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 mechanisms shaping GNSS-R signal structures. In this study, we highlight the critical yet often overlooked role of transmitter–receiver relative velocity (V rel ) 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 V rel can introduce systematic misinterpretations of surface roughness, likely due to DDM distortion. By explicitly incorporating V rel 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.