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Accuracy evaluation of wave spectrum inversion based on Sentinel-1 and GF-3 SAR data

  • role： First author第一作者
  • Affiliation：

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

  • Email：wanyong@upc.edu.cn
  • Introduction：万勇，研究方向为海洋微波遥感。E-mail：wanyong@upc.edu.cn
WAN Yong1,  
  • role： Corresponding author通信作者
  • Affiliation：

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

  • Email：1284914612@qq.com
  • Introduction：马恩男，研究方向为SAR海浪探测。E-mail：1284914612@qq.com
MA Ennan2*,  
  • Affiliation：

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

QU Ruozhao2,  
  • Affiliation：

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

DAI Yongshou1

résumé

Ocean wave is one of the important marine dynamic phenomenon that affect human activities. At present, the main observation means include buoy observation, marine numerical prediction model, and microwave remote sensing observation. However, we cannot conduct large-scale observation by buoy, and the marine numerical prediction model’s result is not measured data. Spectrometers and altimeters in microwave remote sensing instruments can also measure spectral parameters. However, SAR, which has a higher resolution, can provide 2D sea surface information. The Sentinel-1 satellite of ESA and GF-3 satellite independently developed by China are now in orbit, and numerous teams are working to retrieve wave parameters from SAR data of these two satellites. In this work, we compared the wave parameter inversion accuracy of Sentinel-1 SAR Interferometric Wide Swath model and GF-3 SAR strip model based on wave spectrum, which provides a reference for the wide application of GF-3 SAR data.The sea states according to the ERA-5 data of ECMWF are divided into three categories: low, moderate, and high sea states. The sea areas of Hormuz and Malacca Straits of the maritime Silk Road in the Indian Ocean and the coastal waters of the Pacific and Atlantic Ocean are selected as the study areas. Meanwhile, the SAR data of Sentinel-1 and GF-3 satellites under different sea states are selected as the data source. The MPI method is used to retrieve the wave spectrum and wave parameters using the E spectrum as the initial guess. Subsequently, the SAR data inversion results of the two satellites under different sea states are compared with the ERA-5 and buoy wave data. The inversion accuracy of the wave parameters can be verified by calculating the values of the Root Mean Square Error (RMSE) and Scatter Index (SI), and the inversion accuracy of the wave parameters of the two satellites under different sea conditions can be compared.The RMSEs of significant wave height (Hs) retrieved by GF-3 SAR under low, moderate, and high sea conditions are 0.30, 0.34, and 0.48 m, and those of mean wave period (Tm) are 1.02, 0.99, and 0.95 s, respectively, compared with the ERA-5 data. In addition, the RMSE of Hs retrieved by Sentinel-1 SAR under low, moderate, and high sea conditions are 0.30, 0.29, and 0.33 m, respectively, and the RMSEs of Tm are 0.94, 0.51, and 0.64 s, respectively. The RMSEs of Hs and Tm under different sea conditions retrieved by GF-3 SAR are 0.38 m and 0.99 s, and those of Hs and Tm retrieved by Sentinel-1 SAR are 0.31 m and 0.70 s, respectively, compared with the ERA-5 data. The RMSEs of the retrieved Hs and Tm of GF-3 satellite are 0.42 m and 0.94 s, and those of the retrieved Hs and Tm of Sentinel-1 are 0.40 m and 0.91 s, respectively, compared with the buoy data.The SAR wave parameter inversion of Sentinel-1 and GF-3 SAR based on the wave spectrum shows that the inversion results of the two satellites meet the index requirements in this field, and the accuracy of the inversion results of wave spectrum is the same. The strip mode SAR data of GF-3 satellite, China’s first self-developed SAR satellite, has broad prospects in marine research fields.

mots-clés

SAR;GF-3;Sentinel-1;Wave spectrum inversion;Accuracy comparison

References

  1. 1.
    Abdalla S, De Chiara G, Bidlot J and Isaksen L. 2019. Use of satellite data to support ocean wind and wave forecasting: a review.
  2. 2.
    Dai L and Gao M G. 2006. A high-efficient realization algorithm of multi-look processing. Modern Radar, 28(5): 30-33
  3. 3.
    Elfouhaily T, Chapron B, Katsaros K and Vandemark D. 1997. A unified directional spectrum for long and short wind-driven waves. Journal of Geophysical Research: Oceans, 102(C7): 15781-15796
  4. 4.
    Engen G and Johnsen H. 1995. SAR-ocean wave inversion using image cross spectra. IEEE Transactions on Geoscience and Remote Sensing, 33(4): 1047-1056
  5. 5.
    Hasselmann K and 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
  6. 6.
    Hasselmann S, Brüning C, Hasselmann K and Heimbach P. 1996. An improved algorithm for the retrieval of ocean wave spectra from synthetic aperture radar image spectra. Journal of Geophysical Research: Oceans, 101(C7): 16615-16629
  7. 7.
    Li X F, Pichel W G, He M X, et al. 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
  8. 8.
    Li X M. 2010. Study of Algorithms for Ocean Wave Retrieval Using ENVISAT Advanced Synthetic Aperture Radar Wave Mode Data. Qingdao: Ocean University of China
  9. 9.
    Li X M, Lehner S and 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
  10. 10.
    Mastenbroek C and 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
  11. 11.
    Monaldo F, Jackson C, Li X F and Pichel W G. 2016. Preliminary evaluation of sentinel-1A wind speed retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6): 2638-2642
  12. 12.
    Schulz-Stellenfleth J, König T and Lehner S. 2007. An empirical approach for the retrieval of integral ocean wave parameters from synthetic aperture radar data. Journal of Geophysical Research: Oceans, 112(C3): C03019
  13. 13.
    Schulz-Stellenfleth J, Lehner S and 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
  14. 14.
    Sheng Y X, Shao W Z, Zhu S, Sun J, Yuan X Z, Li S Q, Shi J and Zuo J C. 2018. Validation of significant wave height retrieval from co-polarization Chinese Gaofen-3 SAR imagery by using an improved algorithm. Acta Oceanologica Sinica, 37(6): 1-10.
  15. 15.
    Sreelakshmi S and Bhaskaran P K. 2020. Wind-generated wave climate variability in the Indian Ocean using ERA-5 dataset. Ocean Engineering, 209: 107486
  16. 16.
    Stopa J E and Mouche A. 2017. Significant wave heights from Sentinel‐1 SAR: validation and applications. Journal of Geophysical Research: Oceans, 122(3): 1827-1848
  17. 17.
    Sun J and Guan G L. 2006. Parameterized first-guess spectrum method for retrieving directional spectrum of swell-dominated waves and huge waves from SAR images. Chinese Journal of Oceanology and Limnology, 24(1): 12-20
  18. 18.
    Sun J and Kawamura H. 2009. Retrieval of surface wave parameters from SAR images and their validation in the coastal seas around Japan. Journal of Oceanography, 65(4): 567-577
  19. 19.
    Wan Y, Qu R Z, Dai Y S and Zhang X Y. 2020. Research on the applicability of the E spectrum and PM spectrum as the first guess spectrum of SAR wave spectrum inversion. IEEE Access, 8: 169082-169095
  20. 20.
    Wang J. 2019. The Research of Retrieving Wave Information from GF-3 SAR Wave Mode Images. Zhoushan: Zhejiang Ocean University
  21. 21.
    Wang J J, Li B X, Gao Z Y and Wang J K. 2019. Comparison of ECMWF significant wave height forecasts in the China Sea with Buoy Data. Weather and Forecasting, 34(6): 1693-1704
  22. 22.
    Wen B and Liu J. 2006. Review of history and prospect for study of sea wave numerical modeling. Marine Forecasts, 23(4): 76-81
  23. 23.
    Xu F X. 1988. Numerical forecasting experiment on sea waves of the north pacific. Marine Forecasts, 5(2): 29-34
  24. 24.
    Zhang L T. 2014. The Influence of Parameter and Operating Mode of Synthetic Aperture Radar on Sea Wave Retrival. Qingdao: Ocean University of China
  25. 25.
    Zhang Z. 2017. The Research of Retrieving Sea Surface Wind and Ocean Wave Parameters from Synthetic Aperture Radar. Zhoushan: Zhejiang Ocean University
  26. 26.
    Zhu S, Shao W Z, Armando M, Shi J, Sun J, Yuan X Z, Hu J C and Yang D K. 2018. Evaluation of Chinese quad-polarization gaofen-3 SAR wave mode data for significant wave height retrieval. Canadian Journal of Remote Sensing, 44(6): 588-600

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