Estimates and dynamics of surface water extent in the Yangtze Plain from Sentinel-1&2 observations

• The proposed method can map surface water monthly regardless of weather conditions. • Surface water changes in the Yangtze Plain is highly complex viewed at month scale. • The lakes over the Yangtze Plain show distinct seasonal characteristics and classes. The dynamics of surface water in the Yangtze Plain is complex, influenced by the coupled impacts of climate change and intensifying human activities. However, remote sensing observations often encounter challenges in this region due to persistent cloud cover, impeding comprehensive studies of water dynamics. This study introduces a novel Monthly Surface Water Mapping (MSWM) approach combining time series Sentinel-1&2 images, resulting in the generation of a Monthly Surface Water Extent (MSWE) dataset. This dataset boasts a spatial resolution of 10 m and a temporal resolution of one month. Validation results indicate the MSWE exhibits a significant improvement of 19.6 % and 8.9 % in F1 score compared to the temporally-aligned Global Surface Water dataset and thresholding results, respectively. The MSWE demonstrates robust spatial precision and temporal tracking capabilities, even in complex scenes and cloudy conditions. The seasonal fluctuation of surface water bodies in the Yangtze Plain was computed using the monthly dataset and a harmonic analysis model. The results characterized distinct monthly change patterns for surface water extent, allowing for the identification and quantification of four lake classes: 6 seasonal lakes, 11 weak seasonal lakes, 21 generally stable lakes, and 46 stable lakes. The MSWM stands out for its capacity to estimate surface water extent regardless of weather conditions, showcasing promising potential for extension to other regions characterized by constant cloud cover. Furthermore, the availability of a monthly water dataset contributes significantly to enhancing our spatiotemporal understanding of surface water dynamics, offering substantial benefits for sustainable water resources management.