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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Detalles Bibliográficos
Autores: Han, Xinhai, Li, Xiaohui, Yang, Jingsong, Tao, Wei, Han, Guoqi, Wang, Jiuke, Wang, Yiqi, Bao, Qinghua, Chen, Lin, Li, Weiqiang
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/421831
Acceso en línea:http://hdl.handle.net/10261/421831
https://api.elsevier.com/content/abstract/scopus_id/86000201315
Access Level:acceso abierto
Palabra clave:Deep learning
FengYun-3E (FY-3E)
Global navigation satellite system reflectometry (GNSS-R)
Nearshore
Sea surface wind speed
Tianmu-1
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spelling Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missionsHan, XinhaiLi, XiaohuiYang, JingsongTao, WeiHan, GuoqiWang, JiukeWang, YiqiBao, QinghuaChen, LinLi, WeiqiangDeep learningFengYun-3E (FY-3E)Global navigation satellite system reflectometry (GNSS-R)NearshoreSea surface wind speedTianmu-1This 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%).This work was supported by the National Natural Science Foundation of China [grant number 42306200], the National Key Research and Development Program of China [grant number 2023YFC3008100], and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) [grant number 311021004], and the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University [grant number SL2021ZD203].Peer reviewedTaylor & FrancisNational Natural Science Foundation of ChinaNational Key Research and Development Program (China)Southern Marine Science and Engineering Guangdong LaboratoryShanghai Jiao Tong UniversityHan, Xinhai [0000-0002-8943-3149]Li, Xiaohui [0000-0003-0426-3628]Yang, Jingsong [0000-0002-7514-3212]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/421831https://api.elsevier.com/content/abstract/scopus_id/86000201315reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1080/10095020.2024.2441473https://doi.org/10.1080/10095020.2024.2441473Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4218312026-05-22T06:33:51Z
dc.title.none.fl_str_mv Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions
title Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions
spellingShingle Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions
Han, Xinhai
Deep learning
FengYun-3E (FY-3E)
Global navigation satellite system reflectometry (GNSS-R)
Nearshore
Sea surface wind speed
Tianmu-1
title_short 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_sort Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions
dc.creator.none.fl_str_mv Han, Xinhai
Li, Xiaohui
Yang, Jingsong
Tao, Wei
Han, Guoqi
Wang, Jiuke
Wang, Yiqi
Bao, Qinghua
Chen, Lin
Li, Weiqiang
author Han, Xinhai
author_facet Han, Xinhai
Li, Xiaohui
Yang, Jingsong
Tao, Wei
Han, Guoqi
Wang, Jiuke
Wang, Yiqi
Bao, Qinghua
Chen, Lin
Li, Weiqiang
author_role author
author2 Li, Xiaohui
Yang, Jingsong
Tao, Wei
Han, Guoqi
Wang, Jiuke
Wang, Yiqi
Bao, Qinghua
Chen, Lin
Li, Weiqiang
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv National Natural Science Foundation of China
National Key Research and Development Program (China)
Southern Marine Science and Engineering Guangdong Laboratory
Shanghai Jiao Tong University
Han, Xinhai [0000-0002-8943-3149]
Li, Xiaohui [0000-0003-0426-3628]
Yang, Jingsong [0000-0002-7514-3212]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Deep learning
FengYun-3E (FY-3E)
Global navigation satellite system reflectometry (GNSS-R)
Nearshore
Sea surface wind speed
Tianmu-1
topic Deep learning
FengYun-3E (FY-3E)
Global navigation satellite system reflectometry (GNSS-R)
Nearshore
Sea surface wind speed
Tianmu-1
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%).
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/421831
https://api.elsevier.com/content/abstract/scopus_id/86000201315
url http://hdl.handle.net/10261/421831
https://api.elsevier.com/content/abstract/scopus_id/86000201315
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1080/10095020.2024.2441473
https://doi.org/10.1080/10095020.2024.2441473
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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