Chao Wang

Continuous and accurate acquisitions of surface water distribution are important for water resources evaluation, especially high-precision flood monitoring. During surface water extraction, optical imagery is strongly affected by clouds, while synthetic aperture radar (SAR) imagery is easily influenced by numerous physical factors; thus, the water extraction method based on single-sensor imagery cannot obtain high-precision water range under multiple scenarios. Here, we integrated the radar backscattering coefficient of ground objects into the Normalized Difference Water Index to construct a novel SAR and Optical Imagery Water Index (SOWI), and the water ranges of five study areas were extracted. We compared two previous automatic extraction methods based on single-sensor imagery and evaluated the accuracy of the extraction results. Compared with using optical and SAR imagery alone, the accuracy of all five regions was improved by up to 1–18%. The fusion-derived products resulted in user accuracies ranging 95–99% and Kappa coefficients varying by 85–97%. SOWI was then applied to monitor the 2021 heavy rainfall-induced Henan Province flood disaster, obtaining a time-series change diagram of flood inundation range. Our results verify SOWI’s continuous high-precision monitoring capability to accurately identify waterbodies beneath clouds and algal blooms. By reducing random noise, the defects of SAR are improved and the roughness of water boundaries is overcome. SOWI is suitable for high-precision water extraction in myriad scenarios, and has great potential for use in flood disaster monitoring and water resources statistics.