Enhancing surface water mapping and monthly dynamics monitoring with a stepwise gap-filling method

Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.