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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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  • Alpers W R, Bruening C. 1986. On the relative importance of motion-related contributions to the SAR imaging mechanism of ocean surface waves. IEEE Transactions on Geoscience and Remote Sensing, GE-24(6): 873–885, doi: 10.1109/TGRS.1986.289702
    Bruck M, Lehner S. 2013. Coastal wave field extraction using TerraSAR-X data. Journal of Applied Remote Sensing, 7(1): 073694, doi: 10.1117/1.JRS.7.073694
    Bruck M, Lehner S. 2015. TerraSAR-X/TanDEM-X sea state measurements using the XWAVE algorithm. International Journal of Remote Sensing, 36(15): 3890–3912, doi: 10.1080/01431161.2015.1051630
    Chen Tianqi, Guestrin C. 2016. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California,USA: ACM,785–794
    Han Qianqian, Hu Jijun, Song Shiyan, et al. 2013. Simulation on retrieval method of significant wave height from ocean wave spectrometer. Journal of Telemetry, Tracking and Command (in Chinese), 34(6): 7–13
    Hasselmann K, Hasselmann S. 1991. On the nonlinear mapping of an ocean wave spectrum into a synthetic aperture radar image spectrum and its inversion. Journal of Geophysical Research: Oceans, 96(C6): 10713–10729, doi: 10.1029/91JC00302
    Hauser D, Caudal G, Rijckenberg G J, et al. 1992. RESSAC: A new airborne FM/CW radar ocean wave spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 30(5): 981–995, doi: 10.1109/36.175333
    Hauser D, Soussi E, Thouvenot E, et al. 2001. SWIMSAT: a real-aperture radar to measure directional spectra of ocean waves from space—Main characteristics and performance simulation. Journal of Atmospheric and Oceanic Technology, 18(3): 421–437, doi: 10.1175/1520-0426(2001)018<0421:SARART>2.0.CO;2
    He Yijun. 1999. A parametric method of retrieving ocean wave spectra from synthetic aperture radar images. Chinese Science Bulletin, 44(13): 1218–1224, doi: 10.1007/BF02885970
    Hersbach H, Stoffelen A, de Haan S. 2007. An improved C-band scatterometer ocean geophysical model function: CMOD5. Journal of Geophysical Research: Oceans, 112(C3): C03006
    Hersbach H. 2010. Comparison of C-band scatterometer CMOD5. N equivalent neutral winds with ECMWF. Journal of Atmospheric and Oceanic Technology, 27(4): 721–736, doi: 10.1175/2009JTECHO698.1
    Huang Weimin, Liu Xinlong, Gill E W. 2017. An empirical mode decomposition method for sea surface wind measurements from X-band nautical radar data. IEEE Transactions on Geoscience and Remote Sensing, 55(11): 6218–6227, doi: 10.1109/TGRS.2017.2723431
    Huang Weimin, Yang Zhiding, Chen Xinwei. 2021. Wave height estimation from X-band nautical radar images using temporal convolutional network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 11395–11405, doi: 10.1109/JSTARS.2021.3124969
    Jackson F C. 1987. The physical basis for estimating wave-energy spectra with the radar ocean-wave spectrometer. Johns Hopkins APL Technical Digest, 8: 70–73
    Kerbaol V, Chapron B, El Fouhaily T, et al. 1996. Fetch and wind dependence of SAR azimuth cutoff and higher order statistics in a mistral wind case. In: Proceedings of 1996 International Geoscience and Remote Sensing Symposium. Lincoln, NE, USA: IEEE,621–624
    Li Peng. 2019. The study on spaceborne spectrometer for sea surface wind field retrieval (in Chinese)[dissertation]. Wuhan: Huazhong University of Science and Technology
    Li Xiaoming, Lehner S, Bruns T. 2011. Ocean wave integral parameter measurements using Envisat ASAR wave mode data. IEEE Transactions on Geoscience and Remote Sensing, 49(1): 155–174, doi: 10.1109/TGRS.2010.2052364
    Li Xiaofeng, Pichel W G, He Mingxia. 2002. Observation of hurricane-generated ocean swell refraction at the Gulf Stream north wall with the radarsat-1 synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 40(10): 2131–2142, doi: 10.1109/TGRS.2002.802474
    Lin Wenming, Dong Xiaolong. 2011. Design and optimization of a Ku-band rotating, range-gated fanbeam scatterometer. International Journal of Remote Sensing, 32(8): 2151–2171, doi: 10.1080/01431161003674626
    Liu Jiang, Xu Kangzhi, CAI Baigen, et al. 2021. XGBoost-based fault prediction method for on-board train control equipment. Journal of Beijing Jiaotong University (in Chinese), 45(4): 95–106
    Mastenbroek C, De Valk C F. 2000. A semiparametric algorithm to retrieve ocean wave spectra from synthetic aperture radar. Journal of Geophysical Research: Oceans, 105(C2): 3497–3516, doi: 10.1029/1999JC900282
    Pleskachevsky A, Jacobsen S, Tings B, et al. 2019. Estimation of sea state from Sentinel-1 Synthetic aperture radar imagery for maritime situation awareness. International Journal of Remote Sensing, 40(11): 4104–4142, doi: 10.1080/01431161.2018.1558377
    Pleskachevsky A L, Rosenthal W, Lehner S. 2016. Meteo-marine parameters for highly variable environment in coastal regions from satellite radar images. ISPRS Journal of Photogrammetry and Remote Sensing, 119: 464–484, doi: 10.1016/j.isprsjprs.2016.02.001
    Pleskachevsky A, Tings B, Wiehle S, et al. 2022. Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness. Remote Sensing of Environment, 280: 113200, doi: 10.1016/j.rse.2022.113200
    Quilfen Y, Chapron B, Elfouhaily T, et al. 1998. Observation of tropical cyclones by high‐resolution scatterometry. Journal of Geophysical Research: Oceans, 103(C4): 7767–7786, doi: 10.1029/97JC01911
    Ren Lin, Yang J, Xu Y, et al. 2021. Ocean surface wind speed dependence and retrieval from off‐nadir CFOSAT SWIM data. Earth and Space Science, 8(6): e2020EA001505, doi: 10.1029/2020EA001505
    Ren Lin, Yang Jingsong, Zheng Gang, et al. 2016. A joint method to retrieve directional ocean wave spectra from SAR and wave spectrometer data. Chinese Journal of Oceanology and Limnology, 34(4): 847–858, doi: 10.1007/s00343-015-5043-4
    Rikka S, Pleskachevsky A, Jacobsen S, et al. 2018. Meteo-marine parameters from Sentinel-1 SAR imagery: towards near real-time services for the Baltic sea. Remote Sensing, 10(5): 757, doi: 10.3390/rs10050757
    Schulz-Stellenfleth J, Konig T, Lehner S. 2006. An empirical approach for the retrieval of ocean wave parameters from synthetic aperture radar data. In: Proceedings of 2006 IEEE International Symposium on Geoscience and Remote Sensing. Denver, CO, USA: IEEE
    Schulz-Stellenfleth J, Lehner S, Hoja D. 2005. A parametric scheme for the retrieval of two-dimensional ocean wave spectra from synthetic aperture radar look cross spectra. Journal of Geophysical Research: Oceans, 110(C5): C05004
    Shao Weizeng, Wang Jing, Li Xiaofeng, et al. 2017. An empirical algorithm for wave retrieval from Co-polarization X-Band SAR imagery. Remote Sensing, 9(7): 711, doi: 10.3390/rs9070711
    Spencer M W, Wu C, Long D G. 1997. Tradeoffs in the design of a spaceborne scanning pencil beam scatterometer: application to SeaWinds. IEEE Transactions on Geoscience and Remote Sensing, 35(1): 115–126, doi: 10.1109/36.551940
    Stoffelen A, Anderson D. 1997. Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. Journal of Geophysical Research: Oceans, 102(C3): 5767–5780, doi: 10.1029/96JC02860
    Stopa J E, Mouche A. 2017. Significant wave heights from Sentinel-1 SAR: validation and applications. Journal of Geophysical Research: Oceans, 122(3): 1827–1848, doi: 10.1002/2016JC012364
    Wan Yong, Zhang Xiaona, Fan Chenqing, et al. 2022. A joint method for wave and wind field parameter inversion combining SAR with wave spectrometer data. Remote Sensing, 14(15): 3601, doi: 10.3390/rs14153601
    Wang Xiaochen. 2016. Research on airborne spectrometer wave spectrum inversion method (in Chinese)[dissertation]. Qingdao: China University of Petroleum (East China
    Wang He, Yang Jingsong, Lin Mingsen, et al. 2022. Quad-polarimetric SAR sea state retrieval algorithm from Chinese Gaofen-3 wave mode imagettes via deep learning. Remote Sensing of Environment, 273: 112969, doi: 10.1016/j.rse.2022.112969
    Zou Bin, Lin Mingsen, Shi Lijian, et al. 2018. Application of remote sensing technology in ocean disaster. City and Disaster Reduction (in Chinese), (6): 61–65
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