Global soil moisture mapping at 5 km by combining GNSS reflectometry and machine learning in view of HydroGNSS

The potential of GNSS reflectometry (GNSS-R) for the monitoring of soil and vegetation parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely investigated in recent years.

In view of the ESA's HydroGNSS mission, planned to be launched in 2024, this study has explored the possibility to map SM at global scale and relatively high resolution of about 0.05° (corresponding approximately to 5 Km) using GNSS-R observations, by implementing and comparing two retrieval algorithms based on machine learning techniques, namely Artificial Neural Networks (ANN) and Random Forest Regressors (RF). Waiting for HydroGNSS commissioning and operation, the NASA's Cyclone GNSS (CyGNSS) land observations have been considered for this scope. Taking advantage of the versatility of both machine learning techniques, several combinations of input data, including CyGNSS observables and auxiliary information, have been exploited and the role of GNSS-R and auxiliary data has been assessed. Given the lack of global SM data at 0.05° resolution, the following novel strategy has been implemented to establish the training set: as first, training has been carried out at lower resolution by considering as target the SMAP SM on EASE-Grid 36 km. Then the trained algorithms have been applied to CyGNSS data at 0.05° to obtain global SM maps at this resolution. Finally, the SM at 0.05° has been validated against ISMN, to keep training and validation as much independent as possible. The two retrieval techniques exhibited similar accuracies and computational cost, with correlation coefficient R ≃ 0.9 between estimated and target SM computed globally, and RMSE ≃ 0.05 (m³/m³). Moreover, the SM maps at 0.05° revealed some finer details and small-scale patterns that are not shown by the original SMAP SM data at 36 km. Regardless of the ML technique applied, this study confirmed the promising potential of GNSS-R for the global monitoring of SM at improved resolution with respect to SM products available from microwave satellite radiometers.