xcube-eopf

xcube-eopf is a Python package and xcube plugin that adds a data store named eopf-zarr to xcube. The data store is used to access ESA EOPF data products as an analysis-ready datacube (ARDC).

Features

IMPORTANT
xcube-eopf is currently under active development.
Some features may be partially implemented or still in progress.

The EOPF xcube data store is designed to provide analysis-ready data cubes from the EOPF Sentinel Zarr samples for Sentinel-1, Sentinel-2, and Sentinel-3 missions. The main features are summarized below. A more in depth documentation is given in the User Guide.

Currently, support is focused on Sentinel-2 products.

Sentinel-1

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

Sentinel-2

The current implementation supports two Sentinel-2 product levels, available as data_id values:

• sentinel-2-l1c: Level-1C top-of-atmosphere reflectance
• sentinel-2-l2a: Level-2A atmospherically corrected surface reflectance

Cube Generation Workflow

The workflow for building 3D analysis-ready cubes from Sentinel-2 products involves the following steps:

1. Query products using the EOPF STAC API for a given time range and spatial extent.
2. Retrieve observations as cloud-optimized Zarr chunks via the xarray-eopf backend.
3. Mosaic spatial tiles into single images per timestamp.
4. Stack the mosaicked scenes along the temporal axis to form a 3D cube.

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

Example: Sentinel-2 L2A

from xcube.core.store import new_data_store

store = new_data_store("eopf-zarr")
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,  # meters to degrees (approx.)
    crs="EPSG:4326",
    variables=["b02", "b03", "b04", "scl"],
)

Sentinel-3

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

Usage

The xcube-eopf package can be installed from PyPI (pip install xcube-eopf) or conda-forge (conda install -c conda-forge xcube-eopf). After installation, you are ready to go and use the "eopf-zarr" argument to initiate a xcube EOPF data store.

from xcube.core.store import new_data_store

store = new_data_store("eopf-zarr")
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,  # meters converted to degrees (approx.)
    crs="EPSG:4326",
    variables=["b02", "b03", "b04", "scl"],
)

Development

Setting up a development environment

The recommended Python distribution for development is miniforge which includes conda, mamba, and their dependencies.

git clone https://github.com/EOPF-Sample-Service/xcube-eopf.git
cd xcube-eopf
mamba env create
mamba activate xcube-eopf
pip install -ve .

Install the library locally and test

mamba activate xcube-eopf
pip install -ve .
pytest

By default, this will run all unit tests. To run integration tests, use:

pytest integration

To run tests and generate a coverage report, use:

pytest --cov xcube_eopf --cov-report html tests

Some notes on the strategy of unit-testing

The unit test suite uses pytest-recording to mock STAC catalogs. During development an actual HTTP request is performed to a STAC catalog and the responses are saved in cassettes/**.yaml files. During testing, only the cassettes/**.yaml files are used without an actual HTTP request. During development, to save the responses to cassettes/**.yaml, run

pytest -v -s --record-mode new_episodes

Note that --record-mode new_episodes overwrites all cassettes. If the user only wants to write cassettes which are not saved already, --record-mode once can be used. pytest-recording supports all records modes given by VCR.py. After recording the cassettes, testing can be performed as usual.

Setting up a documentation environment

mamba activate xcube-eopf
pip install .[doc]

Testing documentation changes

mkdocs serve

Deploying documentation changes

mkdocs gh-deploy