Mapping Surface Water Fraction Over the Pan-Tropical Region Using CYGNSS Data

A new method, which integrates multivariable consisting of soil moisture (SM) active passive (SMAP)-derived SM and vegetation optical depth, the water seasonality, geolocation, digital elevation model (DEM), slope, and biomass as inputs and adopts the technique of bootstrap aggregation of regression trees (BARTs), is proposed for retrieving monthly surface water fraction (SWF) at a spatial resolution of 0.025 degrees from cyclone global navigation satellite system (CYGNSS) data. The model is trained using surface water microwave product series (SWAMPS) data with a coarser resolution of 25 km and then applied to CYGNSS data with an enhanced resolution of 0.025 degrees to generate high-resolution water maps. The resulting CYGNSS SWF (CSWF) maps are evaluated by comparing them with other water data sources, namely, SWAMPS, global surface water (GSW), and global surface water dynamics (GLADs), as well as ground measurements. A quadruple collocation (QC) analysis indicates that the CSWF results exhibit the lowest error variance among the four SWF datasets. Furthermore, additional testing with water level (WL) measurements demonstrates a strong correlation with station data and clear seasonal patterns. Notably, the CSWF estimates significantly improve spatial coverage compared to both optical data (GSW and GLAD) with enhanced spatial resolution and the coarser SWAMPS data. This study underscores the effectiveness and efficiency of CSWF estimates, highlighting their potential as a valuable complement to existing microwave- and optical-based surface water products.