Development of routine flood mapping using SAR satellite observation for long-term monitoring system in the flood-prone regions, Cambodia

Monitoring inland surface water in near-real time is a key challenge in cloud-prone tropical regions.  Recently, Synthetic Aperture Radar (SAR) products have been widely used to detect surface water. Our area of interest, the Tonle Sap Lake region is a complex environment where very large areas and floodplains are partially or fully submerged seasonally. As the population living around the lake strongly rely on the seasonal flooding dynamics for their socio-economic activities and can at the same time be at risk due to extreme flooding events, it is of main importance to develop tools for the monitoring of flooded areas. In this context, we are adopting and evaluating an algorithm which relies on parametric thresholding, and region growing approaches applied over time series of Sentinel-1 (S1) SAR backscatter images (VV and VH). To evaluate the produced water extent maps based on VV and VH polarizations, we used a cross evaluation using multi-sensor products: high-resolution optical data such as Sentinel-2 (S2) and the coarser resolution Sakamoto flood extend derived from MODIS product. The comparison is made using the Critical Success Index (CSI) and Kappa coefficient performance metrics. During the dry season, the VV polarization demonstrated very good performance using S2-derived maps as a reference, with CSI of 0.84 and a Kappa coefficient of 0.91, indicating highly accurate surface water detection. Performance was similar using the Sakamoto product as a reference (CSI=0.87). However, performance dropped during the rainy season, with the VV polarization's CSI decreasing to 0.76 comparing S2, reflecting challenges in detecting water in the extensive flooded vegetation areas. VH polarization consistently overestimated water extent by misclassifying wet vegetation and rice fields. A merge of VV and VH product yielded an intermediate performance, improving water detection in vegetated areas compared to VV alone. This comprehensive, multi-sensor and multi-season assessment clarifies the specific strengths of each S1 polarization, showing VV's superiority for open water mapping, especially in the dry season. It underscores the importance of selecting the appropriate product (VV for open water, merged for total inundation) and considering seasonal context for operational monitoring, thereby demonstrating the algorithm's robustness while also defining its operational limitations.