Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON

TRITON is a newly launched GNSS-Reflectometry (GNSS-R) satellite mission by Taiwan, designed to enhance global sea surface wind monitoring. Among its scientific objectives, TRITON delivers high-resolution Delay Doppler Map (DDM) observations that enable novel investigations into the physical mechani...

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Main Authors: Ming-Yi Chen, Hwa Chien, Wen-Hao Yeh, Li-Ching Lin, Yu-Fu Liou
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Remote Sensing
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Online Access:https://www.frontiersin.org/articles/10.3389/frsen.2025.1657576/full
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author Ming-Yi Chen
Hwa Chien
Hwa Chien
Wen-Hao Yeh
Li-Ching Lin
Yu-Fu Liou
author_facet Ming-Yi Chen
Hwa Chien
Hwa Chien
Wen-Hao Yeh
Li-Ching Lin
Yu-Fu Liou
author_sort Ming-Yi Chen
collection DOAJ
description TRITON is a newly launched GNSS-Reflectometry (GNSS-R) satellite mission by Taiwan, designed to enhance global sea surface wind monitoring. Among its scientific objectives, TRITON delivers high-resolution Delay Doppler Map (DDM) observations that enable novel investigations into the physical mechanisms shaping GNSS-R signal structures. In this study, we highlight the critical yet often overlooked role of transmitter–receiver relative velocity (Vrel) in influencing DDM morphology within the bistatic measurement geometry. Traditional geophysical model function (GMF) retrieval methods, which rely primarily on surface scattering assumptions, often neglect this orbital dynamic factor. Leveraging a deep learning-based framework, we empirically demonstrate that unaccounted-for Vrel can introduce systematic misinterpretations of surface roughness, likely due to DDM distortion. By explicitly incorporating Vrel as an input feature, our retrieval model achieves improved wind speed estimation accuracy from TRITON data, reducing root-mean-square error (RMSE) by over 11%. These results underscore the importance of orbital dynamics in GNSS-R applications and position TRITON as a valuable platform for advancing ocean remote sensing capabilities.
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issn 2673-6187
language English
publishDate 2025-07-01
publisher Frontiers Media S.A.
record_format Article
series Frontiers in Remote Sensing
spelling doaj-art-ec050cd6476c4d5fa425fd8d147908812025-08-20T02:46:04ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-07-01610.3389/frsen.2025.16575761657576Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITONMing-Yi Chen0Hwa Chien1Hwa Chien2Wen-Hao Yeh3Li-Ching Lin4Yu-Fu Liou5Institute of Hydrological and Oceanic Sciences, National Central University, Taoyuan, TaiwanInstitute of Hydrological and Oceanic Sciences, National Central University, Taoyuan, TaiwanCenter for Space and Remote Sensing Research, National Central University, Taoyuan, TaiwanTaiwan Space Agency, Hsinchu, TaiwanAdvanced Research Center for Earth Sciences, National Central University, Taoyuan, TaiwanAdvanced Research Center for Earth Sciences, National Central University, Taoyuan, TaiwanTRITON is a newly launched GNSS-Reflectometry (GNSS-R) satellite mission by Taiwan, designed to enhance global sea surface wind monitoring. Among its scientific objectives, TRITON delivers high-resolution Delay Doppler Map (DDM) observations that enable novel investigations into the physical mechanisms shaping GNSS-R signal structures. In this study, we highlight the critical yet often overlooked role of transmitter–receiver relative velocity (Vrel) in influencing DDM morphology within the bistatic measurement geometry. Traditional geophysical model function (GMF) retrieval methods, which rely primarily on surface scattering assumptions, often neglect this orbital dynamic factor. Leveraging a deep learning-based framework, we empirically demonstrate that unaccounted-for Vrel can introduce systematic misinterpretations of surface roughness, likely due to DDM distortion. By explicitly incorporating Vrel as an input feature, our retrieval model achieves improved wind speed estimation accuracy from TRITON data, reducing root-mean-square error (RMSE) by over 11%. These results underscore the importance of orbital dynamics in GNSS-R applications and position TRITON as a valuable platform for advancing ocean remote sensing capabilities.https://www.frontiersin.org/articles/10.3389/frsen.2025.1657576/fullGNSS reflectometrydelay Doppler mapsatellite relative velocity in bistatic modewind speed retrievaldeep learningTRITON satellite
spellingShingle Ming-Yi Chen
Hwa Chien
Hwa Chien
Wen-Hao Yeh
Li-Ching Lin
Yu-Fu Liou
Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON
Frontiers in Remote Sensing
GNSS reflectometry
delay Doppler map
satellite relative velocity in bistatic mode
wind speed retrieval
deep learning
TRITON satellite
title Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON
title_full Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON
title_fullStr Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON
title_full_unstemmed Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON
title_short Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON
title_sort evaluation of the effect of satellite motion on gnss r wind speed retrieval insights from triton
topic GNSS reflectometry
delay Doppler map
satellite relative velocity in bistatic mode
wind speed retrieval
deep learning
TRITON satellite
url https://www.frontiersin.org/articles/10.3389/frsen.2025.1657576/full
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