Can We Reconstruct Cloud-Covered Flooding Areas in Harmonized Landsat and Sentinel-2 Image Time Series?

Floods pose severe global risks. While Earth observation satellites offer extensive flood monitoring, cloud cover limits the effectiveness of optical satellite imagery. This paper develops a novel reconstruction method for spatially seamless time series flood extent mapping. Utilizing a fine-tuned large foundation model, the proposed method identifies water bodies and then reconstructs cloud-covered flooding areas based on water occurrence data in the Global Surface Water dataset. The reconstructed water maps are finally refined by spatiotemporal Markov random field modeling to delineate flood areas. Evaluated with Harmonized Landsat and Sentinel-2 datasets, the developed method achieves seamless flood extent mapping at 2-3-day intervals and 30-m resolution. This paper offers an effective approach for flood monitoring under cloudy and rainy conditions, supporting emergency response and disaster management.