A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers

Yadong Yao, Jixuan Yan (), Guang Li, Weiwei Ma, Xiangdong Yao, Miao Song, Qiang Li and Jie Li
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Yadong Yao: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Jixuan Yan: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Guang Li: State Key Laboratory of Aridland Crop Science, Ministry and Province Co-Established, Lanzhou 730070, China
Weiwei Ma: State Key Laboratory of Aridland Crop Science, Ministry and Province Co-Established, Lanzhou 730070, China
Xiangdong Yao: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Miao Song: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Qiang Li: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Jie Li: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China

Agriculture, 2025, vol. 15, issue 8, 1-25

Abstract: The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R 2 of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R 2 (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R 2 outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R 2 values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion.

Keywords: GNSS-IR; SMC; satellite signal; environmental features; NRBO-XGBoost (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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