A New Soil Moisture Retrieval With Considering the Underlying Surface Factors From CYGNSS

Soil moisture is vital for studying hydrological processes, vegetation growth, and climate change. Recently, Global Navigation Satellite System-Reflectometry (GNSS-R) technology has shown great potential for soil moisture monitoring. However, most existing GNSS-R algorithms rely empirically on multiple land surface parameters, which increases model complexity and the risk of overfitting. This study investigates underlying surface influencing factors for soil moisture retrieval and developes a new soil moisture retrieval with considering the factors that influence the relationship between GNSS-R measurements and surface soil moisture, using Cyclone Global Navigation Satellite System (CYGNSS) data in the middle and lower reaches of the Yangtze River (23.5°N-35°N, 110°E-123°E). Monthly CYGNSS reflectivity and SMAP soil moisture data from 2019 to 2024 are analyzed with consideration of influencing factors such as elevation, slope, biomass, land use, tree height, and Normalized Difference Vegetation Index (NDVI). Results show the following key findings: (1) Reflectivity exhibits a significant correlation with soil moisture, with 84.05% displaying a positive correlation. (2) Over the six-year period from 2019 to 2024, the correlation between SMAP soil moisture and CYGNSS reflectivity shows clear seasonal variability, with lower correlations in spring and autumn and higher correlations in summer and winter, peaking in summer. (3) Geodetector analysis revealed that land use had the most significant effect on soil moisture in the single-factor analysis, while biomass and reflectivity had the strongest interaction in the two-factor analysis, characterized by nonlinear enhancement. (4) A retrieval scheme was designed based on the influencing factors, and its accuracy was validated using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm. The root mean square error (RMSE) of the soil moisture retrieval using all selected features was 0.0526 cm3/cm3. The major feature group achieved an RMSE of 0.0527 cm3/cm3, while the minor feature group yielded an RMSE of 0.0611 cm3/cm3. Spatial RMSE analysis further demonstrated the effectiveness of Geodetector in guiding feature selection. The retrieval results were further validated using the SSM V2.0 high-resolution soil moisture product, which has been extensively evaluated against in situ measurements, demonstrating strong consistency and confirming the robustness and practical applicability of the proposed method.