Enhancing Soil Moisture Estimates through the Fusion of SMAP and GNSS-R Data at 3-km Resolution for Daily Mapping

High-resolution, large-scale near-surface soil moisture information is critical for many hydrology and climate applications, yet traditional radars and radiometers often fall short of providing information at the required spatial and temporal scales. This study proposes a method for fusing Soil Moisture Active Passive (SMAP) data with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) measurements from the Cyclone GNSS (CYGNSS) and Spire near-nadir GNSS-R missions, generating soil moisture products at 3- and 9-km resolutions. GNSS-R uses L-band signals that are sensitive to changes in biogeophysical parameters, such as soil moisture. A linear regression-based algorithm retrieves soil moisture from both CYGNSS and Spire data, which, despite showing biases relative to one another, exhibit similar sensitivities to soil moisture variations. The 9-km fused product integrates observed and interpolated GNSS-R estimates to complement daily SMAP 9-km maps, while the 3-km product refines GNSS-R retrievals using available SMAP data. This approach is validated against in-situ measurements and the SMAP/Sentinel 3-km product over mainland Australia for 2021. Our findings indicate a median unbiased root-mean-square error (ubRMSE) of 0.049 cm 3 cm -3 for the 3-km product and 0.054 cm 3 cm -3 for the 9-km product, both of which are comparable to SMAP's ubRMSE of 0.054 cm 3 cm -3 . The fused products provide daily soil moisture retrievals with accuracy comparable to SMAP while significantly improving temporal resolution. The 3-km product, in particular, captures finer spatial variability, offering a more detailed representation of soil moisture dynamics.