Automated rapid estimation of flood depth using a digital elevation model and Earth Observation Satellite (EOS-04)-derived flood inundation

Abstract

Rapid and accurate flood assessment is crucial for effective disaster response, rehabilitation, and mitigation strategies. This study presents a fully automated framework for floodwater delineation and depth estimation using the Earth Observation Satellite 4 (EOS-04) (Radar Imaging Satellite, RISAT-1A) synthetic aperture radar (SAR) imagery and a digital elevation model (DEM). This is the first study to apply the established automatic-tile-based segmentation method and the height above the nearest drainage (HAND) tool to EOS-04 data for flood extent delineation. For flood depth estimation, this study introduces a novel application of the trend surface analysis (TSA) technique, enabling rapid and data-efficient assessment. Unlike traditional hydrodynamic models that demand extensive datasets and computational resources, TSA operates using only the inundated water layer and DEM, providing a highly data-efficient solution. The methodology is applied to flood-prone regions in Andhra Pradesh, Assam, Bihar, and Uttar Pradesh, India. Validation of flood extent against optical data demonstrates accuracy greater than 90 %. Flood depth estimation using TSA is validated by comparing water depths derived from river gauge stations with real-time field measurements and results from the floodwater depth estimation tool (FwDET). The TSA method achieves a root-mean-square error (RMSE) of 0.805, significantly outperforming FwDET's RMSE of 5.23. This integration of high-resolution SAR imagery and DEM represents a transformative, automated solution for real-time flood monitoring and depth estimation, enhancing disaster management capabilities.