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Yong Wan, Xiaona Zhang, Shuyan Lang, Ennan Ma, Yongshou Dai. An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-024-2320-0
Citation: Yong Wan, Xiaona Zhang, Shuyan Lang, Ennan Ma, Yongshou Dai. An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-024-2320-0

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

doi: 10.1007/s13131-024-2320-0
Funds:  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) and the Fundamental Research Funds for the Central Universities under contract No. 23CX04042A.
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  • Corresponding author: E-mail: wanyong@upc.edu.cn
  • Received Date: 2023-10-09
  • Accepted Date: 2023-12-25
  • Available Online: 2024-05-14
  • 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.
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