Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning
Abstract This study introduces two innovative methods—Three-frequency pseudorange combination (TFPC) and Three-frequency carrier phase combination (TFCPC)—for estimating soil moisture using GNSS-IR technology. Unlike traditional methods that require separating direct and reflected signals, these app...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09029-4 |
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| author | Yintao Liu Chao Ren Hongjuan Shao |
| author_facet | Yintao Liu Chao Ren Hongjuan Shao |
| author_sort | Yintao Liu |
| collection | DOAJ |
| description | Abstract This study introduces two innovative methods—Three-frequency pseudorange combination (TFPC) and Three-frequency carrier phase combination (TFCPC)—for estimating soil moisture using GNSS-IR technology. Unlike traditional methods that require separating direct and reflected signals, these approaches leverage carrier phase and pseudorange multipath errors to improve accuracy. The new methods eliminate the impact of geometrical factors and atmospheric delays. By applying minimum covariance determinant (MCD) and moving average filter (MAF), the study effectively detects and corrects outliers in delay phases, enhancing the quality of the data. Using data from the Plate Boundary Observatory (PBO) H2O project, the study finds that combining corrected delay phases from multiple satellites improves correlations between estimated and actual soil moisture values. The TFPC method achieves correlation coefficients of 0.82 and 0.87 with multivariate linear regression (MLR) and radial basis function neural network (RBFNN) models, while the TFCPC method yields even better results at 0.85 and 0.91, respectively. These findings represent a significant advancement in high-precision soil moisture estimation, offering valuable implications for applications in agriculture, weather forecasting, and environmental monitoring. |
| format | Article |
| id | doaj-art-e1faadcb7a1742ca845c4b17865eafdd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e1faadcb7a1742ca845c4b17865eafdd2025-08-20T03:38:15ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-09029-4Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learningYintao Liu0Chao Ren1Hongjuan Shao2College of Geomatics and Geoinformation, Guilin University of TechnologyCollege of Geomatics and Geoinformation , Guilin University of TechnologyCollege of Physics and Electronic Information Engineering, Guilin University of TechnologyAbstract This study introduces two innovative methods—Three-frequency pseudorange combination (TFPC) and Three-frequency carrier phase combination (TFCPC)—for estimating soil moisture using GNSS-IR technology. Unlike traditional methods that require separating direct and reflected signals, these approaches leverage carrier phase and pseudorange multipath errors to improve accuracy. The new methods eliminate the impact of geometrical factors and atmospheric delays. By applying minimum covariance determinant (MCD) and moving average filter (MAF), the study effectively detects and corrects outliers in delay phases, enhancing the quality of the data. Using data from the Plate Boundary Observatory (PBO) H2O project, the study finds that combining corrected delay phases from multiple satellites improves correlations between estimated and actual soil moisture values. The TFPC method achieves correlation coefficients of 0.82 and 0.87 with multivariate linear regression (MLR) and radial basis function neural network (RBFNN) models, while the TFCPC method yields even better results at 0.85 and 0.91, respectively. These findings represent a significant advancement in high-precision soil moisture estimation, offering valuable implications for applications in agriculture, weather forecasting, and environmental monitoring.https://doi.org/10.1038/s41598-025-09029-4GNSS-IRSoil moistureThree-frequency pseudorange combinationThree-frequency carrier phase combinationRobust estimateMachine learning |
| spellingShingle | Yintao Liu Chao Ren Hongjuan Shao Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning Scientific Reports GNSS-IR Soil moisture Three-frequency pseudorange combination Three-frequency carrier phase combination Robust estimate Machine learning |
| title | Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning |
| title_full | Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning |
| title_fullStr | Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning |
| title_full_unstemmed | Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning |
| title_short | Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning |
| title_sort | study on soil moisture estimation using a three frequency combination of observations integrated with robust estimation and machine learning |
| topic | GNSS-IR Soil moisture Three-frequency pseudorange combination Three-frequency carrier phase combination Robust estimate Machine learning |
| url | https://doi.org/10.1038/s41598-025-09029-4 |
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