Water change detection is one of the most critical applications of modern remote sensing. As climate volatility increases, the ability to accurately quantify surface water extent and soil moisture levels has become a priority for hydrologists, agricultural practitioners, policymakers and urban planners alike. Utilizing multi-spectral (Sentinel-2) and Synthetic Aperture Radar (Sentinel-1) data, we can now map hydrologic anomalies (droughts or floods) with unprecedented precision.

In the DaFab project, the water change detection workflow is used to enrich satellite data with additional parameters that indicate flood or drought in the scenes. It helps in optimizing the search, automating the selection of the past flood or drought events visible in Sentinel-1 / Sentinel-2 images and finally monitoring, analysing the affected areas from the generated water masks.