Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Oceanic Application of Satellite Synthetic Aperture Radar - Focused on Sea Surface Wind Retrieval -

인공위성 합성개구레이더 영상 자료의 해양 활용 - 해상풍 산출을 중심으로 -

  • Jang, Jae-Cheol (Department of Science Education, Seoul National University) ;
  • Park, Kyung-Ae (Department of Earth Science Education/Research Institute of Oceanography, Seoul National University)
  • 장재철 (서울대학교 과학교육과) ;
  • 박경애 (서울대학교 지구과학교육과/해양연구소)
  • Received : 2019.06.12
  • Accepted : 2019.08.14
  • Published : 2019.10.31
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Abstract

Sea surface wind is a fundamental element for understanding the oceanic phenomena and for analyzing changes of the Earth environment caused by global warming. Global research institutes have developed and operated scatterometers to accurately and continuously observe the sea surface wind, with the accuracy of approximately ${\pm}20^{\circ}$ for wind direction and ${\pm}2m\;s^{-1}$ for wind speed. Given that the spatial resolution of the scatterometer is 12.5-25.0 km, the applicability of the data to the coastal area is limited due to complicated coastal lines and many islands around the Korean Peninsula. In contrast, Synthetic Aperture Radar (SAR), one of microwave sensors, is an all-weather instrument, which enables us to retrieve sea surface wind with high resolution (<1 km) and compensate the sparse resolution of the scatterometer. In this study, we investigated the Geophysical Model Functions (GMF), which are the algorithms for retrieval of sea surface wind speed from the SAR data depending on each band such as C-, L-, or X-band radar. We reviewed in the simulation of the backscattering coefficients for relative wind direction, incidence angle, and wind speed by applying LMOD, CMOD, and XMOD model functions, and analyzed the characteristics of each GMF. We investigated previous studies about the validation of wind speed from the SAR data using these GMFs. The accuracy of sea surface wind from SAR data changed with respect to observation mode, GMF type, reference data for validation, preprocessing method, and the method for calculation of relative wind direction. It is expected that this study contributes to the potential users of SAR images who retrieve wind speeds from SAR data at the coastal region around the Korean Peninsula.

해상풍은 해양 현상을 이해하고, 지구 온난화에 의한 지구 환경의 변화를 분석하기 위한 필수 요소이다. 전세계 연구 기관은 해상풍을 정확하고 지속적으로 관측하기 위해 산란계(scatterometer)를 개발하여 운영해오고 있으며, 정확도는 풍향이 ${\pm}20^{\circ}$, 풍속이 ${\pm}2m\;s^{-1}$ 안팎이다. 하지만, 산란계의 해상도는 12.5-25.0 km로, 해안선이 복잡하고 섬이 많은 한반도 근해에서는 자료의 결측이 빈번하게 발생하여 활용도가 감소한다. 그에 반해, Synthetic Aperture Radar (SAR, 합성개구레이더)는 마이크로파를 활용하는 전천후 센서로, 1 km 이하의 고해상도 해상풍이 산출이 가능하여 산란계의 단점 보완이 가능하다. 본 연구에서는 일반적으로 활용되는 SAR 자료 기반 해상풍 산출 알고리즘인 Geophysical Model Function (GMF, 지구 물리 모델 함수)를 밴드별로 분류하여 조사하였다. 상대 풍향, 입사각, 풍속에 따른 후방산란계수를 L-band Model (LMOD, L 밴드 모델), C-band Model (CMOD, C 밴드 모델), X-band Model (XMOD, X 밴드 모델)에 적용하여 모의하였고, 각 GMF의 특성을 분석하였다. 이러한 GMF를 SAR 탑재 인공위성 자료에 적용하여 산출한 해상풍의 정확도 검증 연구에 대해 조사하였다. SAR 자료 기반 해상풍의 정확도는 영상 관측 모드, 적용한 GMF의 종류, 정확도 비교 기준 자료, SAR 자료 전처리 방법, 상대 풍향 정보 산출 방법 등에 따라 변하는 것으로 나타났다. 본 연구를 통해 국내 연구자들의 SAR 자료 기반 해상풍 산출 방법에 대한 접근성이 향상되고, 고해상도 해상풍 자료를 활용한 한반도 근해 분석에 이바지할 것으로 기대된다.

Keywords

References

  1. Beaucage, P., Glazer, A., Choisnard, J., Yu, W., Bernier, M., Benoit, R., and Lafrance, G., 2007, Wind assessment in a coastal environment using synthetic aperture radar satellite imagery and a numerical weather prediction model. Canadian Journal of Remote Sensing, 33(5), 368-377. https://doi.org/10.5589/m07-043
  2. Chan, Y.K. and Koo, V.C., 2008, An introduction to synthetic aperture radar (SAR). Progress In Electromagnetics Research B, 2, 27-60. https://doi.org/10.2528/PIERB07110101
  3. Cipollini, P., Benveniste, J., Bouffard, J., Emery, W., Gommenginger, C., Griffin, D., Hoyer, J., Kurapov, A., Madsen, K., Mercier, F., Miller, L., Pascual, A., Ravichandran, M., Shillington, F., Snaith, H., Strub, P.T., Vandemark, D., Vignudelli, S., Wilkin, J., Woodworth, P., and Pascual, A., 2010, The role of altimetry in coastal observing systems. In Ocean information for society: sustating the benefits, realizing the potential, Proceedings of OceanObs, 9, 181-191.
  4. Cornillon, P. and Park, K., 2001, Warm core ring velocities inferred from NSCAT. Geophysical Research Letters, 28(4), 575-578. https://doi.org/10.1029/2000gl011487
  5. Ebuchi, N., 1999, Statistical distribution of wind speeds and directions globally observed by NSCAT. Journal of Geophysical Research: Oceans, 104(C5), 11393-11403. https://doi.org/10.1029/98JC02061
  6. Elfouhaily, T., 1996, Physical modeling of electromagnetic backscatter from the ocean surface: Application toretrieval of wind fields and wind stress by remote sensing of the marine atmospheric boundary layer. Ph.D. dissertation, University of Paris VII, Paris, France.
  7. Graf, J.E., Tsi, W.Y., and Jones, L, 1998, Overview of QuikSCAT mission-a quick deployment of a high resolution, wide swath scanning scatterometer for ocean wind measurement. In Proceedings IEEE Southeastcon '98' Engineering for a New Era', 314-317.
  8. Grieco, G., Nirchio, F., and Migliaccio, M., 2015, Application of state-of-the-art SAR X-band geophysical model functions (GMFs) for sea surface wind (SSW) speed retrieval to a data set of the Italian satellite mission COSMO-SkyMed. International Journal of Remote Sensing, 36(9), 2296-2312. https://doi.org/10.1080/01431161.2015.1034893
  9. Hasager, C.B., Dellwik, E., Nielsen, M., and Furevik, B.R., 2004, Validation of ERS-2 SAR offshore wind-speed maps in the North Sea. International Journal of Remote Sensing, 25(19), 3817-3841. https://doi.org/10.1080/01431160410001688286
  10. Hasager, C.B., Mouche, A., Badger, M., Bingol, F., Karagali, I., Driesenaar, T., Stoffelen, A., Pena, A., and Longepe, N., 2015, Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sensing of Environment, 156, 247-263. https://doi.org/10.1016/j.rse.2014.09.030
  11. 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. https://doi.org/10.1175/2009JTECHO698.1
  12. Hersbach, H., Stoffelen, A., and De Haan, S., 2005, The improved C-band geophysical model function CMOD5. In Envisat and ERS Symposium, 572.
  13. Hersbach, H., Stoffelen, A., and de Haan, S., 2007, An improved C-band scatterometer ocean geophysical model function: CMOD5. Journal of Geophysical Research: Oceans, 112(C3).
  14. Isoguchi, O., Ishizuka, K., Tadono, T., Motohka, T., and Shimada, M., 2019, Effect of Faraday Rotation on Lband ocean normalized radar cross section and wind speed detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  15. Isoguchi, O. and Shimada, M., 2007, An L-band model function for the ocean-normalized radar cross section derived from PALSAR. In Proceeding of 1st Joint PI Symposium of ALOS Data Nodes for ALOS Science Program, 19-23.
  16. Isoguchi, O. and Shimada, M., 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
  17. Isoguchi, O. and Shimada, M., 2016, Detection of wind fields from PALSAR-2. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 Proceedings, 2209-2211.
  18. Jagdish, Kumar, S.A., Chakraborty, A., and Kumar, R., 2018, Validation of wind speed retrieval from RISAT-1 SAR images of the North Indian Ocean. Remote Sensing Letters, 9(5), 421-428. https://doi.org/10.1080/2150704X.2018.1430392
  19. James, R. J., 1989, A history of radar. IEE Review, 35(9), 343-349. https://doi.org/10.1049/ir:19890152
  20. Jang, J.C. and Yang, D., 2018, Validation of sea surface wind estimated from KOMPSAT-5 backscattering coefficient data. Korean Journal of Remote Sensing, 34(6-3), 1383-1398. https://doi.org/10.7780/KJRS.2018.34.6.3.6
  21. Jang, J.C., Park, K.A., Mouche, A.A., Chapron, B., and Lee, J.H., 2019, Validation of sea surface wind from Sentinel-1A/B SAR data in the coastal regions of the Korean Peninsula. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  22. Johansson, A.M., Brekke, C., Spreen, G., and King, J.A., 2018, X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring. Remote Sensing of Environment, 204, 162-180. https://doi.org/10.1016/j.rse.2017.10.032
  23. Katsaros, K.B. and Brown, R.A., 1991, Legacy of the Seasat mission for studies of the atmosphere and air-sea-ice interactions. Bulletin of the American Meteorological Society, 72(7), 967-982. https://doi.org/10.1175/1520-0477(1991)072<0967:LOTSMF>2.0.CO;2
  24. Kim, T.S., Park, K., Choi, W.M., Hong, S., Choi, B.C., Shin, I., and Kim, K.R., 2012, L-band SAR-derived sea surface wind retrieval off the east coast of Korea and error characteristics. Korean Journal of Remote Sensing, 28(5), 477-487. https://doi.org/10.7780/kjrs.2012.28.5.1
  25. Kim, T.S., Park, K.A., Li, X., Lee, M., Hong, S., Lyu, S.J., and Nam, S., 2015, Detection of the Hebei Spirit oil spill on SAR imagery and its temporal evolution in a coastal region of the Yellow Sea. Advances in Space Research, 56(6), 1079-1093. https://doi.org/10.1016/j.asr.2015.05.040
  26. Kim, T.S., Park, K.A., Li, X., Mouche, A.A., Chapron, B., and Lee, M., 2017, Observation of wind direction change on the sea surface temperature front using highresolution full polarimetric SAR data. IEEE Journal of selected topics in applied earth observations and remote sensing, 10(6), 2599-2607. https://doi.org/10.1109/JSTARS.2017.2660858
  27. Koch, W., 2004, Directional analysis of SAR images aiming at wind direction. IEEE Transactions on Geoscience and Remote Sensing, 42(4), 702-710. https://doi.org/10.1109/TGRS.2003.818811
  28. Komarov, S., Komarov, A., and Zabeline, V., 2012, Marine wind speed retrieval from RADARSAT-2 dualpolarization imagery. Canadian Journal of Remote Sensing, 37(5), 520-528. https://doi.org/10.5589/m11-063
  29. Lehner, S., Horstmann, J., Koch, W., and Rosenthal, W., 1998, Mesoscale wind measurements using recalibrated ERS SAR images. Journal of Geophysical Research: Oceans, 103(C4), 7847-7856. https://doi.org/10.1029/97JC02726
  30. Liu, G., Yang, X., Li, X., Zhang, B., Pichel, W., Li, Z., and Zhou, X., 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. https://doi.org/10.1109/JSTARS.2013.2242848
  31. Liu, K.S. and Chan, J.C., 1999, Size of tropical cyclones as inferred from ERS-1 and ERS-2 data. Monthly Weather Review, 127(12), 2992-3001. https://doi.org/10.1175/1520-0493(1999)127<2992:SOTCAI>2.0.CO;2
  32. Liu, W.T., Tang, W., and Polito, P. S., 1998, NASA scatterometer provides global ocean-surface wind fields with more structures than numerical weather prediction. Geophysical Research Letters, 25(6), 761-764. https://doi.org/10.1029/98GL00544
  33. Li, X., Zhang, J.A., Yang, X., Pichel, W.G., DeMaria, M., Long, D., and Li, Z., 2013, Tropical cyclone morphology from spaceborne synthetic aperture radar. Bulletin of the American Meteorological Society, 94(2), 215-230. https://doi.org/10.1175/BAMS-D-11-00211.1
  34. Li, X.M. and Lehner, S., 2013, Algorithm for sea surface wind retrieval from TerraSAR-X and TanDEM-X data. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2928-2939. https://doi.org/10.1109/TGRS.2013.2267780
  35. Mai, M., Zhang, B., Li, X., Hwang, P.A., and Zhang, J.A., 2016, Application of AMSR-E and AMSR2 low-frequency channel brightness temperature data for hurricane wind retrievals. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4501-4512. https://doi.org/10.1109/TGRS.2016.2543502
  36. Massonnet, D. and Souyris, J.C., 2008, Imaging with synthetic aperture radar. EPFL press.
  37. Merrifield, M.A. and Maltrud, M.E., 2011, Regional sea level trends due to a Pacific trade wind intensification. Geophysical Research Letters, 38(21).
  38. Meyer, F.J., Mahoney, A.R., Eicken, H., Denny, C.L., Druckenmiller, H.C., and Hendricks, S., 2011, Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry. Remote Sensing of Environment, 115(12), 3029-3043. https://doi.org/10.1016/j.rse.2011.06.006
  39. Meyer, F.J., Nicoll, J.B., and Doulgeris, A.P., 2013, Correction and characterization of radio frequency interference signatures in L-band synthetic aperture radar data. IEEE Transactions on Geoscience and Remote Sensing, 51(10), 4961-4972. https://doi.org/10.1109/TGRS.2013.2252469
  40. Monaldo, F.M., Thompson, D.R., Beal, R.C., Pichel, W.G., and Clemente-Colon, P., 2001, Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements. IEEE Transactions on Geoscience and Remote Sensing, 39(12), 2587-2600. https://doi.org/10.1109/36.974994
  41. Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., and Papathanassiou, K.P., 2013, A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine, 1(1), 6-43. https://doi.org/10.1109/MGRS.2013.2248301
  42. Mouche, A.A., Chapron, B., Zhang, B., and Husson, R., 2017, Combined co-and cross-polarized SAR measurements under extreme wind conditions. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6746-6755. https://doi.org/10.1109/TGRS.2017.2732508
  43. Nirchio, F. and Venafra, S., 2013, XMOD2-An improved geophysical model function to retrieve sea surface wind fields from Cosmo-Sky Med X-band data. European Journal of Remote Sensing, 46(1), 583-595. https://doi.org/10.5721/EuJRS20134634
  44. Oliver, C. and Quegan, S., 2004, Understanding synthetic aperture radar images. SciTech Publishing.
  45. Oliver, C.J., 1989, Synthetic-aperture radar imaging. Journal of Physics D: Applied Physics, 22(7), 871. https://doi.org/10.1088/0022-3727/22/7/001
  46. Park, K., Cornillon, P., and Codiga, D.L., 2006, Modification of surface winds near ocean fronts: Effects of Gulf Stream rings on scatterometer (QuikSCAT, NSCAT) wind observations. Journal of Geophysical Research: Oceans, 111(C3).
  47. Park, K., Park, J.J., Jang, J.C., Lee, J.H., Oh, S., and Lee, M., 2018, Multi-spectral ship detection using optical, hyperspectral, and microwave SAR remote sensing data in coastal regions. Sustainability, 10(11), 4064. https://doi.org/10.3390/su10114064
  48. Quilfen, Y., Chapron, B., Collard, F., and Vandemark, D., 2004, Relationship between ERS scatterometer measurement and integrated wind and wave parameters. Journal of Atmospheric and Oceanic Technology, 21(2), 368-373. https://doi.org/10.1175/1520-0426(2004)021<0368:RBESMA>2.0.CO;2
  49. Quilfen, Y., Chapron, B., Elfouhaily, T., Katsaros, K., and Tournadre, J., 1998, Observation of tropical cyclones by high-resolution scatterometry. Journal of Geophysical Research: Oceans, 103(C4), 7767-7786. https://doi.org/10.1029/97JC01911
  50. Rignot, E., 2008, Changes in West Antarctic ice stream dynamics observed with ALOS PALSAR data. Geophysical Research Letters, 35(12).
  51. Risien, C.M. and Chelton, D.B., 2008, A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data. Journal of Physical Oceanography, 38(11), 2379-2413. https://doi.org/10.1175/2008JPO3881.1
  52. Rodriguez, E., Gaston, R.W., Durden, S.L., Stiles, B., Spencer, M., Veilleux, L., Hughes, R., Fernadez, D.E., Chan, S., Veleva, S., and Dunbar, R.S., 2009, A scatterometer for XOVWM, the extended ocean vector winds mission. In 2009 IEEE Radar Conference, 1-4.
  53. Shao, W., Zhang, Z., Li, X., and Wang, W., 2016, Sea surface wind speed retrieval from TerraSAR-X HH polarization data using an improved polarization ratio model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11), 4991-4997. https://doi.org/10.1109/JSTARS.2016.2590475
  54. Shimada, M., Nakatani, H., Isono, K., and Kawada, T., 1999, Removal of the interference appeared within the SAR images. Advances in Space Research, 23(8), 1505-1508. https://doi.org/10.1016/S0273-1177(99)00304-X
  55. Skolnik, M.I., 1980, Introduction to radar systems. NY, New York: McGraw-Hill.
  56. Spreen, G., Kwok, R., and Menemenlis, D., 2011, Trends in arctic sea ice drift and role of wind forcing: 1992-2009. Geophysical Research Letters, 38(19).
  57. Stoffelen, A. and Anderson, D., 1997, Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. Journal of Geophysical Research: Oceans, 102(C3), 5767-5780. https://doi.org/10.1029/96JC02860
  58. Takeyama, Y., Ohsawa, T., Kozai, K., Hasager, C., and Badger, M., 2013, Comparison of geophysical model functions for SAR wind speed retrieval in Japanese coastal waters. Remote Sensing, 5(4), 1956-1973. https://doi.org/10.3390/rs5041956
  59. Tamura, T. and Ohshima, K.I., 2011, Mapping of sea ice production in the Arctic coastal polynyas. Journal of Geophysical Research: Oceans, 116(C7).
  60. Tang, W., Liu, W.T., and Stiles, B.W., 2004, Evaluation of high-resolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1762-1769. https://doi.org/10.1109/TGRS.2004.831685
  61. Thompson, D.R., Elfouhaily, T.M., and Chapron, B., 1998, Polarization ratio for microwave backscattering from the ocean surface at low to moderate incidence angles. In 1998 IEEE International Geoscience and Remote Sensing. IGARSS'98 Proceedings, 3, 1671-1673.
  62. Vachon, P.W. and Dobson, F.W., 2000, Wind retrieval from RADARSAT SAR images: Selection of a suitable Cband HH polarization wind retrieval model. Canadian Journal of Remote Sensing, 26(4), 306-313. https://doi.org/10.1080/07038992.2000.10874781
  63. Verspeek, J., Stoffelen, A., Verhoef, A., and Portabella, M., 2012, Improved ASCAT wind retrieval using NWP ocean calibration. IEEE Transactions on Geoscience and Remote Sensing, 50(7), 2488-2494. https://doi.org/10.1109/TGRS.2011.2180730
  64. Wakabayashi, H., Matsuoka, T., Nakamura, K., and Nishio, F., 2004, Polarimetric characteristics of sea ice in the Sea of Okhotsk observed by airborne L-band SAR. IEEE Transactions on Geoscience and Remote Sensing, 42(11), 2412-2425. https://doi.org/10.1109/TGRS.2004.836259
  65. Wakabayashi, H., Mori, Y., and Nakamura, K., 2013, Sea ice detection in the sea of Okhotsk using PALSAR and MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1516-1523. https://doi.org/10.1109/JSTARS.2013.2258327
  66. Xu, L., Zhang, H., Wang, C., Zhang, B., and Tian, S., 2016, Compact polarimetric SAR ship detection with m-a decomposition using visual attention model. Remote Sensing, 8(9), 751. https://doi.org/10.3390/rs8090751
  67. Yang, X., Li, X., Pichel, W.G., and Li, Z., 2011, Comparison of ocean surface winds from ENVISAT ASAR, MetOp ASCAT scatterometer, buoy measurements, and NOGAPS model. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4743-4750. https://doi.org/10.1109/TGRS.2011.2159802
  68. Yuan, X., Martinson, D.G., and Liu, W.T., 1999, Effect of air-sea-ice interaction on winter 1996 Southern Ocean subpolar storm distribution. Journal of Geophysical Research: Atmospheres, 104(D2), 1991-2007. https://doi.org/10.1029/98JD02719
  69. Zhang, B., Li, X., Perrie, W., and He, Y., 2015, Synergistic measurements of ocean winds and waves from SAR. Journal of Geophysical Research: Oceans, 120(9), 6164-6184. https://doi.org/10.1002/2015JC011052
  70. Zhang, B., Perrie, W., and He, Y., 2011, Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model. Journal of Geophysical Research: Oceans, 116(C8).
  71. Zhang, B., Perrie, W., Vachon, P.W., Li, X., Pichel, W.G., Guo, J., and He, Y., 2012, Ocean vector winds retrieval from C-band fully polarimetric SAR measurements. IEEE Transactions on Geoscience and Remote Sensing, 50(11), 4252-4261. https://doi.org/10.1109/TGRS.2012.2194157