Multi-Sensor SAR-Based Flood Mapping for High-Temporal Monitoring of the 2020 Flood Event in Thừa Thiên Huế, Vietnam

The 2020 flood season in Thừa Thiên Huế province, Central Vietnam, was among the most severe in recent history, driven by consecutive tropical storms and prolonged heavy rainfall. Between October and November 2020, a series of storms, including Tropical Storm Linfa, Typhoon Molave, and Typhoon Goni, brought intense precipitation, causing widespread inundation and significant damage to infrastructure and livelihoods. The hydrological complexity of the region, characterized by mountainous terrain, low-lying floodplains, and the extensive Tam Giang-Cau Hai lagoon system, further exacerbated the flood impacts, underscoring the need for advanced monitoring tools to capture the event's dynamics.

This study leverages multi-sensor Synthetic Aperture Radar (SAR) data, including Sentinel-1, Cosmo-Skymed, and TerraSAR-X, to create a high-temporal flood inventory for this hydrologically challenging region. Multi-temporal SAR intensity and coherence data were processed using threshold-based change detection algorithms and normalized difference indices to delineate flood extents. These SAR-based methods, immune to cloud cover, provided continuous observations despite the adverse weather conditions during the flood. Validation was performed using in-situ flood markers and drone imagery, ensuring accuracy in the derived flood maps. To complement SAR data, hydrodynamic modeling using HEC-RAS simulated water flow, inundation depths, and river system behavior, enabling cross-comparison with SAR-derived flood extents.

The 2020 flood event highlighted a challenge often associated with satellite-based flood mapping: image acquisitions seldom capture the peak of the flood. However, the high temporal resolution provided by the combined SAR datasets allowed researchers to track the pulse of the flood, revealing its evolution and alignment with storm events and precipitation patterns. This capability provided critical insights into the timing, extent, and dynamics of flooding, even in a region with complex topography and hydrology.

The high-temporal flood inventory produced in this study enhances understanding of flood dynamics across diverse land-cover types, enabling improved flood risk assessments and adaptive management. The outcomes not only advance flood monitoring methodologies for Vietnam but also demonstrate the value of integrating Earth Observation data with hydrological modeling to support disaster risk reduction efforts. This approach offers scalable solutions for other regions prone to extreme weather events, contributing to global efforts in informed decision-making and adaptive flood management strategies.