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An empirical method for joint inversion of wave and wind para-meters based on SAR and wave spectrometer data

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Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave conditions.However,they face inherent limitations in observing sea surface phenomena.SAR systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field observation.Wave spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field data.This 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)algorithm.The 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,respectively.These results demonstrate the model's effective retrieval of wave and wind parameters.Finally,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind speeds.This comparison highlights the model's superior inversion accuracy over other methods.

synthetic aperture radar(SAR)wave spectrometerextreme gradient boosting(XGBoost)joint inversion methodwave and wind parameters

Yong Wan、Xiaona Zhang、Shuyan Lang、Ennan Ma、Yongshou Dai

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College of Oceanography and Space Informatics,China University of Petroleum,Qingdao 266580,China

National Satellite Marine Application Center,Beijing 100081,China

Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources,Beijing 100081,China

College of Control Science and Engineering,China University of Petroleum,Qingdao 266580,China

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Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural ResourcesNational Natural Science Foundation of ChinaInnovation Fund Project for Graduate Student of China University of Petroleum(East China)Fundamental Research Funds for the Central Universities

2023CFO0166193102523CX04042A

2024

10.1007/s13131-024-2320-0

海洋学报(英文版)
中国海洋学会

海洋学报(英文版)

CSTPCD
影响因子：0.323
ISSN：0253-505X
年,卷(期)：2024.43(5)
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