Recommended Practice: Flood Mapping with Sentinel-1 and Sentinel-2 Imagery and Digital Terrain Models

Flood mapping in urban areas poses significant challenges for Synthetic Aperture Radar (SAR) sensors due to limitations in detecting water in regions characterized by dense vegetation, urban infrastructure, or complex surface conditions. These limitations include reduced sensitivity in vegetated or built-up areas and water-like backscatter effects on smooth, dry, or snow-covered surfaces. The Global Flood Monitoring (GFM) platform employs Sentinel-1 SAR backscatter data for automated flood delineation but recognizes the constraints posed by such conditions. To address these challenges, the GFM has integrated an exclusion mask that highlights regions prone to SAR-based misclassification.  

This recommended practice introduces a novel algorithm developed by the Joint Research Centre of the European Commission that combines SAR-derived flood layers with digital terrain models, the GFM exclusion mask and incomplete Sentinel-2 flood maps. By leveraging DTMs, water depth calculations and hydrodynamic propagation models are applied to infer flood conditions within exclusion mask areas, enhancing the reliability of flood extent delineations.