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An intercomparison of two satellite data-based evapotranspiration approaches

인공위성 데이터 기반의 두 공간 증발산 산정 모형 비교 분석

  • 서찬양 (한양대학교 건설환경공학과) ;
  • 최민하 (한양대학교 건설환경공학과)
  • Received : 2011.08.05
  • Accepted : 2011.10.14
  • Published : 2011.12.31

Abstract

Evapotranspiration (ET) including evaporation from a land surface and transpiration from photosynthesis of vegetation is a hydrological factor that has an important role in water cycle. However, there is a limitation to understand it due to heterogeneity of land cover and vegetation. In this study, Mapping EvapoTRanspiration with Internalized Calibration (METRIC) model, one of the energy balance models, and MODerate resolution Imaging Spectroradiometer (MODIS) satellite based well-known Penman-Monteith algorithm were compared. Two ET maps were categorized and compared by land cover classification. The results represented overall applicability of the two models with the highest correlation coefficients in needleleaf and broadleaf forests. This study will be useful to estimate remote sensing based ET maps with high resolution and to figure out spatio-temporal variability and seasonal changes.

증발산은 토양 표면에서 일어나는 증발 과정과 식물의 광합성으로 인해 발생하는 증산 작용을 포함한 수문 기상인자로 수문 순환과정에서 중요한 역할을 차지한다. 현재 국내외에서는 증발산을 산정하고 공간적인 거동을 파악하기 위한 연구가 활발히 진행되고 있지만 특정 지역에서의 토지 피복의 차이나 식생으로 인해 거동을 이해하는데 많은 제약이 따른다. 본 연구에서는 고해상도의 영상을 제공하는 Landsat 위성이 기반이 되는 원격탐사 기반 에너지 수지 모형인 Mapping EvapoTRanspiration with Internalized Calibration (METRIC) 모형과 MODerate resolution Imaging Spectroradiometer (MODIS) 위성 기반의 Penman-Monteith 알고리즘으로 산정된 증발산의 공간 분포를 비교하였다. 토지 피복별로 분류한 후 두 공간 분포를 비교하여 침엽수림과 활엽수림에서 가장 높은 상관관계를 갖는 것을 확인하였고 두 모형에 대한 적용성이 높음을 알 수 있다. 본 연구를 바탕으로 원격탐사 기반 고해상도 증발산 지도를 제작하여 시공간적 변동성과 계절 변화에 따른 거동을 파악할 수 있을 것이다.

Keywords

References

  1. 기상청 홈페이지 (http://www.kma.go.kr/)
  2. 박종윤, 이미선, 이용준, 김성준. (2008) SWAT 모형을 이용한 미래 토지이용변화가 수문 - 수질에 미치는 영향 분석, 대한토목학회논문집. 대한토목학회. 28(2B): 187-197.
  3. 최민하, 황교택, 김태웅. (2011) Landsat 인공위성 이미지를 이용한 경안천 유역 증발산의 생장기와 휴면기 분포 특성 분석, 대한토목학회논문집. 대한토목학회. 31(1B): 29-36.
  4. Allen, R.G., Pruitt, W.O., Businger, J.A., Fritschen, L.J., Jensen, M.E., and Quinn, F.H. (1996) Evaporation and transpiration. Hydrology Handbook, Edited by wootton et al., American Society of Civil Engineers, New York, NY, pp. 125-252.
  5. Allen, R.G., Tasumi, M., and Trezza, R. (2007a) Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)-Model. Journal of Irrigation and Drainage Engineering, American Society of Civil Engineers, Vol. 133, No. 4, pp. 380-394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)
  6. Allen, R.G., Tasumi, M., Morse, A., Trezza, R., Wright, J.I., Bastiaanssen, W., Kramber, W., Lorite, I., and Robison, C.W. (2007b) Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)- Applications. Journal of Irrigation and Drainage Engineering, American Society of Civil Engineers, Vol. 133, No. 4, pp. 395-406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395)
  7. 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. Ph.D. dissertation, CIP Data Koninklijke Bibliotheek, Den Haag, the Netherlands.
  8. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., and Holtslag, A.A.M. (1998) A remote sensing surface energy balance algorithm for land (SEBAL) : 1. Formulation. Journal of Hydrology, Vol. 212-213, pp. 198-212. https://doi.org/10.1016/S0022-1694(98)00253-4
  9. Bastiaanssen, W.G.M., Noordman, E.J.M., Pelgrum, H., Davids, G., and Allen, R.G. (2005) SEBAL for spatially distributed ET under actual management and growing conditions, ASCE Journal of Irrigation and Drainage Engineering, Vol. 131, No. 1, pp. 85-93. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:1(85)
  10. Choi, M., Kim, T., Park, M., Kim, S. (2009) Evapotranspiration estimates using the Landsat-5 Thematic Mapper image over Gyungan watershed in Korea, International Jounal of Remote Sensing, Vol. 32, No. 15, pp.4327-4341.
  11. Choi, M., Kustas, W.P., Anderson, M.C., Allen, R.G., Li, F., and Kjaersgaard, J.H. (2009) An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX. Agricultural and Forest Meteorology, Vol. 149, pp. 2082-2097. https://doi.org/10.1016/j.agrformet.2009.07.002
  12. Cleugh, H.A., Leuning, R., Mu, Q., and Running, S.W. (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, Vol. 106, pp. 285-304. https://doi.org/10.1016/j.rse.2006.07.007
  13. Jiang, L., and Islam, S. (2001) Estimation of surface evaporation map over Southern Great Plains using remote sensing data. Water Resources Research, Vol. 37, No. 2, pp. 329-340. https://doi.org/10.1029/2000WR900255
  14. Karma, J.D., McVicar, T.R., and McCabe, M.F. (2008) Estimating land surface evaporation : A review of methods using remotely sensed surface temperature data. Surveys in Geophysics, Vol. 29, pp. 421-469. https://doi.org/10.1007/s10712-008-9037-z
  15. Kim, H., Hwang, K., Mu, Q., Lee, S., and Choi, M. (2011) Validation of MODIS 16 global terrestrial ET product in various climate and land cover types in Asia. KSCE Journal of Civil Engineering, (Accepted).
  16. Kramer, H.J. (1994) Observation of the Earth and Its Environment: Survey of Missions and Sensors. Springer, 4th edition, pp. 33.
  17. Kustas, W.P., Anderson, M.C., Norman, J.M., and Li, F. (2007) Utility of radiometricaerodynamic temperature relations for heat flux estimation. Boundary- Layer Meteorology, Vol. 122, pp. 167-187. https://doi.org/10.1007/s10546-006-9093-1
  18. Markham, B.L., Storey, J.C., Williams, D.L., and Irons, J.R. (2004) Landsat Sensor Performance: history and Current Status. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 12, pp. 2691-2694. https://doi.org/10.1109/TGRS.2004.840720
  19. McCabe, M.F., and Wood, E.F. (2006) Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sensing of Environment, Vol. 105, No. 4, pp. 271-285. https://doi.org/10.1016/j.rse.2006.07.006
  20. Mu, Q., Heinsch, F.A., Zhao, M., and Running, S.W. (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, Vol. 111, pp. 519-536. https://doi.org/10.1016/j.rse.2007.04.015
  21. NASA, 1999, The Earth Science Enterprise website (http://www.earth.nasa.gov/)
  22. Timmermans, W., Kustas, W.P., Anderson, M.C., and French, A.N. (2007) An intercomparison of the surface energy balance algorithm for land (SEBAL) and the two-source energy balance (TSEB) modeling schemes. Remote Sensing of Environment, Vol. 108, pp. 369-384. https://doi.org/10.1016/j.rse.2006.11.028
  23. United States Geological Survey (USGS) website (http://www.usgs.gov/)