A Novel Soil Moisture Retrieval Algorithm for FY-3E GNOS-R Leveraging Multi-Angle Observations

Abstract Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm3) for bare, low vegetation, and dense vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques.