Volume 40 Issue 1
Feb.  2021
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Tingting Qin, Tong Jia, Qian Feng, Xiaoming Li. Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods[J]. Acta Oceanologica Sinica, 2021, 40(1): 13-21. doi: 10.1007/s13131-020-1682-1
Citation: Tingting Qin, Tong Jia, Qian Feng, Xiaoming Li. Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods[J]. Acta Oceanologica Sinica, 2021, 40(1): 13-21. doi: 10.1007/s13131-020-1682-1

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

doi: 10.1007/s13131-020-1682-1
Funds:  The National Key Research and Development Program under contract Nos 2016YFC1402703 and 2018YFC1407100.
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  • Corresponding author: E-mail: lixm@radi.ac.cn
  • Received Date: 2020-01-05
  • Accepted Date: 2020-05-26
  • Available Online: 2021-04-21
  • Publish Date: 2021-01-25
  • Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed (SSWS) from HH-polarized Sentinel-1 (S1) SAR images. The Polarization Ratio (PR) models combined with the CMOD5.N Geophysical Model Function (GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HH-polarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation (BP) neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%, respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.
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  • [1]
    Bentamy A, Croize-Fillon D, Perigaud C. 2008. Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations. Ocean Science, 4(4): 265–274. doi: 10.5194/os-4-265-2008
    [2]
    Elfouhaily T. 1996. Physical modeling of electromagnetic backscatter from the ocean surface; application to retrieval of wind fields and wind stress by remote sensing of the marine atmospheric boundary layer [dissertation]. Paris: University Paris VII
    [3]
    ESA (European Space Agency). 2016. Sentinel-1 product specification. https://sentinel.esa.int/web/sentinel/document-library/content/-/article/sentinel-1-product-specification, [2020-2-17]
    [4]
    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
    [5]
    Hersbach H, Stoffelen A, de Haan S. 2007. An improved C‐band scatterometer ocean geophysical model function: CMOD5. Journal of Geophysical Research, 112(C3): C03006
    [6]
    Horstmann J, Schiller H, Schulz-Stellenfleth J, et al. 2003. Global wind speed retrieval from SAR. IEEE Transactions on Geoscience and Remote Sensing, 41(10): 2277–2286. doi: 10.1109/TGRS.2003.814658
    [7]
    Komarov A S, Buehner M. 2017. Automated detection of ice and open water from dual-polarization RADARSAT-2 images for data assimilation. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5755–5769. doi: 10.1109/TGRS.2017.2713987
    [8]
    Liu Guihong, Yang Xiaofeng, Li Xiaofeng, et al. 2013. A systematic comparison of the effect of polarization ratio models on sea surface wind retrieval from C-band synthetic aperture radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3): 1100–1108. doi: 10.1109/JSTARS.2013.2242848
    [9]
    Monaldo F M, Thompson D R, Pichel W G, et al. 2004. A systematic comparison of QuikSCAT and SAR ocean surface wind speeds. IEEE Transactions on Geoscience and Remote Sensing, 42(2): 283–291. doi: 10.1109/TGRS.2003.817213
    [10]
    Moreira A. 1991. Improved multi-look techniques applied to SAR and Scan SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 29(4): 529–534. doi: 10.1109/36.135814
    [11]
    Mouche A A, Hauser D, Daloze J F, et al. 2005. Dual-polarization measurements at C-band over the ocean: results from airborne radar observations and comparison with ENVISAT ASAR data. IEEE Transactions on Geoscience and Remote Sensing, 43(4): 753–769. doi: 10.1109/TGRS.2005.843951
    [12]
    Olivier P, Vidal-Madjar D. 1994. Empirical estimation of the ERS-1 SAR radiometric resolution. International Journal of Remote Sensing, 15(5): 1109–1114. doi: 10.1080/01431169408954144
    [13]
    Peixoto J P, Oort A H. 1992. Physics of Climate. New York: American Institute of Physics, 67
    [14]
    Pierson Jr W J. 1990. Examples of, reasons for, and consequences of the poor quality of wind data from ships for the marine boundary layer: Implications for remote sensing. Journal of Geophysical Research, 95(C8): 13313–13340. doi: 10.1029/JC095iC08p13313
    [15]
    Pond S, Pickard G L. 1983. Currents with friction; wind-driven circulation. In: Pond S, Pickard G, eds. Introductory Dynamical Oceanography. 2nd ed. Amsterdam: Elsevier, 100–162
    [16]
    Quilfen Y, Chapron B, Elfouhaily T, et al. 1998. Observation of tropical cyclones by high-resolution scatterometry. Journal of Geophysical Research, 103(C4): 7767–7786. doi: 10.1029/97JC01911
    [17]
    Richaume P, Badran F, Crepon M, et al. 2000. Neural network wind retrieval from ERS-1 scatterometer data. Journal of Geophysical Research, 105(C4): 8737–8751. doi: 10.1029/1999JC900225
    [18]
    Schwerdt M, Schmidt K, Tous Ramon N, et al. 2014. Independent verification of the Sentinel-1A system calibration. In: Proceedings of 2014 Geoscience and Remote Sensing Symposium. Quebec City, QC, Canada: IEEE
    [19]
    Stoffelen A, Anderson D. 1997. Scatterometer data interpretation: estimation and validation of the transfer function CMOD4. Journal of Geophysical Research, 102(C3): 5767–5780. doi: 10.1029/96JC02860
    [20]
    Thiria S, Mejia C, Badran F, et al. 1993. A neural network approach for modeling nonlinear transfer functions: application for wind retrieval from spaceborne scatterometer data. Journal of Geophysical Research, 98(C12): 22827–22841. doi: 10.1029/93JC01815
    [21]
    Wang Lihua, Lu Peng, Ma Jiapei. 2017. Deriving sea surface wind from synthetic aperture radar based on Fourier transform and neural network. In: Proceedings of the 10th International Congress on Image and Signal Processing. Shanghai: IEEE, 1–6
    [22]
    Yang Yonghong. 2009. Introduction to Synthetic Aperture Radar Ocean Remote Sensing (in Chinese). Beijing: China Ocean Press
    [23]
    Zhang Biao, Mouche A, Lu Yiru, et al. 2019. A geophysical model function for wind speed retrieval from C-band HH-polarized synthetic aperture radar. IEEE Geoscience and Remote Sensing Letters, 16(10): 1521–1525. doi: 10.1109/LGRS.2019.2905578
    [24]
    Zhang Biao, Perrie W, He Yijun. 2011. Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model. Journal of Geophysical Research, 116(C8): C08008
    [25]
    Zhang Biao, Perrie W, Hwang P A, et al. 2010. A new polarization ratio model from C-band RADARSAT-2 fine Quad-Pol imagery. In: Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE, 1948–1951
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