Introduction
s1bursts makes Sentinel-1 synthetic aperture radar (SAR) data easy to use from Python. It searches and downloads single-look complex (SLC) 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. SLC is the Sentinel-1 product that keeps the phase of the radar signal, which coherence needs. Other products, such as ground range detected (GRD) scenes, are not supported.
Products
Sentinel-1’s radar sends microwave pulses to the ground and records their echo, by day and night and through clouds. s1bursts creates two products from it:
- Backscatter is how much of the pulse returns to the satellite. It depends on the roughness, moisture and structure of the surface. Wet snow, for example, looks much darker than dry snow. Use it to follow the state of a surface through time.
- Coherence compares two acquisitions of the same place, from 0 (unrelated) to 1 (unchanged). It drops where the surface melted, moved, grew or was disturbed between the two dates. Use it to detect change.
Backscatter and coherence can then be combined into composites of many acquisitions. Products and bands describes every product in detail.
What is a burst?
s1bursts handles Sentinel-1 data one burst at a time. A burst is the smallest part of a Sentinel-1 scene that can be processed on its own. Each scene is made of three strips side by side (five in EW mode), called sub-swaths, and each sub-swath is a sequence of overlapping bursts about 20 km long. A burst download is roughly 100 MB, compared with about 4 GB for the full scene, so a study area of a few kilometers no longer costs a whole scene.
The same footprint is imaged again on every orbit repeat, which makes bursts a natural unit for time series. s1bursts identifies a burst by four fields:
| Field | Meaning |
|---|---|
| burst_id | Number of the burst footprint, unique within a sub-swath |
| sub_swath | Sub-swath of the footprint: IW1 to IW3 (or EW1 to EW5) |
| polarization | VV, VH, HH or HV |
date |
Acquisition time |
Together, burst_id and sub_swath name a footprint: the same patch of ground on every repeat.
From search to composite
A typical workflow has four steps, each handled by one class:
| Step | Class | Result |
|---|---|---|
| Search | BurstCatalog.search() | A BurstCollection of catalog records |
| Download | BurstCatalog.download() | One SLC file per burst |
| Process | BurstProcessor | One GeoTIFF per burst or burst pair |
| Composite | ResolutionWeighter | GeoTIFFs on 10 km MGRS grid squares |
A FileManager decides where every file is stored, on local disk or on S3, so each step finds the output of the previous one.
Next steps
Start with Installation, then run the Quickstart. The tutorials show complete analyses on real data.