A surface water mapping framework combining optical and radar remote sensing and its application in China

Abstract

Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. In this study, we propose a Seamless Surface Water Mapping Framework (SSWMF) which synergizes both optical (MODIS, Landsat 8, Sentinel-2) and SAR (Sentinel-1) imageries. The validity of SSWMF was first proved over the middle and lower reaches of the Yangtze River of China with abundant lake resources, showing an overall accuracy of 90.72%, and the results indicate that SSWMF can provide surface water map with higher spatial and temporal continuity compared to the Joint Research Centre Global Surface Water dataset. Multi-source validation showed that the SSWMF-derived surface water maps can well capture the temporal fluctuation and spatial heterogeneity of water resources over China, with an overall accuracy of 92.39%. Overall, our results suggest that the proposed water mapping framework is promising and is readily applicable to large-scale water resource management and drought/flood monitoring.