Refined semi-empirical models for soil moisture retrieval in spaceborne GNSS-Reflectometry: Evaluation across diverse land cover types

Abstract

The satellite-based Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a promising technique for detecting surface soil moisture (SSM), which plays a pivotal role in various applications. Different approaches have been developed for SSM retrieval, including parametric semi-empirical models and non-parametric machine learning methods. This study, however, specifically focuses on constructing and evaluating semi-empirical fit models. To this end, the study compares the effectiveness of our enhanced pixel-by-pixel model, the land cover-based linear model, and the Reflectivity-Vegetation-Roughness (R-V-R) model across different land surface types, aiming to both evaluate their efficacy and identify potential limitations. In the assessment, various factors were taken into consideration, such as the correction for vegetation and roughness attenuation, fitting functions employed, and the utilization of a lookup table (LUT). The results of the evaluation showed variations in the performance of the retrieval models across different land cover types, highlighting the impact of the choice of fitting functions and attenuation correction strategies on the accuracy of soil moisture retrieval. The pixel-by-pixel model demonstrated the highest prediction accuracy, with an unbiased root mean square difference (ubRMSD) of 0.056 cm3/cm3 and a correlation coefficient of 0.896. By showcasing these outcomes, this research underscores the significance of accounting for surface conditions and integrating relevant data to enhance the accuracy of GNSS-R SSM retrieval, thereby contributing to the advancement of SSM monitoring methodologies.