GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition
Using global navigation satellite system (GNSS) to monitor snow depth helps scientists study the impacts of climate change and predict future climate patterns. In the process of extracting reflection signals from signal-to-noise ratio (SNR) data, traditional methods usually use low order polynomials...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-06-01
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| Series: | Intelligent and Converged Networks |
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| Online Access: | https://www.sciopen.com/article/10.23919/ICN.2025.0007 |
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| _version_ | 1849426445300924416 |
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| author | Dengao Li Xinyu Luo Jumin Zhao Fanming Wu Hairong Jiang Danyang Shi |
| author_facet | Dengao Li Xinyu Luo Jumin Zhao Fanming Wu Hairong Jiang Danyang Shi |
| author_sort | Dengao Li |
| collection | DOAJ |
| description | Using global navigation satellite system (GNSS) to monitor snow depth helps scientists study the impacts of climate change and predict future climate patterns. In the process of extracting reflection signals from signal-to-noise ratio (SNR) data, traditional methods usually use low order polynomials for detrending terms. However, this traditional algorithm cannot completely eliminate the environmental noise in the SNR during signal decomposition, resulting in other noise sources still existing in the detrended SNR, which affects the inversion results. In order to make the inversion results more accurate, this paper proposes a robust empirical mode decomposition (REMD) based method. In signal decomposition, REMD is applied to improve the algorithm, and the correlation coefficient method is used to denoise the decomposed signal. The proposed algorithm is validated using the data from the U.S. Plate Boundary Observatory network SG27 site from winter 2016 to spring 2017 as the study data. The obtained experimental results are compared with the actual snow depth provided by Snowpack Telemetry. When the improved algorithm is used, the root mean square error and mean absolute error of the snow depth inversion at the SG27 site are improved, respectively. |
| format | Article |
| id | doaj-art-a430b7a4c4d44477a56831e44bc35bdd |
| institution | Kabale University |
| issn | 2708-6240 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Intelligent and Converged Networks |
| spelling | doaj-art-a430b7a4c4d44477a56831e44bc35bdd2025-08-20T03:29:23ZengTsinghua University PressIntelligent and Converged Networks2708-62402025-06-016211512810.23919/ICN.2025.0007GNSS signal-to-noise snow depth inversion based on robust empirical mode decompositionDengao Li0Xinyu Luo1Jumin Zhao2Fanming Wu3Hairong Jiang4Danyang Shi5College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, ChinaUsing global navigation satellite system (GNSS) to monitor snow depth helps scientists study the impacts of climate change and predict future climate patterns. In the process of extracting reflection signals from signal-to-noise ratio (SNR) data, traditional methods usually use low order polynomials for detrending terms. However, this traditional algorithm cannot completely eliminate the environmental noise in the SNR during signal decomposition, resulting in other noise sources still existing in the detrended SNR, which affects the inversion results. In order to make the inversion results more accurate, this paper proposes a robust empirical mode decomposition (REMD) based method. In signal decomposition, REMD is applied to improve the algorithm, and the correlation coefficient method is used to denoise the decomposed signal. The proposed algorithm is validated using the data from the U.S. Plate Boundary Observatory network SG27 site from winter 2016 to spring 2017 as the study data. The obtained experimental results are compared with the actual snow depth provided by Snowpack Telemetry. When the improved algorithm is used, the root mean square error and mean absolute error of the snow depth inversion at the SG27 site are improved, respectively.https://www.sciopen.com/article/10.23919/ICN.2025.0007global navigation satellite system interferometric reflectometry (gnss-ir)snow depthsignal-to-noise ratio (snr)robust empirical mode decomposition (remd) |
| spellingShingle | Dengao Li Xinyu Luo Jumin Zhao Fanming Wu Hairong Jiang Danyang Shi GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition Intelligent and Converged Networks global navigation satellite system interferometric reflectometry (gnss-ir) snow depth signal-to-noise ratio (snr) robust empirical mode decomposition (remd) |
| title | GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition |
| title_full | GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition |
| title_fullStr | GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition |
| title_full_unstemmed | GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition |
| title_short | GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition |
| title_sort | gnss signal to noise snow depth inversion based on robust empirical mode decomposition |
| topic | global navigation satellite system interferometric reflectometry (gnss-ir) snow depth signal-to-noise ratio (snr) robust empirical mode decomposition (remd) |
| url | https://www.sciopen.com/article/10.23919/ICN.2025.0007 |
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