Abstract

Remote-sensed flood monitoring is rapidly advanced by the growing abundance of satellite data. This study presents the progress in building an operational system that harnesses the power of spatially high-resolution (HR), multi-source remote sensing data for event-based flood extent and depth mapping. By integrating pioneering extent retrieval-Radar Produced Inundation Diary (RAPID), Self-supervised Waterbody Detection (SWD), and depth retrieval processors, Global-LOCAL Solvers Integration Algorithm (GLOCAL), and the Emulated Flood Recession Algorithm (EFRA)-our proposed framework marks a significant leap in flood monitoring capabilities. The upgraded RAPID addresses the complexities of Synthetic Aperture Radar (SAR) flood mapping in diverse environments, including snow-covered and arid regions, enhancing adaptability and consistency across multiple SAR satellites such as ESA Sentinel-1, CSA RADARSAT Constellation Mission (RCM), MDA RADARSAT-2 (RS2), and Capella. Meanwhile, SWD brings a method to automatically map flood extents from high-resolution optical images, including Planet, Sentinel-2, and Landsat, during clear weather conditions. GLOCAL and EFRA are tailored for depth estimation from the generated HR remotely sensed flood extents, and HR or VHR topography. Both algorithms