Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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L-band SAR-derived Sea Surface Wind Retrieval off the East Coast of Korea and Error Characteristics

L밴드 인공위성 SAR를 이용한 동해 연안 해상풍 산출 및 오차 특성

  • Kim, Tae-Sung (Department of Science Education, Seoul National University) ;
  • Park, Kyung-Ae (Department of Earth Science Education / Research Institute of Oceanography, Seoul National University) ;
  • Choi, Won-Moon (Department of Science Education, Seoul National University) ;
  • Hong, Sungwook (Satellite Analysis Division, National Meteorological Satellite Center) ;
  • Choi, Byoung-Cheol (Satellite Analysis Division, National Meteorological Satellite Center) ;
  • Shin, Inchul (Satellite Analysis Division, National Meteorological Satellite Center) ;
  • Kim, Kyung-Ryul (School of Earth and Environmental Sciences / Research Institute of Oceanography, Seoul National University)
  • 김태성 (서울대학교 과학교육과) ;
  • 박경애 (서울대학교 지구과학교육과/서울대학교 해양연구소) ;
  • 최원문 (서울대학교 과학교육과) ;
  • 홍성욱 (국가기상위성센터 위성분석과) ;
  • 최병철 (국가기상위성센터 위성분석과) ;
  • 신인철 (국가기상위성센터 위성분석과) ;
  • 김경렬 (서울대학교 지구환경과학부/서울대학교 해양연구소)
  • Received : 2012.09.16
  • Accepted : 2012.10.23
  • Published : 2012.10.31
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Abstract

Sea surface winds in the sea off the east coast of Korea were derived from L-band ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data and their characteristics of errors were analyzed. We could retrieve high-resolution wind vectors off the east coast of Korea including the coastal region, which has been substantially unavailable from satellite scatterometers. Retrieved SAR-wind speeds showed a good agreement with in-situ buoy measurement by showing relatively small an root-mean-square (RMS) error of 0.67 m/s. Comparisons of the wind vectors from SAR and scatterometer presented RMS errors of 2.16 m/s and $19.24^{\circ}$, 3.62 m/s and $28.02^{\circ}$ for L-band GMF (Geophysical Model Function) algorithm 2009 and 2007, respectively, which tended to be somewhat higher than the expected limit of satellite scatterometer winds errors. L-band SAR-derived wind field exhibited the characteristic dependence on wind direction and incidence angle. The previous version (L-band GMF 2007) revealed large errors at small incidence angles of less than $21^{\circ}$. By contrast, the L-band GMF 2009, which improved the effect of incidence angle on the model function by considering a quadratic function instead of a linear relationship, greatly enhanced the quality of wind speed from 6.80 m/s to 1.14 m/s at small incident angles. This study addressed that the causes of wind retrieval errors should be intensively studied for diverse applications of L-band SAR-derived winds, especially in terms of the effects of wind direction and incidence angle, and other potential error sources.

L밴드 ALOS SAR 자료를 활용하여 우리나라 동해 연안해역의 해상풍을 산출하고 오차의 특성을 분석하였다. 그 동안 인공위성 산란계를 이용한 해상풍 산출이 본질적으로 불가능하였던 연안 해역에 대하여 SAR 자료로부터 고해상도 해상풍을 산출할 수 있었다. 산출된 SAR 바람을 해양 부이 자료와 비교한 결과 0.67 m/s의 작은 오차로 잘 일치함을 보여주었다. 서로 다른 L밴드 ALOS PALSAR GMF 2007 모델과 2009 모델로 산출된 해상풍을 인공위성 산란계 해상풍과 비교한 결과, 풍속과 풍향 각각 2.16 m/s와 $19.24^{\circ}$, 3.62 m/s와 $28.02^{\circ}$의 제곱평균근오차를 보이며, 인공위성 산란계의 기대 오차보다 다소 큰 경향을 나타냈다. 또한 산출된 L밴드 SAR 바람장은 풍향과 입사각에 대하여 특징적인 의존성을 보였다. L밴드 GMF 2007 모델은 $21^{\circ}$ 보다 작은 입사각에 대하여 큰 오차를 보인 반면, L밴드 GMF 2009 모델은 입사각에 대한 효과를 선형함수가 아니라 이차함수로 고려하여 주었기 때문에 작은 입사각 범위에서 풍속 오차가 6.8 m/s에서 1.14 m/s로 크게 감소하는 결과를 보였다. 본 연구는 L밴드 SAR 바람장의 다양한 활용을 위해서는 풍향과 입사각 효과, 그리고 다른 잠재적인 오차의 요인을 집중적으로 연구하여야 함을 강조하였다.

Keywords

References

  1. 강문경, 박용욱, 이문진, 이훈열, 2007. SAR를 이용한 해풍, 파랑, 해류 추출 기법 연구, 한국항해항만학회지, 31(1): 35-42. https://doi.org/10.5394/kinpr.2007.31.1.035
  2. 김태림, 1999. 한국 주변 해역에서의 SAR 영상 응용예, 대한원격탐사학회지, 15(1): 61-71.
  3. 윤홍주, 박광순, 김상익, 2006. SAR자료를 이용한 해상풍 산출 및 현장 자료 간의 비교.검정, 한국해양정보통신학회지, 10(4): 706-711.
  4. 이문진, 김혜진, 이승현, 홍기용, 2007. SAR 위성영상 해수유동 정보추출 및 활용, 해양환경안전학회지, 13(4): 21-26.
  5. 황효정, 김현구, 2011. SAR 위성영상을 이용한 해상풍 분석, 한국풍공학회지, 15(4): 67-71.
  6. Bentamy, A., D. Croize-Fillon, and C. Perigaud, 2008. Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations, Ocean Science, 4: 265-274. https://doi.org/10.5194/os-4-265-2008
  7. Ebuchi, N., 2000. Evaluation of NSCAT-2 wind vectors by using statistical distributions of wind speeds and directions, Journal of Oceanography, 56(2): 161-172. https://doi.org/10.1023/A:1011183029009
  8. Ebuchi, N., 2001. Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data, International Geoscience and Remote Sensing Symposium, IGARSS'01, 3: 1082-1085.
  9. Freilich, M.H. and R.S. Dunbar, 1999. The accuracy of the NSCAT-1 vector winds: comparisons with NDBC buoys, Journal of Geophysical Research, 104(C5): 11231-11246. https://doi.org/10.1029/1998JC900091
  10. Horstmann, J., W. Koch, and S. Lehner, 2002. High resolution wind fields retrieved from SAR in comparison to numerical models, International Geoscience and Remote Sensing Symposium, IGARSS'02, 3(3): 1877-1879.
  11. Isoguchi, O. and M. Shimada, 2007. An L-band model function for the ocean-normalized radar cross section derived from PALSAR, Proc. of 1st Joint PI Symposium of ALOS Data Nodes for ALOS Science Program, Kyoto, Nov. 19-23, Digitally available on CD.
  12. Isoguchi, O. and M. Shimada, 2009. An L-band ocean geophysical model function derived from PALSAR, IEEE Transactions on Geoscience and Remote Sensing, 47(7): 1925-1936. https://doi.org/10.1109/TGRS.2008.2010864
  13. Kang, M.K. and H. Lee, 2007. Estimation of ocean current velocity near Incheon using Radarsat- 1 SAR and HF-radar data, Korean Journal of Remote Sensing, 23(5): 421-430. https://doi.org/10.7780/kjrs.2007.23.5.421
  14. Kim, D.J., 2009. Wind retrieval from X-band SAR image using numerical ocean scattering model, Korean Journal of Remote Sensing, 25(3): 243-253. https://doi.org/10.7780/kjrs.2009.25.3.243
  15. Kim, D.J. and W.I. Moon, 2002. Estimation of sea surface wind vector using RADARSAT data, Remote Sensing of Environment, 80(1): 55-64. https://doi.org/10.1016/S0034-4257(01)00267-X
  16. Kim, D.J., W.I. Moon, D.A. Imel, and D. Moller, 2001. Remote sensing of ocean waves and currents in Ulsan area using AIRSAR Along- Track Interferometry(ATI), International Symposium on Remote Sensing, 17(1): 657-662.
  17. Kim, T.S. and K.A. Park, 2011. Estimation of polarization ratio for sea surface wind retrieval from SIR-C SAR Data, Korean Journal of Remote Sensing, 27(6): 729-740. https://doi.org/10.7780/kjrs.2011.27.6.729
  18. Kim, T.S., K.A. Park, and W.I. Moon, 2010. Wind vector retrieval from SIR-C SAR data off the east coast of Korea, Journal of Korean Earth Science Society, 31(5): 475-487. https://doi.org/10.5467/JKESS.2010.31.5.475
  19. Liu, W.T. and W. Tang, 1996. Equivalent neutral wind, JPL Publication 96-17, Jet Propulsion Laboratory, Pasadena, CA, USA.
  20. Liu, W.T., K.B. Katsaros, and J.A. Businger, 1979. Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface, Journal of the Atmospheric Sciences, 36: 1722-1735. https://doi.org/10.1175/1520-0469(1979)036<1722:BPOASE>2.0.CO;2
  21. Park, K.A. and K.R. Kim, 2010. Unprecedented coastal upwelling in the East/Japan Sea and linkage to long-term large-scale variations, Geophysical research letters, 37:L09603, doi:10.1029/2009GL042231.
  22. Paulson, C.A., 1970. The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer, Journal of Applied Meteorology, 9: 857-861. https://doi.org/10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2
  23. Pensieri, S., R. Bozzano, and M.E. Schiano, 2010. Comparison between QuikSCAT and buoy wind data in the Ligurian Sea, Journal of Marine Systems, 81(4): 286-296. https://doi.org/10.1016/j.jmarsys.2010.01.004
  24. Pickett, M.H., W. Tang, L.K. Rosenfeld, and C.H. Wash, 2003. QuikSCAT satellite comparisons with nearshore buoy wind data off the U.S. west coast, Journal of Atmospheric and Oceanic Technology, 20(12): 1869-1879. https://doi.org/10.1175/1520-0426(2003)020<1869:QSCWNB>2.0.CO;2
  25. Satheesan, K., A. Sarkar, A. Parekh, M.R. Ramesh Kumar, and Y. Kuroda, 2007. Comparison of wind data from QuikSCAT and buoys in the Indian Ocean, International Journal of Remote Sensing, 28(10): 2375-2382. https://doi.org/10.1080/01431160701236803
  26. Takeyama, Y., T. Ohsawa, K. Kozai, C. Hasager, and M. Badger, 2010. Effect of wind direction on ENVISAT ASAR wind speed retrieval, Proc. of Techno-Ocean Network 2010, Kobe, Oct. 14-16, Digitally available on CD.
  27. 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, Canadian Journal of Remote Sensing, 26(4): 306-313. https://doi.org/10.1080/07038992.2000.10874781
  28. Wackerman, C.C., C.L. Rufenach, R.A. Shuchman, J.A. Johannessen, and K.L. Davidson, 1996. Wind vector retrieval using ERS-1 synthetic aperture radar imagery, IEEE Transactions on Geoscience and Remote Sensing, 34(6): 1343-1352. https://doi.org/10.1109/36.544558
  29. Won, J.S., H.S. Jeong, and T.R. Kim, 1998. Wind field estimation using ERS-1 SAR data: The initial report, International Symposium on Remote Sensing, 14: 286-291.

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