Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm³cm⁻³, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stability.

In terrestrial remote sensing applications, the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has demonstrated its worth. The application to land surface soil moisture (SSM) detection is particularly intriguing since it has the ability to provide fine-scale results to supplement traditional satellite-based active and passive missions. To date, many retrieval algorithms for spaceborne GNSS-R have been developed in order to produce SSM products. However, detailed product reliability and robustness evaluations are still absent. In this study, the satellite-based microwave radiometry product, the model-base product, and in-situ measurements from the Chinese soil moisture monitoring network with over 1800 ground stations during the year 2018 were used to evaluate the CYclone Global Navigation Satellite System (CYGNSS) mission Level-3 SSM products released by the University Corporation for Atmospheric Research (UCAR) and the University of Colorado at Boulder (CU). Typical relative skill metrics and triple collocation-based metrics, along with corresponding confidence intervals, are given to analyze the performance. According to the pixel-by-pixel validation and overall statistical findings, the results reveal that the current CYGNSS-based SSM exhibits low performance in southern China when compared to the radiometry-based data with a low R² (median R²=0.09) and the ubRMSD 0.055 cm³cm^-3, which is poorer than the results from SMAP against in-situ measurements (median R²=0.25, ubRMSD=0.046 cm³cm^-3). To acquire better results to support the related operational applications in the future, the new enhanced retrieval algorithms and high-accuracy calibration referenced data must be used.