Using CYGNSS and L-Band Radiometer Observations to Retrieve Surface Water Fraction: A Case Study of the Catastrophic Flood of 2022 in Pakistan

Spaceborne global navigation satellite system reflectometry (GNSS-R) has shown potential for terrestrial applications. The feasibility of monitoring and mapping surface inundation using Cyclone Global Navigation Satellite Systems (CYGNSSs) has been demonstrated in literature. Nevertheless, most studies have only classified the surface into two states, inundated and noninundated, which do not meet the requirements of refined hydrological modeling. Here, we propose a new calculation flow to retrieve the surface water fraction (Wf) based on CYGNSS data by considering fractional and dynamic surface water monitoring for the first time. First, a newly proposed physics-based algorithm was utilized for coupling CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature (Tb) to attenuate the effects of surface roughness and vegetation on CYGNSS observations. After removing the effects of surface roughness and vegetation, the SR was assumed to be equal to the sum of inundated SR and the noninundated SR multiplied by the corresponding fraction. Thus, Wf was obtained on a per-pixel basis. Next, Wf retrieval results were validated using the case of the catastrophic floods of 2022 in Pakistan. A comparison with the Wf obtained using SMAP Tb showed high spatial and temporal similarity; nevertheless, the CYGNSS results had a higher spatiotemporal resolution. A quantitative pixel-by-pixel comparison with the global flood monitoring (GFM) data derived from Sentinel-1 radar imagery, which had a finer resolution (similar to 10 m) showed that the average overall accuracy of Wf retrieval was 64.44% and the average commission error was 17.78%. Finally, we quantified the impact of land use and land cover on the retrieval results while analyzing the advantages and limitations of the method. Thus, this study provides new insights for the future use of spaceborne GNSS-R data for monitoring inland water bodies.