Filling gaps in optical satellite images using sar imagery for flood mapping: case studies in China, Spain, and Somalia

Flood mapping from optical satellite imagery is often hindered by persistent cloud cover, while historical-water-occurrence-based gap-filling may fail when flood extent exceeds previous records. This study develops a Sentinel-1/Sentinel-2 framework for flood mapping in China, Spain, and Somalia. Sentinel-1-derived water maps provide cloud-independent dynamic references. A Markov chain first fills cloud-covered pixels whose adjacent satellite observations show consistent water or non-water states, and a spatiotemporal Markov random field then reconstructs the remaining uncertain pixels using spatial neighborhood coherence and temporally weighted observations. The gap-filled Sentinel-2 maps are further combined with Sentinel-1 maps to generate denser flood sequences. Across the three cases, the framework achieved 10 m resolution, mIoU values of 0.82–0.99, and effective mapping intervals of 1.6–3.6 days. Independent PlanetScope validation, component analysis, confidence intervals, and significance tests supported its reliability. The framework can provide timely flood-extent information for disaster response and flood-risk management.