An Improved Ground-Based GNSS-R Soil Moisture Retrieval Algorithm Incorporating Precipitation Effects

Abstract

Global navigation satellite system reflectometry (GNSS-R) is a promising technique for retrieving soil moisture (SM), with advantages including high spatiotemporal resolution, low-cost, and low-power consumption. Compared to space-borne and airborne platforms, ground-based GNSS-R enables continuous SM monitoring in targeted regions like farmland overextended periods with high resolution. However, reflected GNSS signal penetration depth is affected by rainfall, degrading SM retrieval accuracy during precipitation. SM at a depth of 5–10 cm is a key focus of research in the agricultural field. However, the root mean square error (RMSE) of SM at a depth of 5 cm resolved by GNSS-R can reach 0.15 m3/m3 during rainy weather, which is much higher than the average accuracy level of 0.05 m3/m3 during nonrainy weather. To address this issue, we collected over one year of observational data from a ground-based GNSS-R station deployed within a farmland. In the data processing, reflectance was first calculated from intermediate frequency (IF) data. Subsequently, initial SM was retrieved from the Fresnel reflection coefficients using the Topp empirical model. Analysis revealed that precipitation events induced anomalies in the retrieved reflectance, leading to significant deviations between the GNSS-R derived SM and in situ time domain reflectometry (TDR) measurements. Leveraging this dataset, we proposed a novel ground-based GNSS-R correction algorithm integrating rainfall intensity segmentation with real-time signal-to-noise ratio (SNR) modulation. In situ TDR measurements evaluated the results. The training set RMSE improved to 0.0440 m3/m3, and the test set reached 0.0264 m3/m3.