This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasquère, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios.

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm³/cm³, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm³/cm³, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval model based on LSTM (long–short term memory) is proposed to improve accuracy and robustness as to the impacts of vegetation cover and soil surface roughness. The Oceanpal GNSS-R data obtained from the experimental campaign at the Valencia Anchor Station are used as the main input data, and the TB (brightness temperature) and TR (soil roughness and vegetation integrated attenuation coefficient) outputs of the ELBARA-II radiometer are used as auxiliary input data, while field measurements with a Delta-T ML2x ThetaProbe soil moisture sensor were used for reference and validation. The results show that the LSTM model can be used to retrieve soil moisture, and that it performs better in the data fusion scenario with GNSS-R and radiometer. The STD of the multi-satellite fusion model is 0.013. Among the single-satellite models, PRN13, 20, and 32 gave the best retrieval results with STD = 0.011, 0.012, and 0.007, respectively.

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology has emerged as a research hotspot in the remote sensing field in recent years due to its advantages of low cost and high precision for soil moisture monitoring. Addressing the issue that fixed elevation angle intervals struggle to adapt to the varying signal characteristics of different satellites, this paper proposes an adaptive elevation angle interval selection method based on wavelet transform. This method utilizes wavelet transform to analyze the time-frequency characteristics of the residual Signal-to-Noise Ratio (SNR) signal, calculates the ratio sequence of the main frequency component strength to the noise component strength, and sets a threshold to automatically determine the retrieval elevation angle interval for each satellite, thereby improving the accuracy of feature parameter extraction. The results show the following: ① Compared to traditional fixed elevation angle intervals (5–20° and 5–30°), the proposed method significantly enhances soil moisture retrieval accuracy. ② For the averaged phase feature parameters calculated within the algorithm-selected intervals for all satellites, the R2 and RMSE are 0.925 and 0.55%, respectively, representing improvements of 3.1% and 14.2% compared to the original results. ③ For signals from low-quality reflection zones, R2 increased from 0.728 to 0.839 (a 13.2% improvement), while RMSE decreased from 1.045 to 0.806 (a 22.9% reduction). This method effectively adapts to the quality attenuation characteristics of satellite signals across different reflection zones, providing an optimized elevation angle interval selection strategy for GNSS-IR soil moisture retrieval.