Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/4013 |
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| author | Lili Jing Lei Yang Wentao Yang Tianhe Xu Fan Gao Yilin Lu Bo Sun Dongkai Yang Xuebao Hong Nazi Wang Hongliang Ruan José Darrozes |
| author_facet | Lili Jing Lei Yang Wentao Yang Tianhe Xu Fan Gao Yilin Lu Bo Sun Dongkai Yang Xuebao Hong Nazi Wang Hongliang Ruan José Darrozes |
| author_sort | Lili Jing |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:52:02Z |
| format | Article |
| id | doaj.art-55c5dc954dfa4d6d851526ac4b3ed5ff |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:52:02Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-55c5dc954dfa4d6d851526ac4b3ed5ff2023-11-22T16:44:12ZengMDPI AGRemote Sensing2072-42922021-10-011319401310.3390/rs13194013Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion ApproachLili Jing0Lei Yang1Wentao Yang2Tianhe Xu3Fan Gao4Yilin Lu5Bo Sun6Dongkai Yang7Xuebao Hong8Nazi Wang9Hongliang Ruan10José Darrozes11Institute of Space Science, Shandong University, Weihai 264209, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Geological Engineering and Surveying and Mapping, Chang’an University, Xi’an 710054, ChinaInstitute of Space Science, Shandong University, Weihai 264209, ChinaInstitute of Space Science, Shandong University, Weihai 264209, ChinaChina Association of Remote Sensing Application, Beijing 100094, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaInstitute of Space Science, Shandong University, Weihai 264209, ChinaBusiness School, Jinhua Polytechnic, Jinhua 321000, ChinaLaboratoire Géosciences Environnement Toulouse, Université Paul Sabatier, 31400 Toulouse, FranceThis 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.https://www.mdpi.com/2072-4292/13/19/4013GNSSSignal-to-Noise Ratiosoil moistureRobust Kalman Filterdata fusion |
| spellingShingle | Lili Jing Lei Yang Wentao Yang Tianhe Xu Fan Gao Yilin Lu Bo Sun Dongkai Yang Xuebao Hong Nazi Wang Hongliang Ruan José Darrozes Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach Remote Sensing GNSS Signal-to-Noise Ratio soil moisture Robust Kalman Filter data fusion |
| title | Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach |
| title_full | Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach |
| title_fullStr | Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach |
| title_full_unstemmed | Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach |
| title_short | Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach |
| title_sort | robust kalman filter soil moisture inversion model using gps snr data a dual band data fusion approach |
| topic | GNSS Signal-to-Noise Ratio soil moisture Robust Kalman Filter data fusion |
| url | https://www.mdpi.com/2072-4292/13/19/4013 |
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