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User Guide

Accessing EOPF Sentinel Zarr Data Cubes with xcube

The "eopf-zarr" xcube data store enables you to create analysis-ready data cubes (ARDCs) from Sentinel Zarr sample products published by the EOPF Sentinel Zarr Sample Service.

This plugin provides convenient access to analysis-ready data from the Sentinel-1, Sentinel-2, and Sentinel-3 missions. Currently, only Sentinel-2 products are supported.

This guide walks you through:

  1. Set up a EOPF Data Store
  2. Selecting a Product
  3. Opening a Spatiotemporal Data Cube
  4. Inspecting, Visualizing, and Saving the Data Cube

1. Set up a EOPF Data Store

To instantiate the data store: 

from xcube.core.store import new_data_store

store = new_data_store("eopf-stac")

2. Select a Data Product

Data products from Sentinel-1, -2, and -3 are accessed via the data_id parameter, which corresponds to collection names in the EOPF STAC API.

To list all available data IDs: 

store.list_data_ids()
Each data_id is documented in the respective sections for the supported missions below.

3. Open a Spatiotemporal Data Cube

To open a cube (e.g., for Sentinel-2 Level-2A): 

ds = store.open_data(
    data_id="sentinel-2-l2a",
    bbox=[9.7, 53.4, 10.3, 53.7],
    time_range=["2025‑05‑01", "2025‑05‑07"],
    spatial_res=10 / 111320,  # here approx. 10m in degrees
    crs="EPSG:4326",
)
It uses the xarray-eopf backend as a reading routine to open the EOPF Zarr Samples.

💡 Note
open_data() builds a Dask graph and returns a lazy xarray.Dataset. No actual data is loaded at this point.

Required parameters:

  • bbox: Bounding box ["west", "south", "est", "north"] in CRS coordinates.
  • time_range: Temporal extent ["YYYY-MM-DD", "YYYY-MM-DD"].
  • spatial_res: Spatial resolution in meter of degree (depending on the CRS).
  • crs: Coordinate reference system (e.g. "EPSG:4326").

These parameters control the STAC API query and define the output cube's spatial grid.

Optional parameters:

  • variables: Variables to include in the dataset. Can be a name or regex pattern or iterable of the latter.
  • tile_size: Spatial tile size of the returned dataset (width, height).
  • query: Additional query options for filtering STAC Items by properties. See STAC Query Extension for details.

Additional parameters specific to each Sentinel mission are documented in the section below.

4. Inspect, Visualize, and Save the Data Cube

You can visualize a time slice:

ds.b04.isel(time=0).plot()

⚠️ Warning
This operation triggers data downloads and processing. For large regions, use with care.

Saving the Data Cube

Although the EOPF xcube plugin focuses on data access, it integrates seamlessly with the broader xcube ecosystem for post-processing and storage.

To persist the data cube, write it to a local file or S3-compatible object storage using the file or s3 xcube data store backends:

Local Filesystem Data Store: 

storage = new_data_store("file")
S3 Data Store: 
storage = new_data_store(
    "s3",
    root="bucket-name",
    storage_options=dict(
        anon=False,
        key="your_s3_key",
        secret="your_s3_secret",
    ),
)
More info: Filesystem-based data stores.

Then, write the cube:

storage.write_data(ds, "path/to/file.zarr")

Visualize in xcube Viewer

Once saved as Zarr, you can use xcube Viewer, to visualize the cube:

from xcube.webapi.viewer import Viewer

viewer = Viewer()
ds = storage.open_data("path/to/file.zarr")
viewer.add_dataset(ds)
viewer.show()

To retrieve the temporary URL of the launched viewer as a web app:

viewer.info()

Specific Parameters for supported Sentinel Missions

🛰️ Sentinel-1

Support for Sentinel-1 will be added in an upcoming release.

🛰️ Sentinel-2

Sentinel-2 provides multi-spectral imagery at different native resolutions:

  • 10m: b02, b03, b04, b08
  • 20m: b05, b06, b07, b8a, b11, b12
  • 60m: b01, b09, b10

Sentinel-2 products are organized as STAC Items, each representing a single tile. These tiles are stored in their native UTM CRS, which varies by geographic location.

Data Cube Generation Workflow

  1. STAC Query: A STAC API request returns relevant STAC Items (tiles) based on spatial and temporal extent (bbox and time_range argument).
  2. Sorting: Items are ordered by solar acquisition time and Tile ID.
  3. Native Alignment: Within each UTM zone, tiles from the same solar day are aligned in the native UMT without reprojection. Overlaps are resolved by selecting the first non-NaN pixel value in item order.
  4. Cube Assembly: The method of cube creation depends on the user's request, as summarized below:
Scenario Native Resolution Preservation Reprojected or Resampled Cube
Condition Requested bounding box lies within a single UTM zone, native CRS is requested, and the spatial resolution matches the native resolution. Data spans multiple UTM zones, a different CRS is requested (e.g., EPSG:4326), or a custom spatial resolution is requested.
Processing steps Only upsampling or downsampling is applied to align the differing resolutions of the spectral bands. Data cube is directly cropped using the requested bounding box, preserving original pixel values. Spatial extent may deviate slightly due to alignment with native pixel grid. A target grid mapping is computed from bounding box, spatial resolution, and CRS. Data from each UTM zone is reprojected/resampled to this grid. Overlaps resolved by first non-NaN pixel.

Upsampling and downsampling are controlled using the agg_methods and spline_order parameters (see below).

Data Identifiers

The EOPF xcube data store supports two Sentinel-2 product types via the data_id argument:

Data ID Description
sentinel-2-l1c Level‑1C top‑of‑atmosphere (TOA) reflectance
sentinel-2-l2a Level‑2A atmospherically corrected surface reflectance

Remarks to Opening Parameters

The opening parameters mentioned in the Section Open a Spatiotemporal Data Cube also apply to this data product.

Supported Variables

  • Surface reflectance bands:
    b01, b02, b03, b04, b05, b06, b07, b08, b8a, b09, b11, b12
  • Classification/Quality layers (L2A only):
    cld, scl, snw

Parameters governing Resampling and Reprojection

Users can specify any spatial resolution and coordinate reference system (CRS) when opening data with open_data. As a result, spectral bands may be resampled — either upsampled or downsampled — and reprojected to match the target grid. If reprojection is needed at a lower resolution, the process first downsamples the data and subsequently performs the reprojection.

Upsampling / Reprojecting:

  • Controlled via 2D interpolation using the spline_orders parameter.
  • Accepts a single order for all variables, or a dictionary mapping orders to variable names or data types.
  • Supported spline orders:
    • 0: nearest neighbor (default for scl)
    • 1: linear
    • 2: bi-linear
    • 3: cubic (default)

Downsampling:

  • Controlled via the agg_methods parameter.
  • Can be specified as a single method for all variables, or as a dictionary mapping methods to variable names or data types.
  • Supported aggregation methods:
    • "center" (default for scl)
    • "count"
    • "first"
    • "last"
    • "max"
    • "mean" (default)
    • "median"
    • "mode"
    • "min"
    • "prod"
    • "std"
    • "sum"
    • "var"

🛰️ Sentinel-3

Support for Sentinel-3 products will be added in an upcoming release.