A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers
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 f...
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MDPI AG
2025-04-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/8/837 |
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| author | Yadong Yao Jixuan Yan Guang Li Weiwei Ma Xiangdong Yao Miao Song Qiang Li Jie Li |
| author_facet | Yadong Yao Jixuan Yan Guang Li Weiwei Ma Xiangdong Yao Miao Song Qiang Li Jie Li |
| author_sort | Yadong Yao |
| collection | DOAJ |
| description | 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<sup>2</sup> 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<sup>2</sup> (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R<sup>2</sup> 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<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-ec7d79ec8e4b4ff1b25bb59cd4db713d |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-ec7d79ec8e4b4ff1b25bb59cd4db713d2025-08-20T03:14:20ZengMDPI AGAgriculture2077-04722025-04-0115883710.3390/agriculture15080837A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation CoversYadong Yao0Jixuan Yan1Guang Li2Weiwei Ma3Xiangdong Yao4Miao Song5Qiang Li6Jie Li7College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaState Key Laboratory of Aridland Crop Science, Ministry and Province Co-Established, Lanzhou 730070, ChinaState Key Laboratory of Aridland Crop Science, Ministry and Province Co-Established, Lanzhou 730070, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaThe 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<sup>2</sup> 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<sup>2</sup> (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R<sup>2</sup> 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<sup>2</sup> 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.https://www.mdpi.com/2077-0472/15/8/837GNSS-IRSMCsatellite signalenvironmental featuresNRBO-XGBoost |
| spellingShingle | Yadong Yao Jixuan Yan Guang Li Weiwei Ma Xiangdong Yao Miao Song Qiang Li Jie Li A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers Agriculture GNSS-IR SMC satellite signal environmental features NRBO-XGBoost |
| title | A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers |
| title_full | A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers |
| title_fullStr | A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers |
| title_full_unstemmed | A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers |
| title_short | A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers |
| title_sort | gnss ir soil moisture inversion method considering multi factor influences under different vegetation covers |
| topic | GNSS-IR SMC satellite signal environmental features NRBO-XGBoost |
| url | https://www.mdpi.com/2077-0472/15/8/837 |
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