Open Earth Monitor — Global Workshop 2023

The use of synthetic aperture radar (SAR) data has become increasingly important in remote sensing for environmental monitoring. SAR data provides valuable information on surface characteristics and changes, such as land cover and land use change, soil condition, and vegetation growth, making it a powerful tool for various applications, including agriculture, forestry, and climate change studies. However, processing and integrating SAR data into analysis-ready formats can be complex and time-consuming, requiring specialized knowledge and tools.
In this contribution, we propose a software project called force-sar, which aims to integrate Sentinel-1 data into the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE). FORCE is a widely used data cube framework for processing and analyzing optical remote sensing data and the integration of SAR data into FORCE increases its capabilities for large-scale, multi-modal analysis.
Force-sar automatically queries available Sentinel-1 Ground Range Detected (GRD) imagery covering the spatial and temporal dimensions of your area of interest. The scenes are then directly accessed from satellite data repositories on cloud environments like Creodias or the Copernicus Data and Exploitation Platform Germany (CODE-DE). When no connection to such repositories is available, the data can also be downloaded from data centers like the Alaskan Satellite Facility (ASF). The scenes are processed to radiometrically calibrated gamma-naught backscatter data using a pre-built but customizable ESA SNAP processing graph. After resampling, reprojecting, and tiling the data, they are ready for ingestion into a FORCE data cube.
The integration of Sentinel-1 data into FORCE allows for the creation of SAR data cubes with consistent radiometric and geometric properties, covering large regions and spanning multiple time periods. This enables users to perform multi-sensor and multi-temporal analyses like change detection, compositing, and classification and regression tasks for environmental monitoring at large scales, thereby supporting decision-making and policy evaluation in frameworks like the EUs Green Deal or the Common Agricultural Policy.
Force-sar is already used in the ongoing Mowing Detection Intercomparison Exercise (MODCiX), where more than ten teams compare their algorithms for grassland mowing detection on a consistent and harmonized remote sensing and reference data set. A consistent data cube holding optical and SAR data covering test regions in eight European countries was created and is used for the study.
Force-sar provides a streamlined and fully containerized workflow for preprocessing and integrating SAR data into an existing data cube framework without the need for the installation of any external tools or dependencies. This opens up new possibilities for utilizing SAR data in large-scale environmental monitoring applications, particularly when working in cloud environments.