Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions
This study evaluates and calibrates wind products derived from Global Navigation Satellite System Reflectometry (GNSS-R) using data from the FengYun-3E (FY-3E) global navigation satellite system occultation sounder II (GNOS-II) and Tianmu-1 missions. The research highlights the significance of remot...
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Taylor & Francis Group
2025-04-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2441473 |
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| author | Xinhai Han Xiaohui Li Jingsong Yang Wei Tao Guoqi Han Jiuke Wang Yiqi Wang Qinghua Bao Lin Chen Weiqiang Li |
| author_facet | Xinhai Han Xiaohui Li Jingsong Yang Wei Tao Guoqi Han Jiuke Wang Yiqi Wang Qinghua Bao Lin Chen Weiqiang Li |
| author_sort | Xinhai Han |
| collection | DOAJ |
| description | This study evaluates and calibrates wind products derived from Global Navigation Satellite System Reflectometry (GNSS-R) using data from the FengYun-3E (FY-3E) global navigation satellite system occultation sounder II (GNOS-II) and Tianmu-1 missions. The research highlights the significance of remote sensing for the accurate measurement of sea surface wind speeds in nearshore areas, which are crucial for environmental monitoring and climate studies. Initial comparisons with National Data Buoy Center (NDBC) measurements revealed root – mean – square errors (RMSE) of 2.49 m/s for FY-3E GNOS-II Beidou navigation satellite system (BDS) signals and 2.13 m/s for global positioning system (GPS) signals. For the Tianmu-1 mission, the RMSE values were 3.21 m/s for BDS, 3.13 m/s for GPS, 2.91 m/s for GLONASS (GLO), and 2.91 m/s for Galileo (GAL) signals. To improve accuracy, especially in the complex nearshore environments, a deep learning calibration model incorporating residual blocks was employed. This model significantly enhanced the performance compared to a basic neural network. An ablation study confirmed that including residual blocks reduced RMSE by over 20% across all signal types. The calibrated model achieved substantial accuracy improvements in the test set, reducing RMSE to 1.03 m/s for FY BDS (improvement of 60%), 0.99 m/s for FY GPS (improvement of 54%), 1.57 m/s (improvement of 51%), 1.36 m/s for Tianmu-1 GPS (57% improvement), 1.26 m/s for Tianmu-1 GLO (improvement of 56%), and 1.50 m/s for Tianmu-1 GAL (improvement 47%). |
| format | Article |
| id | doaj-art-e177d58eaa1e4ccf892756dd490e6d8a |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-e177d58eaa1e4ccf892756dd490e6d8a2025-08-20T03:48:13ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-04-0111210.1080/10095020.2024.2441473Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missionsXinhai Han0Xiaohui Li1Jingsong Yang2Wei Tao3Guoqi Han4Jiuke Wang5Yiqi Wang6Qinghua Bao7Lin Chen8Weiqiang Li9School of Oceanography, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaFisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, CanadaSchool of Artificial Intelligence, Sun Yat-sen University, Zhuhai, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, ChinaAerospace New Generation Communications Co., Ltd., Chongqing, ChinaAerospace Tianmu (Chongqing) Satellite Science and Technology Co., Ltd., Chongqing, ChinaInstitute of Space Sciences (ICE, CSIC, Barcelona, SpainThis study evaluates and calibrates wind products derived from Global Navigation Satellite System Reflectometry (GNSS-R) using data from the FengYun-3E (FY-3E) global navigation satellite system occultation sounder II (GNOS-II) and Tianmu-1 missions. The research highlights the significance of remote sensing for the accurate measurement of sea surface wind speeds in nearshore areas, which are crucial for environmental monitoring and climate studies. Initial comparisons with National Data Buoy Center (NDBC) measurements revealed root – mean – square errors (RMSE) of 2.49 m/s for FY-3E GNOS-II Beidou navigation satellite system (BDS) signals and 2.13 m/s for global positioning system (GPS) signals. For the Tianmu-1 mission, the RMSE values were 3.21 m/s for BDS, 3.13 m/s for GPS, 2.91 m/s for GLONASS (GLO), and 2.91 m/s for Galileo (GAL) signals. To improve accuracy, especially in the complex nearshore environments, a deep learning calibration model incorporating residual blocks was employed. This model significantly enhanced the performance compared to a basic neural network. An ablation study confirmed that including residual blocks reduced RMSE by over 20% across all signal types. The calibrated model achieved substantial accuracy improvements in the test set, reducing RMSE to 1.03 m/s for FY BDS (improvement of 60%), 0.99 m/s for FY GPS (improvement of 54%), 1.57 m/s (improvement of 51%), 1.36 m/s for Tianmu-1 GPS (57% improvement), 1.26 m/s for Tianmu-1 GLO (improvement of 56%), and 1.50 m/s for Tianmu-1 GAL (improvement 47%).https://www.tandfonline.com/doi/10.1080/10095020.2024.2441473FengYun-3E (FY-3E)Tianmu-1Global Navigation Satellite System Reflectometry (GNSS-R)sea surface wind speednearshoredeep learning |
| spellingShingle | Xinhai Han Xiaohui Li Jingsong Yang Wei Tao Guoqi Han Jiuke Wang Yiqi Wang Qinghua Bao Lin Chen Weiqiang Li Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions Geo-spatial Information Science FengYun-3E (FY-3E) Tianmu-1 Global Navigation Satellite System Reflectometry (GNSS-R) sea surface wind speed nearshore deep learning |
| title | Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions |
| title_full | Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions |
| title_fullStr | Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions |
| title_full_unstemmed | Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions |
| title_short | Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions |
| title_sort | evaluation and deep learning based calibration of nearshore sea surface wind speeds from fy 3e gnos ii and tianmu 1 missions |
| topic | FengYun-3E (FY-3E) Tianmu-1 Global Navigation Satellite System Reflectometry (GNSS-R) sea surface wind speed nearshore deep learning |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2441473 |
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