• Bourassa, M. A., and Coauthors, 2019: Remotely sensed winds and wind stresses for marine forecasting and ocean modeling. Front. Mar. Sci., 6, 443, https://doi.org/10.3389/fmars.2019.00443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charbonneau, F. J., and Coauthors, 2010: Compact polarimetry overview and applications assessment. Can. J. Remote Sens., 36, S298S315, https://doi.org/10.5589/m10-062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chelton, D. B., and S. P. Xie, 2010: Coupled ocean-atmosphere interaction at oceanic mesoscales. Oceanography, 23 (4), 5269, https://doi.org/10.5670/oceanog.2010.05.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Denbina, M., and M. J. Collins, 2016: Wind speed estimation using C-band compact polarimetric SAR for wide swath imaging modes. ISPRS J. Photogramm. Remote Sens., 113, 7585, https://doi.org/10.1016/j.isprsjprs.2016.01.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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. Ph.D. dissertation, Université Paris VII.

  • Fang, H., and Coauthors, 2019: Ocean surface wind speed retrieval using simulated RADARSAT Constellation Mission compact polarimetry SAR data. Remote Sens., 11, 1876, https://doi.org/10.3390/rs11161876.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geldsetzer, T., F. Charbonneau, M. Arkett, and T. Zagon, 2015: Ocean wind study using simulated RCM compact-polarimetry SAR. Can. J. Remote Sens., 41, 418430, https://doi.org/10.1080/07038992.2015.1104635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geldsetzer, T., S. K. Khurshid, W. Warner, F. Botelho, and D. Flett, 2019: Wind speed retrieval from simulated RADARSAT Constellation Mission compact polarimetry SAR data for marine application. Remote Sens., 11, 1682, https://doi.org/10.3390/rs11141682.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., 2010: Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF. J. Atmos. Oceanic Technol., 27, 721736, https://doi.org/10.1175/2009JTECHO698.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., A. Stoffelen, and S. de Hann, 2007: An improved C-band scatterometer ocean geophysical model function: CMOD5. J. Geophys. Res., 112, C03006, https://doi.org/10.1029/2006JC003743.

    • Search Google Scholar
    • Export Citation
  • Horstmann, J., H. Schiller, J. Schulz-Stellenfleth, and S. Lehner, 2003: Global wind speed retrieval from SAR. IEEE Trans. Geosci. Remote Sens., 41, 22772286, https://doi.org/10.1109/TGRS.2003.814658.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnsen, H., G. Engen, and G. Guitton, 2008: Sea-surface polarization ratio from Envisat ASAR AP data. IEEE Trans. Geosci. Remote Sens., 46, 36373646, https://doi.org/10.1109/TGRS.2008.2001061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lehner, S., J. Horstmann, W. Koch, and W. Rosenthal, 1998: Mesoscale wind measurements using recalibrated ERS SAR images. J. Geophys. Res., 103, 78477856, https://doi.org/10.1029/97JC02726.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., B. Zhang, W. Perrie, A. Mouche, X. Li, and H. Wang, 2018: A C-band geophysical model function for determining coastal wind speed using synthetic aperture radar. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11, 24172428, https://doi.org/10.1109/JSTARS.2018.2836661.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., B. Zhang, W. Perrie, A. Mouche, and G. Zhang, 2021: CMODH validation for C-band synthetic aperture radar HH polarization wind retrieval over the ocean. IEEE Geosci. Remote Sens. Lett., 18, 102106, https://doi.org/10.1109/LGRS.2020.2967811.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mears, C. A., D. K. Smith, and F. J. Wentz, 2001: Comparison of Special Sensor Microwave Imager and buoy-measured wind speed from 1987 to 1997. J. Geophys. Res., 106, 11 71911 729, https://doi.org/10.1029/1999JC000097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mouche, A., D. Hauser, J.-F. Daloze, and C. Guerin, 2005: Dual-polarization measurements at C-band over the ocean: Results from airborne radar observations and comparison with Envisat ASAR data. IEEE Trans. Geosci. Remote Sens., 43, 753769, https://doi.org/10.1109/TGRS.2005.843951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mouche, A., B. Chapron, B. Zhang, and R. Husson, 2017: Combined co- and cross-polarized SAR measurements under extreme wind conditions. IEEE Trans. Geosci. Remote Sens., 55, 67466755, https://doi.org/10.1109/TGRS.2017.2732508.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nghiem, S. V., S. H. Yueh, R. Kwok, and F. K. Li, 1992: Symmetry properties in polarimetric remote sensing. Radio Sci., 27, 693711, https://doi.org/10.1029/92RS01230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nord, M. E., T. L. Ainsworth, J.-S. Lee, and N. J. S. Stacy, 2009: Comparison of compact polarimetric synthetic aperture radar modes. IEEE Trans. Geosci. Remote Sens., 47, 174188, https://doi.org/10.1109/TGRS.2008.2000925.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peixoto, J. P., and A. H. Oort, 1992: Physics of Climate. American Institute of Physics, 520 pp.

  • Raney, R. K., 2007: Hybrid-polarity SAR architecture. IEEE Trans. Geosci. Remote Sens., 45, 33973404, https://doi.org/10.1109/TGRS.2007.895883.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., and D. Anderson, 1997: Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. J. Geophys. Res., 102, 57675780, https://doi.org/10.1029/96JC02860.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, A. A., 2015: Overview of the RADARSAT Constellation Mission. Can. J. Remote Sens., 41, 401407, https://doi.org/10.1080/07038992.2015.1104633.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. R., T. M. Elfouhaily, and B. Chapron, 1998: Polarization ratio for microwave backscattering from ocean surface at low to moderate incidence angles. 1998 IEEE Int. Geosci. Remote Sens. Symp., Los Alamitos, CA, Institute of Electrical and Electronics Engineers, 1671–1673, https://doi.org/10.1109/IGARSS.1998.692411.

    • Crossref
    • Export Citation
  • Vachon, P. W., and F. W. Dobson, 2000: Wind retrieval from RADARSAT SAR images: Selection of a suitable C-band HH polarization wind retrieval model. Can. J. Remote Sens., 26, 306313, https://doi.org/10.1080/07038992.2000.10874781.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vachon, P. W., and J. Wolfe, 2011: C-band cross-polarization wind speed retrieval. IEEE Geosci. Remote Sens. Lett., 8, 456459, https://doi.org/10.1109/LGRS.2010.2085417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., and A. Ribal, 2019: Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, eaav9527, https://doi.org/10.1126/science.aav9527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., and W. Perrie, 2012: Cross-polarized synthetic aperture radar: A new potential measurement technique for hurricanes. Bull. Amer. Meteor. Soc., 93, 531541, https://doi.org/10.1175/BAMS-D-11-00001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., W. Perrie, and Y. He, 2011: Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model. J. Geophys. Res., 116, C08008, https://doi.org/10.1029/2010JC006522.

    • Search Google Scholar
    • Export Citation
  • Zhang, B., W. Perrie, P. W. Vachon, X. Li, W. G. Pichel, J. Guo, and Y. He, 2012: Ocean vector winds retrieval from C-band fully polarimetric SAR measurements. IEEE Trans. Geosci. Remote Sens., 50, 42524261, https://doi.org/10.1109/TGRS.2012.2194157.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., W. Perrie, J. A. Zhang, E. Uhlhorn, and Y. He, 2014: High-resolution hurricane vector winds from C-band dual-polarization SAR observations. J. Atmos. Oceanic Technol., 31, 272286, https://doi.org/10.1175/JTECH-D-13-00006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., A. Mouche, Y. Lu, W. Perrie, G. Zhang, and H. Wang, 2019: A geophysical model function for wind speed retrieval from C-band HH-polarized synthetic aperture radar. IEEE Geosci. Remote Sens. Lett., 16, 15211525, https://doi.org/10.1109/LGRS.2019.2905578.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., B. Zhang, W. Perrie, Y. He, H. Li, S. Khurshid, and K. Warner, 2019: C-band right-circular polarization ocean wind retrieval. IEEE Geosci. Remote Sens. Lett., 16, 13981401, https://doi.org/10.1109/LGRS.2019.2898557.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Compact Polarimetry Synthetic Aperture Radar Ocean Wind Retrieval: Model Development and Validation

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  • 1 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China
  • 2 Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
  • 3 Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada
  • 4 IFREMER, Université Brest, CNRS, IRD, Laboratoire d’Océanographie Physique et Spatiale, Brest, France
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Abstract

We have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Biao Zhang, zhangbiao@nuist.edu.cn

Abstract

We have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Biao Zhang, zhangbiao@nuist.edu.cn
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