bopen/sarsen

Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors

What it solves

Sarsen addresses the geometric and radiometric distortions inherent in Synthetic Aperture Radar (SAR) satellite data. Because SAR sensors are side-looking, they create artifacts like foreshortening, layover, and shadows when capturing terrain with varying elevations, which Sarsen corrects to produce accurate imagery.

How it works

The library provides algorithms for terrain correction using Digital Elevation Models (DEMs) to map radar data to real-world coordinates. It offers two primary modes:

  • Geometric Terrain Correction (GTC): Geocodes images to fix spatial distortions.
  • Radiometric Terrain Correction (RTC): Applies "gamma flattening" to compensate for backscatter modulation caused by topography.

Sarsen is built for cloud-native processing, integrating with xarray and Dask to handle datasets larger than available memory and enabling distributed processing of remote data from sources like the Copernicus Data Space Ecosystem (CDSE).

Who it’s for

It is designed for geospatial data scientists and satellite imagery analysts who work with Sentinel-1 SAR data and need to process large-scale radar datasets efficiently in Python environments.

Highlights

  • Cloud-Native Architecture: Leverages Dask and xarray for distributed, larger-than-memory processing.
  • Sentinel-1 Support: Compatible with Single Look Complex (SLC) and Ground Range Detected (GRD) products.
  • Flexible Data Access: Reads local and remote data via fsspec and rioxarray.
  • Dual Correction Modes: Provides both fast geometric correction and high-accuracy modes for interferometric processing.

Related

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