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

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

doi:10.1007/s13131-024-2320-0

Volume 43 Issue 5

May 2024

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Article Navigation > Acta Oceanologica Sinica > 2024 > 43(5): 133-144

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, 2024, 43(5): 133-144. 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, 2024, 43(5): 133-144. doi: 10.1007/s13131-024-2320-0

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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

Yong Wan^{1
,
,},
Xiaona Zhang¹,
Shuyan Lang^{2, 3},
Ennan Ma⁴,
Yongshou Dai¹

1.
College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
2.
National Satellite Marine Application Center, Beijing 100081, China
3.
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
4.
College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China

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); the Fundamental Research Funds for the Central Universities under contract No. 23CX04042A.

More Information

Corresponding author: E-mail: wanyong@upc.edu.cn
Received Date: 2023-10-09
Accepted Date: 2023-12-25

Available Online: 2024-05-14

Publish Date: 2024-05-30

Abstract

Abstract

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 spectrometer,
- extreme gradient boosting (XGBoost),
- joint inversion method,
- wave and wind parameters

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References(38)

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