Access Sentinel-3 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-3 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-3 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-3¶

Sentinel-3 products are provided on their native grid, where each pixel is defined by a latitude/longitude pair, forming a 2D curvilinear (irregular) grid.

The analysis mode applies the rectification algorithm in xcube-resampling to transform this irregular grid into a regular (rectilinear) grid with 1D latitude and longitude coordinates.

For SLSTR products, an additional terrain correction is applied during this process. This is necessary because the original geolocation accounts for Earth curvature, but not for terrain-induced variability due to topography. See the SLSTR product description for details.

For OLCI products, no additional terrain correction is required, as it is already included in the Level-1 data. See the OLCI Level-1 product description for details.

Key Properties of Sentinel-3 Analysis Mode¶

Default mode for the "eopf-zarr" backend
Rectification: converts the 2D curvilinear grid into a rectilinear grid
Spatial subsetting via the bbox argument
Reprojection to different CRS via the crs argument (default: WGS84, EPSG:4326)
Flexible variable selection using names or regular expressions

For full details on available parameters, see the Analysis Mode documentation and, specifically for Sentinel-3, the Sentinel-3 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]:

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

Open a Sentinel-3 OLCI L1 EFR¶

We begin with an example that accesses a Sentinel-3 OLCI L1 EFR product in analysis mode.

Find a Sentinel-3 OLCI L1 EFR Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-3 OLCI Level-1 EFR tile.

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catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-olci-l1-efr"],
        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-3-olci-l1-efr"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

Out[2]:

[<Item id=S3B_OL_1_EFR____20260313T100048_20260313T100348_20260313T120352_0179_117_350_2160_ESA_O_NR_004>,
 <Item id=S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004>]

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item = items[0]
item = items[0]

Open Sentinel-3 OLCI L1 EFR with default parameters¶

We can now open the Sentinel-3 product in analysis mode using the xr.open_dataset method. 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 recifified to a regular grid. If no resolution is given, the spatial resolution is estimated from the 2D latitude and longitude grids.

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

Out[4]:

<xarray.Dataset> Size: 4GB
Dimensions:        (lon: 5157, lat: 4791)
Coordinates:
  * lon            (lon) float64 41kB -4.786 -4.782 -4.778 ... 15.21 15.22 15.22
  * lat            (lat) float64 38kB 52.46 52.46 52.46 ... 39.56 39.56 39.55
    spatial_ref    int64 8B ...
Data variables: (12/21)
    oa01_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa02_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa03_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa04_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa05_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa06_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    ...             ...
    oa16_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa17_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa18_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa19_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa20_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa21_radiance  (lat, lon) float64 198MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
Attributes: (9)

As an example, we plot the red band (oa08_radiance), which triggers data loading, rectification, and visualization of the rectified result.

Note: To speed up rendering of large datasets in Matplotlib, we plot the data at a lower resolution (every 4th pixel).

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ds.oa08_radiance[::4, ::4].plot(vmin=0., vmax=150.)
ds.oa08_radiance[::4, ::4].plot(vmin=0., vmax=150.)

Out[5]:

<matplotlib.collections.QuadMesh at 0x785a5c34b4d0>

No description has been provided for this image

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.

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ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    resolution=0.005,
    bbox=[7, 44, 9, 45.5],
    chunks={},
)
ds
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    resolution=0.005,
    bbox=[7, 44, 9, 45.5],
    chunks={},
)
ds

Out[6]:

<xarray.Dataset> Size: 20MB
Dimensions:        (lon: 400, lat: 300)
Coordinates:
  * lon            (lon) float64 3kB 7.003 7.008 7.013 ... 8.988 8.992 8.998
  * lat            (lat) float64 2kB 45.5 45.49 45.49 45.48 ... 44.01 44.01 44.0
    spatial_ref    int64 8B ...
Data variables: (12/21)
    oa01_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa02_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa03_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa04_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa05_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa06_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    ...             ...
    oa16_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa17_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa18_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa19_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa20_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
    oa21_radiance  (lat, lon) float64 960kB dask.array<chunksize=(300, 400), meta=np.ndarray>
Attributes: (9)

As an example, we plot again the red band (oa08_radiance).

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ds.oa08_radiance.plot(vmin=0., vmax=150.)
ds.oa08_radiance.plot(vmin=0., vmax=150.)

Out[7]:

<matplotlib.collections.QuadMesh at 0x785a43462c10>

We can request the same area in LAEA CRS by specifying crs="EPSG:3035". Note that both the resolution and bbox must be provided in the CRS units.

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bbox = [7, 44, 9, 45.5]
transformer = pyproj.Transformer.from_crs("EPSG:4326", "EPSG:3035", always_xy=True)
bbox_crs = transformer.transform_bounds(*bbox) 
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    crs="EPSG:3035", 
    resolution=500,
    bbox=bbox_crs,
    chunks={},
)
ds
bbox = [7, 44, 9, 45.5]
transformer = pyproj.Transformer.from_crs("EPSG:4326", "EPSG:3035", always_xy=True)
bbox_crs = transformer.transform_bounds(*bbox) 
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    crs="EPSG:3035", 
    resolution=500,
    bbox=bbox_crs,
    chunks={},
)
ds

Out[8]:

<xarray.Dataset> Size: 19MB
Dimensions:        (x: 326, y: 342)
Coordinates:
  * x              (x) float64 3kB 4.08e+06 4.081e+06 ... 4.242e+06 4.243e+06
  * y              (y) float64 3kB 2.492e+06 2.492e+06 ... 2.322e+06 2.322e+06
    spatial_ref    int64 8B ...
Data variables: (12/21)
    oa01_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa02_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa03_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa04_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa05_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa06_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    ...             ...
    oa16_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa17_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa18_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa19_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa20_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
    oa21_radiance  (y, x) float64 892kB dask.array<chunksize=(342, 326), meta=np.ndarray>
Attributes: (9)

We plot the red band (oa08_radiance).

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ds.oa08_radiance.plot(vmin=0., vmax=150.)
ds.oa08_radiance.plot(vmin=0., vmax=150.)

Out[9]:

<matplotlib.collections.QuadMesh at 0x785a5cc0ac10>

Open a Sentinel-3 OLCI L1 ERR¶

We now access a Sentinel-3 OLCI L1 ERR product in analysis mode. The data access methods shown above apply equally to Sentinel-3 OLCI L1 EFR.

Find a Sentinel-3 OLCI L1 ERR Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for available Sentinel-3 OLCI L1 ERR tiles.

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catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-olci-l1-err"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-23", "2026-03-23"],
    ).items()
)
items
catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-olci-l1-err"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-23", "2026-03-23"],
    ).items()
)
items

Out[10]:

[<Item id=S3A_OL_1_ERR____20260323T093015_20260323T101415_20260324T104040_2640_137_250______PS1_O_NT_004>,
 <Item id=S3A_OL_1_ERR____20260323T093015_20260323T101415_20260323T113534_2640_137_250______PS1_O_NR_004>]

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item = items[0]
item = items[0]

Open Sentinel-3 OLCI L1 ERR with Default Parameters¶

We can now open the Sentinel-3 product in analysis mode using the xr.open_dataset method, as before.

If no spatial subsetting is applied via the bbox argument, the full dataset (typically covering half an orbit) is rectified to a regular grid in the WGS84 coordinate reference system.

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ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    resolution=0.01,
    bbox=[-0, 20, 40, 60],
    chunks={},
)
ds
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    resolution=0.01,
    bbox=[-0, 20, 40, 60],
    chunks={},
)
ds

Out[12]:

<xarray.Dataset> Size: 3GB
Dimensions:        (lon: 4000, lat: 4000)
Coordinates:
  * lon            (lon) float64 32kB 0.005 0.015 0.025 ... 39.97 39.98 39.99
  * lat            (lat) float64 32kB 59.99 59.98 59.97 ... 20.03 20.02 20.01
    spatial_ref    int64 8B ...
Data variables: (12/21)
    oa01_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa02_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa03_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa04_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa05_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa06_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    ...             ...
    oa16_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa17_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa18_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa19_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa20_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    oa21_radiance  (lat, lon) float64 128MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
Attributes: (8)

As an example, we plot the red band (oa08_radiance), which triggers data loading, rectification, and visualization of the rectified result.

Note: To speed up rendering of large datasets in Matplotlib, we plot the data at a lower resolution (every 10th pixel).

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ds.oa08_radiance[::4, ::4].plot(vmin=0., vmax=150.)
ds.oa08_radiance[::4, ::4].plot(vmin=0., vmax=150.)

Out[13]:

<matplotlib.collections.QuadMesh at 0x785a5cf8b250>

Open a Sentinel-3 SLSTR Level-2 LST¶

We now access a Sentinel-3 SLSTR Level-2 LST product in analysis mode. The data access methods shown above apply equally to Sentinel-3 SLSTR Level-2 LST.

Find a Sentinel-3 SLSTR Level-2 LST Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-3 SLSTR Level-2 LST tile.

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catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-slstr-l2-lst"],
        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-3-slstr-l2-lst"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

Out[14]:

[<Item id=S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004>,
 <Item id=S3B_SL_2_LST____20260313T212343_20260313T212643_20260315T051508_0179_117_357_0720_ESA_O_NT_004>,
 <Item id=S3B_SL_2_LST____20260313T100048_20260313T100348_20260314T174003_0180_117_350_2160_ESA_O_NT_004>]

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item = items[0]
item = items[0]

Open Sentinel-3 SLSTR Level-2 LST with Default Parameters¶

We can now open the Sentinel-3 product in analysis mode using the xr.open_dataset method, as before.

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

Out[16]:

<xarray.Dataset> Size: 21MB
Dimensions:      (lon: 1743, lat: 1491)
Coordinates:
  * lon          (lon) float64 14kB -9.795 -9.781 -9.768 ... 13.47 13.48 13.5
  * lat          (lat) float64 12kB 54.49 54.48 54.47 54.46 ... 41.12 41.11 41.1
    spatial_ref  int64 8B ...
Data variables:
    lst          (lat, lon) float64 21MB dask.array<chunksize=(1491, 1743), meta=np.ndarray>
Attributes: (16)

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ds.lst.plot()
ds.lst.plot()

Out[17]:

<matplotlib.collections.QuadMesh at 0x785a4027aad0>

We can also just open a subset of the tile using the bbox parameter as shown below.

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ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    bbox=[-5, 47, -3, 49],
)
ds
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    bbox=[-5, 47, -3, 49],
)
ds

Out[18]:

<xarray.Dataset> Size: 271kB
Dimensions:      (lon: 150, lat: 223)
Coordinates:
  * lon          (lon) float64 1kB -4.993 -4.98 -4.966 ... -3.021 -3.007 -2.994
  * lat          (lat) float64 2kB 49.0 48.99 48.98 48.97 ... 47.02 47.01 47.0
    spatial_ref  int64 8B ...
Data variables:
    lst          (lat, lon) float64 268kB dask.array<chunksize=(223, 150), meta=np.ndarray>
Attributes: (16)

As an example, we plot the LST array, which triggers data loading, rectification, and visualization of the rectified result.

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ds.lst.plot()
ds.lst.plot()

Out[19]:

<matplotlib.collections.QuadMesh at 0x785a26f2ad50>

Open Sentinel-3 SLSTR Level-1B RBT¶

We now access a Sentinel-3 SLSTR Level-1B RBT product in analysis mode.

The SLSTR instrument provides observations from two viewing geometries: nadir and oblique (forward view). These are designed to improve atmospheric correction and enable more accurate surface measurements.

Accordingly, many variables in the RBT product are available in pairs:

*_an → nadir view (e.g. s1_radiance_an)
*_ao → oblique view (e.g. s1_radiance_ao)

Find a Sentinel-3 SLSTR Level-1B RBT Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-3 SLSTR Level-1B RBT tile.

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catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-slstr-l1-rbt"],
        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-3-slstr-l1-rbt"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

Out[20]:

[<Item id=S3B_SL_1_RBT____20260313T212343_20260313T212643_20260313T235116_0179_117_357_0720_ESA_O_NR_004>,
 <Item id=S3B_SL_1_RBT____20260313T212343_20260313T212643_20260315T025651_0179_117_357_0720_ESA_O_NT_004>,
 <Item id=S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004>,
 <Item id=S3B_SL_1_RBT____20260313T100048_20260313T100348_20260314T152444_0180_117_350_2160_ESA_O_NT_004>]

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item = items[2]
item = items[2]

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item
item

Out[22]:

type "Feature"
stac_version "1.1.0"
stac_extensions[] 7 items
- 0 "https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"
- 1 "https://stac-extensions.github.io/sat/v1.0.0/schema.json"
- 2 "https://stac-extensions.github.io/eo/v2.0.0/schema.json"
- 3 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"
- 4 "https://stac-extensions.github.io/product/v0.1.0/schema.json"
- 5 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"
- 6 "https://stac-extensions.github.io/version/v1.2.0/schema.json"
id "S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004"
geometry
- type "Polygon"
- coordinates[] 1 items
  - 0[] 71 items
    
    0[] 2 items
    
    0 -5.04515
    
    1 42.0074
    
    1[] 2 items
    
    0 -4.43407
    
    1 41.9533
    
    2[] 2 items
    
    0 -3.82311
    
    1 41.8942
    
    3[] 2 items
    
    0 -3.21783
    
    1 41.8406
    
    4[] 2 items
    
    0 -2.60781
    
    1 41.7651
    
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properties
- gsd 1000.0
- created "2026-03-13T12:49:25.607472Z"
- mission "Sentinel-3"
- updated "2026-03-13T12:49:25.607472Z"
- datetime "2026-03-13T10:02:18.256830Z"
- platform "sentinel-3b"
- providers[] 3 items
  - 0
    
    url "https://commission.europa.eu/"
    
    name "European Commission"
    
    roles[] 1 items
    
    0 "licensor"
  - 1
    
    url "https://sentinel.esa.int/web/sentinel/missions/sentinel-3"
    
    name "ESA"
    
    roles[] 2 items
    
    0 "producer"
    
    1 "processor"
  - 2
    
    url "https://zarr.eopf.copernicus.eu/"
    
    name "EOPF Sentinel Zarr Samples Service"
    
    roles[] 2 items
    
    0 "host"
    
    1 "processor"
- published "2026-03-13T12:49:25.607472Z"
- deprecated False
- instruments[] 1 items
  - 0 "slstr"
- end_datetime "2026-03-13T10:03:48.256830Z"
- product:type "S03SLSRBT"
- constellation "sentinel-3"
- start_datetime "2026-03-13T10:00:48.256830Z"
- sat:orbit_state "descending"
- processing:level "L1"
- processing:lineage "systematic"
- processing:version "03.50"
- product:timeliness "PT3H"
- sat:absolute_orbit 41047
- sat:relative_orbit 350
- processing:facility "OPE"
- processing:software
  - PUG "03.50"
  - EOPF-CPM "2.6.2"
- product:timeliness_category "NR"
- sat:platform_international_designator "2018-039A"
links[] 5 items
- 0
  - rel "collection"
  - href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-slstr-l1-rbt"
  - type "application/json"
- 1
  - rel "parent"
  - href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-slstr-l1-rbt"
  - type "application/json"
- 2
  - rel "root"
  - href "https://stac.core.eopf.eodc.eu"
  - type "application/json"
  - title "EOPF Sentinel Zarr Samples Service STAC API"
- 3
  - rel "self"
  - href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-slstr-l1-rbt/items/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004"
  - type "application/geo+json"
- 4
  - rel "license"
  - href "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
  - type "application/pdf"
  - title "Legal notice on the use of Copernicus Sentinel Data and Service Information"
assets
- BT_fn
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/fnadir"
  - type "application/vnd+zarr"
  - title "TOA brightness temperature - F1, nadir view"
  - description "Dataset of the TOA brightness temperature for the F1 channel, 1km grid, nadir view"
  - gsd 1000
  - bands[] 1 items
    
    0
    
    name "F1"
    
    eo:center_wavelength 3742
    
    eo:full_width_half_max 398
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- BT_fo
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/foblique"
  - type "application/vnd+zarr"
  - title "TOA brightness temperature - F1, oblique view"
  - description "Dataset of the TOA brightness temperature for the F1 channel, 1km grid, oblique view"
  - gsd 1000
  - bands[] 1 items
    
    0
    
    name "F1"
    
    eo:center_wavelength 3742
    
    eo:full_width_half_max 398
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- BT_in
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/inadir"
  - type "application/vnd+zarr"
  - title "TOA brightness temperature - TIR, nadir view"
  - description "Dataset of the TOA brightness temperature for channels S7-S9 and F2 in the 1km grid, nadir view"
  - gsd 1000
  - bands[] 4 items
    
    0
    
    name "S7"
    
    eo:center_wavelength 3742
    
    eo:full_width_half_max 398
    
    1
    
    name "S8"
    
    eo:center_wavelength 10854
    
    eo:full_width_half_max 776
    
    2
    
    name "S9"
    
    eo:center_wavelength 12022.5
    
    eo:full_width_half_max 905
    
    3
    
    name "F2"
    
    eo:center_wavelength 10854
    
    eo:full_width_half_max 776
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- BT_io
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/ioblique"
  - type "application/vnd+zarr"
  - title "TOA brightness temperature - TIR, oblique view"
  - description "Dataset of the TOA brightness temperature for channels S7-S9 and F2, 1km grid, oblique view"
  - gsd 1000
  - bands[] 4 items
    
    0
    
    name "S7"
    
    eo:center_wavelength 3742
    
    eo:full_width_half_max 398
    
    1
    
    name "S8"
    
    eo:center_wavelength 10854
    
    eo:full_width_half_max 776
    
    2
    
    name "S9"
    
    eo:center_wavelength 12022.5
    
    eo:full_width_half_max 905
    
    3
    
    name "F2"
    
    eo:center_wavelength 10854
    
    eo:full_width_half_max 776
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- product
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr"
  - type "application/vnd+zarr"
  - title "EOPF Product"
  - description "The full Zarr store of the EOPF product"
  - xarray:open_datatree_kwargs
    
    chunks
    
    engine "eopf-zarr"
    
    op_mode "native"
  - roles[] 2 items
    
    0 "data"
    
    1 "metadata"
- radiance_an
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/anadir"
  - type "application/vnd+zarr"
  - title "TOA radiance - stripe A, nadir view"
  - description "Dataset of the TOA radiances for the 500m grid, stripe A, nadir view"
  - gsd 500
  - bands[] 6 items
    
    0
    
    name "S1"
    
    eo:common_name "green"
    
    eo:center_wavelength 554.27
    
    eo:full_width_half_max 19.26
    
    1
    
    name "S2"
    
    eo:common_name "red"
    
    eo:center_wavelength 659.47
    
    eo:full_width_half_max 19.25
    
    2
    
    name "S3"
    
    eo:common_name "nir08"
    
    eo:center_wavelength 868
    
    eo:full_width_half_max 20.6
    
    3
    
    name "S4"
    
    eo:common_name "cirrus"
    
    eo:center_wavelength 1374.8
    
    eo:full_width_half_max 20.8
    
    4
    
    name "S5"
    
    eo:common_name "swir16"
    
    eo:center_wavelength 1613.4
    
    eo:full_width_half_max 60.68
    
    5
    
    name "S6"
    
    eo:common_name "swir22"
    
    eo:center_wavelength 2255.7
    
    eo:full_width_half_max 50.15
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- radiance_ao
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/aoblique"
  - type "application/vnd+zarr"
  - title "TOA radiance - stripe A, oblique view"
  - description "Dataset of the TOA radiances for the 500m grid, stripe A, oblique view"
  - gsd 500
  - bands[] 6 items
    
    0
    
    name "S1"
    
    eo:common_name "green"
    
    eo:center_wavelength 554.27
    
    eo:full_width_half_max 19.26
    
    1
    
    name "S2"
    
    eo:common_name "red"
    
    eo:center_wavelength 659.47
    
    eo:full_width_half_max 19.25
    
    2
    
    name "S3"
    
    eo:common_name "nir08"
    
    eo:center_wavelength 868
    
    eo:full_width_half_max 20.6
    
    3
    
    name "S4"
    
    eo:common_name "cirrus"
    
    eo:center_wavelength 1374.8
    
    eo:full_width_half_max 20.8
    
    4
    
    name "S5"
    
    eo:common_name "swir16"
    
    eo:center_wavelength 1613.4
    
    eo:full_width_half_max 60.68
    
    5
    
    name "S6"
    
    eo:common_name "swir22"
    
    eo:center_wavelength 2255.7
    
    eo:full_width_half_max 50.15
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- radiance_bn
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/bnadir"
  - type "application/vnd+zarr"
  - title "TOA radiance - stripe B, nadir view"
  - description "Dataset of the TOA radiances for the 500m grid, stripe B, nadir view"
  - gsd 500
  - bands[] 3 items
    
    0
    
    name "S4"
    
    eo:common_name "cirrus"
    
    eo:center_wavelength 1374.8
    
    eo:full_width_half_max 20.8
    
    1
    
    name "S5"
    
    eo:common_name "swir16"
    
    eo:center_wavelength 1613.4
    
    eo:full_width_half_max 60.68
    
    2
    
    name "S6"
    
    eo:common_name "swir22"
    
    eo:center_wavelength 2255.7
    
    eo:full_width_half_max 50.15
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- radiance_bo
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/measurements/boblique"
  - type "application/vnd+zarr"
  - title "TOA radiance - stripe B, oblique view"
  - description "Dataset of the TOA radiances for the 500m grid, stripe B, oblique view"
  - gsd 500
  - bands[] 3 items
    
    0
    
    name "S4"
    
    eo:common_name "cirrus"
    
    eo:center_wavelength 1374.8
    
    eo:full_width_half_max 20.8
    
    1
    
    name "S5"
    
    eo:common_name "swir16"
    
    eo:center_wavelength 1613.4
    
    eo:full_width_half_max 60.68
    
    2
    
    name "S6"
    
    eo:common_name "swir22"
    
    eo:center_wavelength 2255.7
    
    eo:full_width_half_max 50.15
  - roles[] 2 items
    
    0 "data"
    
    1 "dataset"
- zipped_product
  - href "https://download.user.eopf.eodc.eu/zip/collections/sentinel-3-slstr-l1-rbt/items/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zip"
  - type "application/zip"
  - title "Zipped EOPF Product"
  - description "The full EOPF Zarr store as zip archive"
  - roles[] 3 items
    
    0 "data"
    
    1 "metadata"
    
    2 "archive"
- product_metadata
  - href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slsrbt-eu/13/products/cpm_v262/S3B_SL_1_RBT____20260313T100048_20260313T100348_20260313T122107_0179_117_350_2160_ESA_O_NR_004.zarr/.zmetadata"
  - type "application/json"
  - title "Consolidated Metadata"
  - description "Consolidated metadata of the EOPF product"
  - roles[] 1 items
    
    0 "metadata"
collection "sentinel-3-slstr-l1-rbt"

Open Sentinel-3 SLSTR Level-1B RBT with Default Parameters¶

We can now open the Sentinel-3 product in analysis mode using the xr.open_dataset method, as before.

In [23]:

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ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    bbox=[10, 43, 17, 48],
    variables=["s1_radiance_an", "s1_radiance_ao"],
)
ds
ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    chunks={},
    bbox=[10, 43, 17, 48],
    variables=["s1_radiance_an", "s1_radiance_ao"],
)
ds

Out[23]:

<xarray.Dataset> Size: 19MB
Dimensions:         (lon: 1093, lat: 1114)
Coordinates:
  * lon             (lon) float64 9kB 10.0 10.01 10.02 ... 16.99 16.99 17.0
  * lat             (lat) float64 9kB 48.0 48.0 47.99 47.99 ... 43.01 43.01 43.0
    spatial_ref     int64 8B ...
Data variables:
    s1_radiance_an  (lat, lon) float64 10MB dask.array<chunksize=(1114, 1093), meta=np.ndarray>
    s1_radiance_ao  (lat, lon) float64 10MB dask.array<chunksize=(1114, 1093), meta=np.ndarray>
Attributes: (90)

As an example, we plot the nadir and oblique views of the S1 thermal channel side by side.

In [24]:

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%%time
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
ds.s1_radiance_an.plot(ax=ax[0], robust=True)
ds.s1_radiance_ao.plot(ax=ax[1], robust=True)
plt.tight_layout()
%%time
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
ds.s1_radiance_an.plot(ax=ax[0], robust=True)
ds.s1_radiance_ao.plot(ax=ax[1], robust=True)
plt.tight_layout()

CPU times: user 1.84 s, sys: 302 ms, total: 2.14 s
Wall time: 4.82 s

Other Sentinel-3 products can be accessed using the same approach and are therefore not shown here in detail.

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-3 spectral bands are rectified to a rectilinear grid
Data can be accessed lazily at a user-defined resolution, bounding box, and CRS, enabling flexible resampling, subsetting, and reprojection.

Note: This notebook only covers analysis mode for Sentinel-3. To learn more about native mode, see the Sentinel-3 native mode notebook.