KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Estimation of Spatial Evapotranspiration Using satellite images and SEBAL Model

위성영상과 SEBAL 모형을 이용한 공간증발산량 산정 연구

  • 하림 (건국대학교 사회환경시스템공학과) ;
  • 신형진 (건국대학교 사회환경시스템공학과) ;
  • 이미선 (건국대학교 사회환경시스템공학과) ;
  • 김성준 (건국대학교 사회환경시스템공학과)
  • Received : 2009.03.25
  • Accepted : 2010.03.08
  • Published : 2010.06.30
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Abstract

SEBAL (Surface Energy Balance Algorithm for Land) developed by Bastiaanssen (1995) is an image-processing model comprisedof twenty-five sub models that calculates spatial evapotranspiration (ET) and other energy exchanges at the surface. SEBAL uses image data from Landsat or other satellites measuring thermal infrared radiation, visible and near infrared. In this study, the model was applied to Gyeongancheon watershed, the main tributary of Han river Basin. ET was computed on apixel-by-pixel basis from an energy balance using 4 years (2001-2004) Landsat and MODIS images. The scale effect between Landsat (30 m) and MODIS (1 km) was evaluated. The results both from Landsat and MODIS were compared with FAO Penman-Monteith ET. The absolute errors between satellite ETs and Penman-Monteith ET were within 12%. The spatial and temporal characteristics of ET distribution within the watershed were also analyzed.

Bastiaanssen(1995)에 의해 개발된 SEBAL(Surface Energy Balance Algorithm for Land) 모형은 25개의 sub model들을 이용하여 지표의 증발산량과 기타 여러 에너지 교환을 계산하는 이미지-프로세싱 모형이다. SEBAL 모형은 Landsat 또는 기타 여러 위성영상을 통해 얻을 수 있는 열적외선 방사, 표시 및 근적외선 측정 자료 등을 사용한다. 본 연구에서는 한강유역의 주 지류인 경안천 유역에 모형을 적용시켰다. 증발산량(ET)은 4개년의(2001년-2004년) Landsat과 MODIS 위성영상을 입력자료로 사용하여, 에너지 균형원리를 통해 pixel-by-pixel을 기준으로 계산되었다. Landsat(30 m)과 MODIS(1 km) 사이의 비교 결과도 평가되었으며, Landsat과 MODIS 결과들은 FAO Penman-Monteith 증발산량과 비교하였다. 위성영상 ET들과 FAO Penman-Monteith ET 간의 절대 오차는 12% 이내로 확인되었으며, 유역 분포 증발산량의 시공간분포특성 또한 분석하였다.

Keywords

References

  1. 김주훈, 김경탁, 박정술(2005) LAI를 고려한 잠재증발산량 추정. 한국지리정보학회지. 한국지리정보학회, 제8권 제4호, pp.1-13.
  2. 신사철, 안태용(2004) NDVI와 기온자료를 이용한 광역증발산량의 추정. 한국지리정보학회지. 한국지리정보학회, 제7권 제3호, pp. 79-89.
  3. 신사철, 안태용(2007) 인공위성 자료를 활용한 광역증발산량의 산정방법 개발, 한국지리정보학회지. 한국지리정보학회, 제10권 제2호, pp. 70-80.
  4. 유진웅(2003) SEBAL 모형을 이용한 증발산량의 추정. 석사학위논문, 서울대학교.
  5. Anthony, M., Masahiro, T., Richard, G. A., and William, J.K. (2000) Final report; application consumptive use of water and streamflow depletion in the bear river basin of idaho through remote sensing. ldaho Department of Water Resources.
  6. Bastiaanssen, W.G.M. (1995) Regionalization of Surface flux densities and moisture indicators in composite terrain : A remote sensing approach under clear skies in Mediterranean climates. Wageningen Agricultural University, Wageningen. The Netherlands.
  7. Bastiaanssen, W.G.M. (1998a) Remote sensing ln water resources management : the state of the art. lnternational Water Management lnstitute, Colombo, Sri Lanka, pp. 118.
  8. Bastiaanssen, W.G.M. (2000) SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin. Turkey, Journal of Hydrology, vol. 229, pp. 87-100. https://doi.org/10.1016/S0022-1694(99)00202-4
  9. Bastiaanssen, W.G.M., Menenti, M., Feddes, R. A., and Holtslag, A.A.M. (1998b) A remote sensing sllrface energy balance algorithm for land (SEBAL) : 1. Formulation. Journal of Hydrology, 212-213, pp. 198-212. https://doi.org/10.1016/S0022-1694(98)00253-4
  10. Bastiaanssen, W.G.M., Pelgrllm, H., Wang, J., Ma, Y., Moreno, J.F., Roerink, G.J., and van der Wal, T. (1998c) A remote sensing surface energy balance algorithm1 for land (SEBAL) : 2. Validation. Journal of Hydrology, 212-213, pp. 213-229. https://doi.org/10.1016/S0022-1694(98)00254-6
  11. Brakke, T. W. and Kanemasu, E.T. (1981) Insolation estimation from satellite measurements of reflected radiation. Remote Sensing of Environment, Vol. 11, pp. 157-167. https://doi.org/10.1016/0034-4257(81)90015-8
  12. Gautier, c., Diak, G., and Masse, S. (1980) A simple physical model to estimate incident solar radiation at the surface from GOES satellite data. J. Appl. Meteor., Vol. 19, pp. 1005-1012. https://doi.org/10.1175/1520-0450(1980)019<1005:ASPMTE>2.0.CO;2
  13. Gurney, R.J. and Hall, D.J. (1983) Satellite-derived surface energy balance estimates in the alaskan sub-arctic. J. Climate and Applied Met. Vol. 22, pp. 115-125. https://doi.org/10.1175/1520-0450(1983)022<0115:SDSEBE>2.0.CO;2
  14. Heilman, J.L., Kanemasu, E.T., Bagley, J.O., and Rasmussen, V.P. (1977) Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data. Remote Sensing of Environment, Vol. 6, No. 4, pp. 315-326. https://doi.org/10.1016/0034-4257(77)90051-7
  15. Kustas, W. P. (1995) Recent advances associated with large scale field experiments in hydrology. Rev. of Geophys. Suppl, pp. 959-965.
  16. Kustas, W.P. and Norman, J.M. (1996) Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal, Vol. 41, pp. 495-516. https://doi.org/10.1080/02626669609491522
  17. MacFarland, M.J., Miller, R.I., and Neale, CM. U. (1990) Land surface temperature derived from SSM/l passive microwave brightness temperatures. IEEE Trans. Geosci. Remote Sens., Vol. 28, pp. 839-845. https://doi.org/10.1109/36.58971
  18. Moran, M.S., Jackson, R.D., Raymond, L.H., Gay, L.W. and Slater, P.N. ( 1989) Mapping surface energy balance components by combining LANDSAT Thematic Mapper and ground-based mereorological data. Remote Sens. Environ. Vol. 30, pp. 77-87. https://doi.org/10.1016/0034-4257(89)90049-7
  19. Norman, J.M., and Becker, F. (1995) Termino;ogy in thermal infrared remote sensing of natural surface. Remote Sens. Rev., Vol. 12, pp. 159-173. https://doi.org/10.1080/02757259509532284
  20. Pinker, R.T., Frovin, R., and Li, Z. (1995) A review of satellite methods to derive surface shortwave irradiance. Remote Sens. Environ. Vol. 51, pp. 108-124. https://doi.org/10.1016/0034-4257(94)00069-Y
  21. Price, J.T. (1982) The law and management of water resources and supply : A. S. Wisdom and J. L. G. Skeet Shaw and Sons, 275. Advances in Water Resources, Vol. 5, No. 4, pp. 225.
  22. Reginato, R.J., Jackson, R.D., and Pinter Jr, P.J. (1985) Evapotrans-piration calculated from remote multispectral and ground station meteorological data. Remote Sensing of Environment, Vol. 18, No. 1, pp. 75-89. https://doi.org/10.1016/0034-4257(85)90039-2
  23. Sellers, P.J., Meeson, B.W., Hall, F.G., Asrar, G, Murphy, R.E., Schiffer, R.A., Bremerton, F.P., Dickinson, R.E., Ellingson, R.G., Field, C.B., Huemmrich, K.F., Justice, C.O., Melack, J.M., Roulet, N.T., Schimel, D.S., and Try, P.D. (1995) Remote sensing of the land surface for studies of global change: Models - algorithms - experiments. Remote Sens. Environ., Vol. 51, pp. 1 -17. https://doi.org/10.1016/0034-4257(95)90011-X
  24. Soer, G.J.R. (1980) Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures. Remote Sens. Environ., Vol. 9, pp. 27-45. https://doi.org/10.1016/0034-4257(80)90045-0
  25. Sugita, M. and Brutsaett, W. (1991) Daily evaporation over a region from lower boundary layer profiles. Wat. Resour. Res., Vol. 27, pp. 747-752. https://doi.org/10.1029/90WR02706
  26. Taconet, O., Carlson, T., Bernard, R., and Vidal-Madjar, D. (1986) Evaluation of a surface/vegetation parameterization using satellite measurements of surface temperature. Clim. Appl. Met,. Vol. 25, pp. 1752-1767. https://doi.org/10.1175/1520-0450(1986)025<1752:EOASPU>2.0.CO;2