Comparison of a Semiempirical Algorithm and an Artificial Neural Network for Soil Moisture Retrieval Using CYGNSS Reflectometry Data

This research, carried out within the framework of the European Space Agency’s second Scout mission (HydroGNSS), seeks to utilize CYGNSS Level 1B products over land for soil moisture estimation. The approach involves a novel physically based algorithm, which inverts a semiempirical forward model of surface reflectivity proposed in the literature. An Artificial Neural Network (ANN) algorithm has also been developed. Both methods are implemented in the frame of the HydroGNSS mission to make the most of the reliability of an approach rooted in a physical background and the power of a data-driven approach that may suffer from limited training data, especially right after launch. The study aims to compare the results and performance of these two methods. Additionally, it intends to evaluate the impact of auxiliary data. The static auxiliary data include topography, Above Ground Biomass (AGB), land cover, and surface roughness. Dynamic auxiliary data include Vegetation Water Content (VWC) and Vegetation Optical Depth (VOD) from Soil Moisture Active Passive (SMAP), as well as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Moderate Resolution Imaging Spectroradiometer (MODIS), on enhancing the accuracy of retrievals. The algorithms were trained and validated using target soil moisture values derived from SMAP L3 global daily products and in situ measurements from the International Soil Moisture Network (ISMN). In general, the ANN approach outperformed the semiempirical model with RMSE = 0.047 m3 m−3 and R = 0.91. We also introduced a global stratification framework by intersecting land cover classes with climate regimes. Results show that the ANN consistently outperforms the semiempirical model in most strata, achieving around RMSE = 0.04 m3 m−3 and correlations above 0.8. The semiempirical model, however, remained more stable in data-scarce conditions, highlighting complementary strengths for HydroGNSS.