An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

作     者：Yong Wan Xiaona Zhang Shuyan Lang Ennan Ma Yongshou Dai Yong Wan;Xiaona Zhang;Shuyan Lang;Ennan Ma;Yongshou Dai

作者机构：College of Oceanography and Space InformaticsChina University of PetroleumQingdao 266580China National Satellite Marine Application CenterBeijing 100081China Key Laboratory of Space Ocean Remote Sensing and ApplicationMinistry of Natural ResourcesBeijing 100081China College of Control Science and EngineeringChina University of PetroleumQingdao 266580China 

出 版 物：《Acta Oceanologica Sinica》 (海洋学报（英文版）)

年 卷 期：2024年第43卷第5期

页      面：133-144页

核心收录：

学科分类：08[工学] 0816[工学-测绘科学与技术] 

基　　金：The project supported by Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources under contract No.2023CFO016 the National Natural Science Foundation of China under contract No.61931025 the Innovation Fund Project for Graduate Student of China University of Petroleum(East China) the Fundamental Research Funds for the Central Universities under contract No.23CX04042A 

主　　题：synthetic aperture radar(SAR) wave spectrometer extreme gradient boosting(XGBoost) joint inversion method wave and wind parameters 

摘      要：Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave ***,they face inherent limitations in observing sea surface *** systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field *** spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field *** study utilizes SAR and surface wave investigation and monitoring(SWIM)data to initially extract key feature parameters,which are then prioritized using the extreme gradient boosting(XGBoost)*** research further addresses feature collinearity through a combined analysis of feature importance and correlation,leading to the development of an inversion model for wave and wind parameters based on XGBoost.A comparative analysis of this model with ERA5 reanalysis and buoy data for of significant wave height,mean wave period,wind direction,and wind speed reveals root mean square errors of 0.212 m,0.525 s,27.446°,and 1.092 m/s,compared to 0.314 m,0.888 s,27.698°,and 1.315 m/s from buoy data,*** results demonstrate the model’s effective retrieval of wave and wind ***,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind *** comparison highlights the model’s superior inversion accuracy over other methods.