.8246394 0.80078117 0.7823386 0.76360274 0.74962771 0.7387221 0.73079407 0.72271237 0.72507708 0.72563856 0.72304217 0.72229211 0.71616364 0.71159446 0.70826954 0.70157205 0.69924532 0.70093762 0.70692733 0.71824001 0.73124634 0.7484061 0.76818541 0.78394807 0.7968381 0.80260206 0.8045194 0.80240918 0.79699072 0.78920304 0.77691621 0.76518406 0.75119717 0.73700357 0.72262399 0.70412578 0.68410805 0.66474528 0.64736891 0.63005125 0.61564222 0.60249557 0.58988992 0.57993399 0.57136506 0.56094242 0.55235105 0.54568236 0.53958052 0.53510215 0.53093675 0.53016508 0.52984662 0.53036682 0.53211463 0.53271918 0.53246806 0.53331158 0.5319278 0.53051055 0.52951499 0.52996848 0.53253373 0.53705085 0.54235344 0.54912497 0.55523055 0.56011135 0.55767999 0.54821984 0.53144613 0.50763528 0.47811224 0.45092793 0.42798466 0.41051405 0.40039139 0.40087302 0.40829375 0.42086556 0.43007022 0.42456692 0.39136817 0.33009008 0.25720509 0.18189031 0.11650668 0.07031579 0.04275381 0.02593154 0.01574394 0.00394326', 'units': 'nm', 'wavelength_max': 907.0, 'wavelength_min': 760.0}, '09': {'bandwidth': '20.0', 'central_wavelength': 945.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.593408, 'physical_gain': 8.53843412, 'spectral_response_step': 1, 'spectral_response_values': '0.01662953 0.06111857 0.17407094 0.38946454 0.6645915 0.87454114 0.93695988 0.96751014 0.9893391 0.9951269 1 0.97845762 0.98069118 0.9922335 0.98798379 0.99428313 0.98348041 0.97820013 0.95023367 0.95299604 0.92240308 0.85573828 0.70970227 0.46429542 0.21538427 0.06534121 0.01625596', 'units': 'nm', 'wavelength_max': 958.0, 'wavelength_min': 932.0}, '10': {'bandwidth': '30.0', 'central_wavelength': 1373.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 5.6989655, 'physical_gain': 54.96215494, 'spectral_response_step': 1, 'spectral_response_values': '0.00024052 5.404e-05 3.052e-05 2.872e-05 7.632e-05 0.00010949 8.804e-05 0.00012356 0.00017424 0.0003317 0.00036891 0.0004467 0.00065919 0.0010913 0.00196903 0.00373668 0.00801754 0.01884719 0.04466732 0.10165546 0.20111776 0.34284841 0.50710992 0.6632068 0.78377143 0.86153862 0.91000261 0.94193255 0.96182259 0.97365119 0.98169786 0.98795826 0.99283342 0.99649788 0.99906011 1 0.99907734 0.99601604 0.9909083 0.98479854 0.97802142 0.97030114 0.96080954 0.94849765 0.93314108 0.91482336 0.8937997 0.86825426 0.83023193 0.76384193 0.65440009 0.50671604 0.35014737 0.21799972 0.12643091 0.06768988 0.0322709 0.013544 0.00544557 0.00237642 0.00111267 0.00053796 0.0003457 0.00017488 0.00021619 0.00019479 0.00010421 5.919e-05 5.109e-05 6.115e-05 5.527e-05 3.856e-05 3.147e-05 0.00012289 0.0001089 2.502e-05', 'units': 'nm', 'wavelength_max': 1412.0, 'wavelength_min': 1337.0}, '11': {'bandwidth': '90.0', 'central_wavelength': 1613.7, 'onboard_compression_rate': 2.4, 'onboard_integration_time': 1.4035684, 'physical_gain': 35.19950849, 'spectral_response_step': 1, 'spectral_response_values': '6.79e-06 6.66e-06 8e-06 2.734e-05 3.685e-05 8.851e-05 0.00014522 0.00024812 0.00047627 0.00056335 0.00065326 0.00089835 0.00114664 0.00165604 0.00241611 0.00350246 0.00524274 0.0081538 0.01237062 0.0186097 0.02721853 0.03879155 0.05379167 0.07353187 0.09932758 0.1334178 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 0.08235799 0.07149445 0.0626855 0.05498009 0.04818852 0.04285814 0.03859244 0.03494044 0.03199172 0.02958044 0.02741084 0.02556884 0.02395058 0.02166741 0.0191457 0.01632139 0.0109837 0.00736032 0.00649061 0.00469736 0.00205874', 'units': 'nm', 'wavelength_max': 2320.0, 'wavelength_min': 2078.0}, '8A': {'bandwidth': '20.0', 'central_wavelength': 864.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.5586717, 'physical_gain': 5.13740108, 'spectral_response_step': 1, 'spectral_response_values': '0.00030097 0 0 0 0 0 0 0 0 0 0.00157217 0.00249886 0.01332037 0.02614866 0.05260479 0.10779709 0.22160755 0.39721628 0.60986885 0.81658883 0.9322445 0.97210033 0.97545482 0.97538048 0.97328205 0.97607828 0.98034955 0.98690928 0.99087465 0.99741818 0.99984673 0.99939141 0.99587928 0.99541228 1 0.99640762 0.92359433 0.74137684 0.48965971 0.25020643 0.11221246 0.04755984 0.02297815 0.01061438 0.00108149', 'units': 'nm', 'wavelength_max': 881.0, 'wavelength_min': 837.0}}

  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

  meteo :

  {'source': 'ECMWF', 'type': 'FORECAST'}

  multispectral_registration_assessment :

  null

  onboard_compression_flag :

  true

  onboard_equalization_flag :

  null

  optical_crosstalk_correction_flag :

  null

  percentage_of_degraded_MSI_data :

  0.0

  planimetric_stability_assessment_from_AOCS :

  null

  product_quality_status :

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

  reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date :

  1.01479136898228

  spectral_band_of_reference :

  3

For comparison, we plot the red band (b04).

In [9]:

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

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

Out[9]:

<matplotlib.collections.QuadMesh at 0x7b0fb770c7d0>

Explore Different Aggregation Methods¶

As mentioned above, the default aggregation method for spectral bands during downsampling is to take the mean of each superpixel. We can also specify the aggregation method via the keyword argument agg_methods, which will be demonstrated in the next cell.

Note: The interpolation method can also be specified when upsampling via the keyword argument interp_methods. Both keywords can be also a mapping from varaible name or data type to resampling method. More details are available in the Analysis Mode documentation.

In [10]:

ds_agg_max = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    resolution=60,
    agg_methods="max"
)
ds_agg_mean = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    resolution=60
)

fig, ax = plt.subplots(1, 3, figsize=(18, 5))
ds_agg_max.b04[1500:, 1500:].plot(ax=ax[0], vmin=0.0, vmax=0.4)
ax[0].set_title("Aggregation method 'max'")
ds_agg_mean.b04[1500:, 1500:].plot(ax=ax[1], vmin=0.0, vmax=0.4)
ax[1].set_title("Aggregation method 'mean'")
(ds_agg_max.b04 - ds_agg_mean.b04)[1500:, 1500:].plot(
    ax=ax[2], vmin=-0.1, vmax=0.1, cmap="bwr"
)
ax[2].set_title("Difference")
plt.tight_layout()

ds_agg_max = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", chunks={}, resolution=60, agg_methods="max" ) ds_agg_mean = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", chunks={}, resolution=60 ) fig, ax = plt.subplots(1, 3, figsize=(18, 5)) ds_agg_max.b04[1500:, 1500:].plot(ax=ax[0], vmin=0.0, vmax=0.4) ax[0].set_title("Aggregation method 'max'") ds_agg_mean.b04[1500:, 1500:].plot(ax=ax[1], vmin=0.0, vmax=0.4) ax[1].set_title("Aggregation method 'mean'") (ds_agg_max.b04 - ds_agg_mean.b04)[1500:, 1500:].plot( ax=ax[2], vmin=-0.1, vmax=0.1, cmap="bwr" ) ax[2].set_title("Difference") plt.tight_layout()

Support for Common Band Names¶

Support has been added for common band names from the STAC EO extension in Sentinel-2 analysis mode.

The variables parameter now accepts standard spectral names such as blue, green, red, nir, and others.

The next cell demonstrates an example where we filter spectral bands using these common names.

In [11]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    resolution=60,
    variables=["red", "green", "blue", "nir"]
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", chunks={}, resolution=60, variables=["red", "green", "blue", "nir"] ) ds

Out[11]:

<xarray.Dataset> Size: 107MB
Dimensions:      (y: 1830, x: 1830)
Coordinates:
    spatial_ref  int64 8B ...
  * x            (x) float64 15kB 3e+05 3.001e+05 ... 4.097e+05 4.098e+05
  * y            (y) float64 15kB 5e+06 5e+06 5e+06 ... 4.89e+06 4.89e+06
Data variables:
    blue         (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    green        (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    red          (y, x) float64 27MB dask.array<chunksize=(305, 305), meta=np.ndarray>
    nir          (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)
    float64
    3e+05 3.001e+05 ... 4.098e+05

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

  • y
    (y)
    float64
    5e+06 5e+06 ... 4.89e+06 4.89e+06

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

• Data variables: (4)
  • blue
    (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': 'TOA reflectance from MSI acquisition at spectral band 02 490 nm', 'scale_factor': 0.0001, 'short_name': 'b02_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 02 490 nm

    units :

    digital_counts

    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

  • green
    (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': 'TOA reflectance from MSI acquisition at spectral band 03 560 nm', 'scale_factor': 0.0001, 'short_name': 'b03_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 03 560 nm

    units :

    digital_counts

    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

  • red
    (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': 'TOA reflectance from MSI acquisition at spectral band 04 665 nm', 'scale_factor': 0.0001, 'short_name': 'b04_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 04 665 nm

    units :

    digital_counts

    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

  • nir
    (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': 'TOA reflectance from MSI acquisition at spectral band 08 842 nm', 'scale_factor': 0.0001, 'short_name': 'b08_10m', 'units': 'digital_counts', 'valid_max': 65535, 'valid_min': 1}

    dtype :

    <u2

    long_name :

    TOA reflectance from MSI acquisition at spectral band 08 842 nm

    units :

    digital_counts

    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

• 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='float64', 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='float64', name='y', length=1830))

• Attributes: (24)

  L0_ancillary_data_quality :

  null

  L0_ephemeris_data_quality :

  null

  NUC_table_ID :

  3

  SWIR_rearrangement_flag :

  null

  UTM_zone_identification :

  UTM zone 32N

  absolute_location_assessment_from_AOCS :

  null

  band_description :

  {'01': {'bandwidth': '20.0', 'central_wavelength': 442.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.4473767, 'physical_gain': 4.0887333, 'spectral_response_step': 1, 'spectral_response_values': '0.001775742 0.004073061 0.003626143 0.003515199 0.005729163 0.003780292 0.002636732 0.001262113 0.001987583 0.001368913 0.001250444 0.000463454 0.000814293 0.001376431 0.001485086 0.001823735 0.001626817 0.004392062 0.029008099 0.11874593 0.32387506 0.57281921 0.71472749 0.76196778 0.78929702 0.80862387 0.81089382 0.82419876 0.85415811 0.87079088 0.88731097 0.92619924 0.98228149 1 0.9752382 0.93596338 0.88997148 0.85021048 0.82569453 0.78390239 0.61417422 0.33007109 0.12410831 0.04365694 0.014749595', 'units': 'nm', 'wavelength_max': 456.0, 'wavelength_min': 412.0}, '02': {'bandwidth': '65.0', 'central_wavelength': 492.7, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2821506, 'physical_gain': 3.74715511, 'spectral_response_step': 1, 'spectral_response_values': '0.04255531 0.0722983 0.15374322 0.32799225 0.55336788 0.71011166 0.75285179 0.75232691 0.75668081 0.76326948 0.76239425 0.7852515 0.81546669 0.86179176 0.89282599 0.9195221 0.91900649 0.91315754 0.90035366 0.88989693 0.8823246 0.87606118 0.88429987 0.90695544 0.93232085 0.93947252 0.94383543 0.92204086 0.8860231 0.84743609 0.81251687 0.7823971 0.7731087 0.77209054 0.78742652 0.81217177 0.84605052 0.88767996 0.92793997 0.95069235 0.96573311 0.96938253 0.96570294 0.95832003 0.95405064 0.95178268 0.95699722 0.96556515 0.9770514 0.97709574 0.97436606 0.95903183 0.93506318 0.90190134 0.87165792 0.84402444 0.82280852 0.81536043 0.82057639 0.8395149 0.86992171 0.91526205 0.96067028 0.99163699 1 0.98356097 0.91130763 0.74018256 0.50395858 0.3050155 0.18004605 0.10738342 0.06593592 0.04207746 0.02662129 0.0143396 0.00265779 0.00081822', 'units': 'nm', 'wavelength_max': 533.0, 'wavelength_min': 456.0}, '03': {'bandwidth': '35.0', 'central_wavelength': 559.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3230393, 'physical_gain': 4.18026291, 'spectral_response_step': 1, 'spectral_response_values': '0.01448181 0.03422251 0.07346335 0.15444843 0.31661425 0.55322279 0.74859406 0.84890306 0.89772216 0.9215368 0.92572845 0.91122688 0.88818924 0.86523756 0.84718187 0.8387572 0.84459081 0.86219653 0.88838714 0.92443236 0.96017974 0.98685516 1 0.9986008 0.98076472 0.94522089 0.8981778 0.85580323 0.81841734 0.78862048 0.76460653 0.74963745 0.75055111 0.76137888 0.78244479 0.79890086 0.81016957 0.81408886 0.77358596 0.62881065 0.40397555 0.21542098 0.10715281 0.04792877 0.01848693 0.00108588', 'units': 'nm', 'wavelength_max': 583.0, 'wavelength_min': 538.0}, '04': {'bandwidth': '30.0', 'central_wavelength': 664.6, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.3872929, 'physical_gain': 4.51634367, 'spectral_response_step': 1, 'spectral_response_values': '0.00141521 0.02590238 0.11651178 0.39088616 0.74959342 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'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7251472, 'physical_gain': 4.87027032, 'spectral_response_step': 1, 'spectral_response_values': '0.00171088 0.05467153 0.25806676 0.64722098 0.89218999 0.90232877 0.91508768 0.94115846 0.96299993 0.97510481 0.9770217 0.98736251 1 0.98880277 0.97179916 0.90126739 0.60672391 0.20520227 0.0267569', 'units': 'nm', 'wavelength_max': 749.0, 'wavelength_min': 731.0}, '07': {'bandwidth': '19.0', 'central_wavelength': 782.8, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.7489293, 'physical_gain': 4.53413059, 'spectral_response_step': 1, 'spectral_response_values': '0.00045899 0.0117201 0.05219715 0.16561733 0.36903355 0.63685453 0.86119638 0.97002897 0.99119602 0.99897921 1 0.97725155 0.92572385 0.86605804 0.81969611 0.79407674 0.79111029 0.80431552 0.81902721 0.82571292 0.82011829 0.79222195 0.72054559 0.58767794 0.41430355 0.23088817 0.09850282 0.02736551 0.00516235', 'units': 'nm', 'wavelength_max': 797.0, 'wavelength_min': 769.0}, '08': {'bandwidth': '105.0', 'central_wavelength': 832.8, 'onboard_compression_rate': 2.97, 'onboard_integration_time': 1.2704681, 'physical_gain': 6.16307432, 'spectral_response_step': 1, 'spectral_response_values': '0.00067259 0.00388856 0 0 0 0 0 0 0 0 0 0 0 0.00028956 0.00702964 0.01752391 0.03231111 0.05328661 0.08299885 0.12748502 0.19591065 0.30246323 0.43553954 0.57141637 0.69766701 0.80303852 0.89115744 0.95284584 0.98894161 1 0.98840653 0.96389216 0.94207967 0.93694643 0.94227343 0.95395718 0.96828896 0.97966549 0.9854444 0.98592681 0.98391181 0.97793903 0.97722771 0.97810609 0.98144486 0.98764558 0.98857708 0.9862422 0.98070921 0.97078624 0.95721089 0.93865821 0.91672388 0.89620759 0.872888 0.85160331 0.8246394 0.80078117 0.7823386 0.76360274 0.74962771 0.7387221 0.73079407 0.72271237 0.72507708 0.72563856 0.72304217 0.72229211 0.71616364 0.71159446 0.70826954 0.70157205 0.69924532 0.70093762 0.70692733 0.71824001 0.73124634 0.7484061 0.76818541 0.78394807 0.7968381 0.80260206 0.8045194 0.80240918 0.79699072 0.78920304 0.77691621 0.76518406 0.75119717 0.73700357 0.72262399 0.70412578 0.68410805 0.66474528 0.64736891 0.63005125 0.61564222 0.60249557 0.58988992 0.57993399 0.57136506 0.56094242 0.55235105 0.54568236 0.53958052 0.53510215 0.53093675 0.53016508 0.52984662 0.53036682 0.53211463 0.53271918 0.53246806 0.53331158 0.5319278 0.53051055 0.52951499 0.52996848 0.53253373 0.53705085 0.54235344 0.54912497 0.55523055 0.56011135 0.55767999 0.54821984 0.53144613 0.50763528 0.47811224 0.45092793 0.42798466 0.41051405 0.40039139 0.40087302 0.40829375 0.42086556 0.43007022 0.42456692 0.39136817 0.33009008 0.25720509 0.18189031 0.11650668 0.07031579 0.04275381 0.02593154 0.01574394 0.00394326', 'units': 'nm', 'wavelength_max': 907.0, 'wavelength_min': 760.0}, '09': {'bandwidth': '20.0', 'central_wavelength': 945.1, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 7.593408, 'physical_gain': 8.53843412, 'spectral_response_step': 1, 'spectral_response_values': '0.01662953 0.06111857 0.17407094 0.38946454 0.6645915 0.87454114 0.93695988 0.96751014 0.9893391 0.9951269 1 0.97845762 0.98069118 0.9922335 0.98798379 0.99428313 0.98348041 0.97820013 0.95023367 0.95299604 0.92240308 0.85573828 0.70970227 0.46429542 0.21538427 0.06534121 0.01625596', 'units': 'nm', 'wavelength_max': 958.0, 'wavelength_min': 932.0}, '10': {'bandwidth': '30.0', 'central_wavelength': 1373.5, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 5.6989655, 'physical_gain': 54.96215494, 'spectral_response_step': 1, 'spectral_response_values': '0.00024052 5.404e-05 3.052e-05 2.872e-05 7.632e-05 0.00010949 8.804e-05 0.00012356 0.00017424 0.0003317 0.00036891 0.0004467 0.00065919 0.0010913 0.00196903 0.00373668 0.00801754 0.01884719 0.04466732 0.10165546 0.20111776 0.34284841 0.50710992 0.6632068 0.78377143 0.86153862 0.91000261 0.94193255 0.96182259 0.97365119 0.98169786 0.98795826 0.99283342 0.99649788 0.99906011 1 0.99907734 0.99601604 0.9909083 0.98479854 0.97802142 0.97030114 0.96080954 0.94849765 0.93314108 0.91482336 0.8937997 0.86825426 0.83023193 0.76384193 0.65440009 0.50671604 0.35014737 0.21799972 0.12643091 0.06768988 0.0322709 0.013544 0.00544557 0.00237642 0.00111267 0.00053796 0.0003457 0.00017488 0.00021619 0.00019479 0.00010421 5.919e-05 5.109e-05 6.115e-05 5.527e-05 3.856e-05 3.147e-05 0.00012289 0.0001089 2.502e-05', 'units': 'nm', 'wavelength_max': 1412.0, 'wavelength_min': 1337.0}, '11': {'bandwidth': '90.0', 'central_wavelength': 1613.7, 'onboard_compression_rate': 2.4, 'onboard_integration_time': 1.4035684, 'physical_gain': 35.19950849, 'spectral_response_step': 1, 'spectral_response_values': '6.79e-06 6.66e-06 8e-06 2.734e-05 3.685e-05 8.851e-05 0.00014522 0.00024812 0.00047627 0.00056335 0.00065326 0.00089835 0.00114664 0.00165604 0.00241611 0.00350246 0.00524274 0.0081538 0.01237062 0.0186097 0.02721853 0.03879155 0.05379167 0.07353187 0.09932758 0.1334178 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 0.08235799 0.07149445 0.0626855 0.05498009 0.04818852 0.04285814 0.03859244 0.03494044 0.03199172 0.02958044 0.02741084 0.02556884 0.02395058 0.02166741 0.0191457 0.01632139 0.0109837 0.00736032 0.00649061 0.00469736 0.00205874', 'units': 'nm', 'wavelength_max': 2320.0, 'wavelength_min': 2078.0}, '8A': {'bandwidth': '20.0', 'central_wavelength': 864.7, 'onboard_compression_rate': 2.655, 'onboard_integration_time': 2.5586717, 'physical_gain': 5.13740108, 'spectral_response_step': 1, 'spectral_response_values': '0.00030097 0 0 0 0 0 0 0 0 0 0.00157217 0.00249886 0.01332037 0.02614866 0.05260479 0.10779709 0.22160755 0.39721628 0.60986885 0.81658883 0.9322445 0.97210033 0.97545482 0.97538048 0.97328205 0.97607828 0.98034955 0.98690928 0.99087465 0.99741818 0.99984673 0.99939141 0.99587928 0.99541228 1 0.99640762 0.92359433 0.74137684 0.48965971 0.25020643 0.11221246 0.04755984 0.02297815 0.01061438 0.00108149', 'units': 'nm', 'wavelength_max': 881.0, 'wavelength_min': 837.0}}

  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

  meteo :

  {'source': 'ECMWF', 'type': 'FORECAST'}

  multispectral_registration_assessment :

  null

  onboard_compression_flag :

  true

  onboard_equalization_flag :

  null

  optical_crosstalk_correction_flag :

  null

  percentage_of_degraded_MSI_data :

  0.0

  planimetric_stability_assessment_from_AOCS :

  null

  product_quality_status :

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

  reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date :

  1.01479136898228

  spectral_band_of_reference :

  3

In [12]:

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

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

Out[12]:

<matplotlib.collections.QuadMesh at 0x7b0f5e8d1810>

Open Sentinel-2 Level-2A product¶

Find a Sentinel-2 Level-2A Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for available Sentinel-2 Level-2A tiles.

In [13]:

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-2-l2a"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-2-l2a"], bbox=[7.2, 44.5, 7.4, 44.7], datetime=["2026-03-13", "2026-03-13"], ).items() ) items

Out[13]:

[<Item id=S2A_MSIL2A_20260313T101741_N0512_R065_T32TLQ_20260313T171916>]

In [14]:

item = items[0]

item = items[0]

In [15]:

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

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

Out[15]:

<xarray.Dataset> Size: 352MB
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) uint8 3MB 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)
    uint8
    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 3.19 MiB 90.84 kiB Shape (1830, 1830) (305, 305) Dask graph 36 chunks in 2 graph layers Data type uint8 numpy.ndarray

• Indexes: (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, 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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 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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

Open Sentinel-2 Level-2A with default parameters¶

We can now open the Sentinel-2 product in analysis mode.

Also here, the following cell returns a lazy dataset, with all bands resampled to 60 m resolution. This lower resolution is chosen to keep plotting fast, as above.

For the SCL variable, the flag names, values, and corresponding colors are stored in the attributes to simplify visualization. The colors follow the Sentinel Hub specifications.

In the next cell, we use these flag values and names to plot the categorical SCL dataset.

In [16]:

fig, ax = plt.subplots(1, 2, figsize=(12, 5))

ds.b04.plot(ax=ax[0], vmin=0.0, vmax=0.3)
ax[0].set_title("B04 - red")

cmap = mcolors.ListedColormap(ds.scl.attrs["flag_colors"].split(" "))
nb_colors = len(ds.scl.attrs["flag_values"])
norm = mcolors.BoundaryNorm(
    boundaries=np.arange(nb_colors + 1) - 0.5, ncolors=nb_colors
)
im = ds.scl.plot.imshow(ax=ax[1], cmap=cmap, norm=norm, add_colorbar=False)
cbar = fig.colorbar(im, ax=ax[1], ticks=ds.scl.attrs["flag_values"])
cbar.ax.set_yticklabels(ds.scl.attrs["flag_meanings"].split(" "))
cbar.set_label("Class")
ax[1].set_title("Scene classification layer")

plt.tight_layout()

fig, ax = plt.subplots(1, 2, figsize=(12, 5)) ds.b04.plot(ax=ax[0], vmin=0.0, vmax=0.3) ax[0].set_title("B04 - red") cmap = mcolors.ListedColormap(ds.scl.attrs["flag_colors"].split(" ")) nb_colors = len(ds.scl.attrs["flag_values"]) norm = mcolors.BoundaryNorm( boundaries=np.arange(nb_colors + 1) - 0.5, ncolors=nb_colors ) im = ds.scl.plot.imshow(ax=ax[1], cmap=cmap, norm=norm, add_colorbar=False) cbar = fig.colorbar(im, ax=ax[1], ticks=ds.scl.attrs["flag_values"]) cbar.ax.set_yticklabels(ds.scl.attrs["flag_meanings"].split(" ")) cbar.set_label("Class") ax[1].set_title("Scene classification layer") plt.tight_layout()

Conclusion¶

This notebook demonstrates how to access Sentinel-2 EOPF Zarr samples in analysis mode using the xarray-eopf plugin. The key takeaways are:

• Analysis mode is the default op_mode.
• In analysis mode, Sentinel-2 variables are automatically resampled (upscaled or downscaled) to share a consistent set of x and y coordinates.
• Data can be accessed lazily at a user-defined resolution, bounding box, and CRS, enabling flexible resampling, subsetting, and reprojection.
• Common band names defined by the STAC EO extension are supported for variable selection.
• Aggregation and interpolation methods can be customized by the user for both downsampling and upsampling.
• Scene Classification Layer (SCL) attributes include flag values, meanings, and colors, which can be used directly for visualization.

Note: This notebook only covers analysis mode for Sentinel-2. To learn more about native mode, see the Sentinel-2 native mode notebook.