Soil moisture retrieval using GNSS signal-to-noise ratio data based on an improved optimal arc selection method

Abstract

Abstract Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) can be used to monitor soil moisture by establishing a relationship between phase and soil moisture. Therefore, the accuracy of the phase value is very important. However, topography and vegetation can introduce errors in the phase values when processing the raw signal-to-noise ratio reflection component (SRC). This study proposes an optimal arc selection (OAS) method to overcome this limitation. The novelty of this method is the use of entropy to evaluate the accuracy of curve fitting and the use of a particle swarm optimization (PSO) algorithm to search for the optimal elevation range of SRC. We processed SNR data from 3 GNSS stations and provided the verification results through in-situ soil moisture measurements. The results showed that the phase values calculated using the OAS method were more accurate than those calculated using the conventional method. The new method improved the agreement between GNSS-derived soil moisture and in-situ measurements, with a reduction of 29% in root mean square error (RMSE) and 31% in mean absolute error (MAE). This suggests that the OAS method can improve the capacity of soil moisture retrieval in undulating terrain areas and promote the development of GNSS-IR.