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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Bibliographic Details
Authors: Han, Xinhai, Li, Xiaohui, Yang, Jingsong, Tao, Wei, Han, Guoqi, Wang, Jiuke, Wang, Yiqi, Bao, Qinghua, Chen, Lin, Li, Weiqiang
Format: article
Status:Published version
Publication Date:2025
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/421831
Online Access:http://hdl.handle.net/10261/421831
https://api.elsevier.com/content/abstract/scopus_id/86000201315
Access Level:Open access
Keyword:Deep learning
FengYun-3E (FY-3E)
Global navigation satellite system reflectometry (GNSS-R)
Nearshore
Sea surface wind speed
Tianmu-1
Description
Summary: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%).

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