Enhancing Monthly Flood Monitoring in Wetlands Through Spatiotemporal Fusion of Multi-Sensor SAR Data: A Case Study of Chen Lake Wetland (2020–2024)

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems.