s1bursts 0.8.0

Sentinel-1 download, processing and compositing made easy.

s1bursts makes Sentinel-1 synthetic aperture radar (SAR) data easy to use from Python. It searches and downloads the data from the Copernicus Data Space Ecosystem (CDSE), processes it with ESA SNAP into backscatter and coherence, and combines the results into composites on a fixed grid.

It works with bursts, the smallest part of a Sentinel-1 scene that can be processed on its own. A burst is about 100 MB instead of a 4 GB scene, so you download and process only the area you are interested in. This makes dense time series of a fixed area practical.

Features

• Search the CDSE catalog by area, time window, polarization, orbit and more
• Download single bursts, with automatic fallback between CDSE's burst service and its S3 storage
• Process to terrain-corrected backscatter and interferometric coherence, with SNAP processing graphs built from Python arguments
• Composite many acquisitions onto the 10 km MGRS grid with local resolution weighting, which favors the acquisitions with the highest spatial resolution at each pixel
• Store everything on local disk or S3

Installation

pip install s1bursts

Processing needs ESA SNAP 13 or 14, and downloading a free CDSE account. The installation guide covers both.

Quick start

With your CDSE credentials in the environment, download and process two acquisitions of one burst over the Aletsch glacier, 12 days apart:

from s1bursts import BurstCatalog, BurstProcessor, FileManager, SearchQuery, TimeRange

fm = FileManager("data/quickstart")
catalog = BurstCatalog(fm)

bursts = catalog.search(
    SearchQuery(
        time_range=TimeRange("2022-08-01", "2022-08-13"),
        burst_id=30346,
        sub_swath="IW1",
        polarization="VV",
    )
)
catalog.download(bursts).raise_on_failure()

# "auto" geocodes each burst in its own UTM zone, here EPSG:32632.
processor = BurstProcessor(fm)
rtc = processor.create_backscatter(bursts, crs="auto").raise_on_failure()

pairs = bursts.find_pairs(baseline=12)
coherence = processor.create_coherences(pairs, crs="auto").raise_on_failure()

Documentation

The documentation has a user guide, tutorials on real data, and the API reference.

Development

uv sync                       # set up the environment
uv run pytest                 # offline unit tests
uv run pytest -m integration  # network and SNAP tests (needs credentials and SNAP)
uv run ruff format && uv run ruff check --fix
uv run pyrefly check

See the contributing guide for the integration tests and how to build the documentation.

License

MIT