Multi-Sensor SAR-based Flood Mapping for High-Temporal Assessment of the 2020 Flood Event in Hue, Vietnam

Kurzfassung

The 2020 flood event in Hue, Central Vietnam, ranks among the most devastating in recent history, highlighting the critical need for high-temporal monitoring to improve flood risk management and disaster response. The Thua Thien Hue provinces topography is marked by a complex interplay of mountainous regions, low-lying plains, and an extensive lagoon system, making it highly vulnerable to diverse flood dynamics. Situated along the Huong River, where steep upstream gradients transition into flat downstream areas, Hue faces heightened risks of fluvial, pluvial, and coastal flooding, particularly during extreme weather events. The 2020 flood, driven by prolonged inundation lasting several weeks due to consecutive typhoons and tropical depressions, including Typhoons Molave and Goni, caused extensive damage and severely tested local disaster response capacities. This study leverages Earth Observation (EO) data from Sentinel-1 Synthetic Aperture Radar (SAR), Cosmo-Skymed, and TerraSAR-X acquisitions to capture the flood dynamics of the 2020 event. Threshold-based change detection algorithms were applied to delineate flood extents, revealing spatial and temporal flood evolution. Drone imagery and in situ measurements from the early flood period provided critical reference for accuracy assessment. Additionally, flood modeling using HEC-RAS simulated the hydrodynamic behavior of the Huong River system, offering detailed insights into water flow, inundation extent, and depths. Results from the HEC-RAS model were cross-compared with SAR-derived data. The results are illustrating the prolonged rainfall’s extreme challenge to flood risk management systems. The findings underscore the importance of understanding flood dynamics for improving flood management and adaptation processes. This research demonstrates the effectiveness of integrating multi-sensor EO data and hydrodynamic modeling for disaster risk reduction, providing scalable methodologies to address the increasing frequency of extreme weather events. Future work will focus on integrating additional EO sensors and enhancing automation in flood detection to further improve disaster response timeliness and accuracy.