Concept and Initial Realization of PHYSER—A Physics-Based Framework for Spaceborne GNSS-R Soil Moisture Retrieval with Accurate Soil Reflectivity

Global soil moisture (SM) observation using spaceborne global navigation satellite system reflectometry (GNSS-R) is becoming an effective supplement and enhancement to traditional microwave remote sensing observations. The state-of-the-art SM retrieval frameworks for spaceborne GNSS-R are based on empirical or semiempirical modeling, which relies on reference SM data from other sources (e.g., microwave radiometer or in situ SM products) to eliminate the effects of land surface errors (e.g., surface roughness and vegetation). This study, for the first time, defines a generic framework for physics-based spaceborne GNSS-R SM retrieval, namely, PHYSER, and proposes initial strategies to realize the framework. The framework concept is devoted to deriving accurate soil reflectivity and retrieving SM by estimating soil permittivity from Fresnel reflection coefficients, thus wholly independent of external SM products. It assumes that GNSS-R surface reflectivity and its related soil reflectivity are affected by observed system errors and land surface errors. The framework is initially realized by deriving accurate soil reflectivity from empirical corrections to avoid the grand challenge of building a forward-scattering model under complex land surface conditions. Accurate soil reflectivity is derived through 2 steps: (a) Surface Reflectivity CALibrating (SuR-CAL), aiming to calibrate the system errors by using the reflectivity of inland water bodies, and (b) Soil Reflectivity CORrecting (SoR-COR), aiming to correct the errors mainly from surface roughness and vegetation using the zeroth-order radiative transfer ( τ – ω ) model. The framework is validated using 1-year data from BuFeng-1 A/B (BF-1) twin satellites. The findings and conclusions mainly include the following: (a) PHYSER reveals that independent spaceborne GNSS-R SM retrieval without reference SM products is achievable through the derivation of accurate soil reflectivity. (b) Land surface errors play a more significant role in influencing soil reflectivity than system errors. The SuR-CAL and SoR-COR steps improve the correlation coefficient ( R ) between BF-1 reflectivity and the Soil Moisture Active Passive (SMAP) SM up to ~7% and ~36%, respectively. (c) The BF-1 SM estimates agree well with the SMAP SM and the fifth-generation ECMWF atmospheric reanalysis (ERA5) SM, with an unbiased root-mean-square difference (ubRMSD) of 0.067 m 3 ·m −3 and a mean absolute error (MAE) of 0.073 m 3 ·m −3 against SMAP and a ubRMSD of 0.079 m 3 ·m −3 and an MAE of 0.088 m 3 ·m −3 against ERA5. The BF-1 SM also agrees well with the in situ measurements with mean unbiased root-mean-square error = 0.055 m 3 m −3 and MAE = 0.066 m 3 m −3 . The proposed framework provides a promising physics-based concept to independently retrieve SM for the GNSS-R community, which is expected to considerably support the in-orbit and next-generation GNSS-R missions to promote operational SM retrieval and applications.