Korean Journal of Remote Sensing, Vol.28, No.4, 2012, pp.435~448 http://dx.doi.org/10.7780/kjrs.2012.28.4.7 Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method Chanyang Sur*, Seungjae Han*, Junghoon Lee** and Minha Choi* *Department of Civil engineering, Hanyang University, **Hydrological Survey Center Abstract : Evapotranspiration (ET) including evaporation from a land surface and transpiration from photosynthesis of vegetation is a sensitive hydrological factor with outer circumstances. Though both direct measurements with an evaporation pan and a lysimeter, and empirical methods using eddy covariance technique and the Bowen ratio have been widely used to observe ET accurately, they have a limitation that the observation can stand for the exact site, not for an area. In this study, remote sensing technique is adopted to compensate the limitation of ground observation using the Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral sensor mounted on Terra satellite. We improved to evapotranspiration model based on remote sensing (Mu et al., 2007) and estimated Penman-Monteith evapotranspiration considering regional characteristics of Korea that was using only MODIS product. We validated evapotranspiration of Sulma (SMK)/Cheongmi (CFK) flux tower observation and calculation. The results showed high correlation coefficient as 0.69 and 0.74. Key Words : Evapotranspiration, Eddy covariance technique, MODIS, Penman-Monteith algorithm, RS-PM, Remote Sensing 435
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 436
Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method 437
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 (a) (b) Fig. 1. Geographical location at study sites. (a) Seolmacheon b) Cheongmicheon). Table 1. Site description Site ID SMK CFK Site name Seolmacheon Site Cheongmicheon Farmland Site Longitude 126 57 17 E 127 39 10 E Latitude 37 56 20 E 37 9 35 N Altitude 293 m 141 m Annual temperature 11.5 C 11.5 C Annual precipitation 1332 mm 1107 mm Landuse Mixed forest Rice paddy Dominant Species Q. variaabilis, Q. mongolica, Q. serrata, P. koraiensis Oryza sativa Soil type Sandy loam Silty loam to loam 438
Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method Table 2. MODIS products (http://modis.gsfc.nasa.gov/) Product Type Product ID Dataset Atmosphere MOD07_L2 Atmospheric Profiles MOD11A1 Land Surface Temperature (LST) & Emissivity MCD12Q1 Land Cover Type Land MOD13A2 Gridded Vegetation Indices MOD15A2 Leaf Area Index (LAI) & Fraction of Photosynthetically Active Radiation (FPAR) MCD43B3 Broad-band Surface Reflectance (Surface Albedo) R N =(1 _ a) R sd + R ld _ Rlu (1) R N a R sd R ld R lu a R sd = G sc cosq d r t sw (2) G sc q d r t sw t sw _ t sw = 0.35 + 0.627exp _ [ 0.00146Pa 0.075 ( W ) 0.4] (3) K t cosq cosq P a W K t K t d r d r = 1 + 0.033 cos (DOY(2p/365)) (4) R ld R N R ld 4 R ld = e a s T a (5) 439
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 e a s T a e a e a = 1.24(e a /T a ) 0.14 (6) 4 R lu = se s T s (7) e s T s R N C s = c L m(t min ) m(vpd) (8) C s c L m(t min ) m(vpd) m(t min ) m(vpd) if T min T min.open, m(t min ) = 1.0 T min _ Tmin.close if T min.close < T min < T min.open, m(t min ) = T (9) min.open _ Tmin.close if T min T min.close, m(t min ) = 0.1 if VPD VPD open, m(vpd) = 1.0 VPD close _ VPD if VPD open < VPD < VPD close, m(vpd) = VPD _ (10) close VPD open if VPD VPD close, m(vpd) = 0.1 C s C C C C = C s LAI (11) 440
Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method r NIR _ rred NDVI = r (12) NIR + r red r NIR _ rred EVI = G r (13) NIR + C 1 r red _ C2 r blue + L r NIR r red r blue G C 1 C 2 F c EVI _ EVI min F c = EVI (14) max _ EVImin A c = F c A (15) A SOIL = (1 _ F c ) A (16) A A c A SOIL r tot r s r u r tot = r u + r s (17) r tot r totc r totc r tot = r totc rcorr (18) rcorr = 1.0 0 (19) 273.15 + T 101300 293.15 P ( ) 1.75 r u r a r a r a r c r r r a r C p r r = (20) 4.0 s T 3 r c r r r a = r (21) c + r r la SOIL + rc p (e sat _ e)/ra le SOIL.POT = s + g r tot r r a (22) RH le SOIL =le SOIL.POT ( ) (e sat _ e)/100 100 (23) ΔA c + rc p (e sat _ e)/ra le = Δ + g(1 + r s /r a ) (24) l le e sat e r C p r a g s 441
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 R N _ G = H + LE (25) R n H LE R LE = N _ G (26) 1 + B R N _ G (a) SMK (b) CFK Fig. 2. Time series of Latent Heat Flux(LE_Est: Estimated LE, LE_BR: corrected by Bowen ratio, LE_res: corrected by residual method). 442
Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method (a) (b) Fig. 3. Spatial distribution of evapotranspiration (a) 2010. 01. 14 11:00, b) 2010. 08. 23 11:50). (a) Fig. 4. Relationship between MODIS-based evapotranspiration(latent Heat Flux) and fluxtower measurement evapotranspiration (SMK) (a) Latent Heat Flux, (b) Bowen ratio correction, (c) residual correction). (b) 443
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 Fig. 4. Continued (c) (a) (b) (c) Fig. 5. Relationship between MODIS-based Evapotranspiration(Latent Heat Flux) and fluxtower measurement Evapotranspiration CFK) (a) Latent heat flux, (b) Bowen ratio correction, (c) residual correction). 444
Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method Table 3. Statistics comparing observed and estimated evapotranspiration Min. Value Max. Value Mean Vlaue CV Bias RMSE s CV RMSE (W m -2 ) (W m -2 ) (W m -2 ) (MODIS-Fluxtower LE) MODIS LE 0.27 529.66 167.88 151.02 0.90 SMK Fluxtower LE 0.78 334.97 97.21 80.47 0.83 Fluxtower BR 10.03 627.39 242.68 134.28 0.55 70.67 148.25 Fluxtower res 84.46 564.23 330.98 134.60 0.41 MODIS LE 2.81 468.94 108.09 116.71 1.16 CFK Fluxtower LE 7.36 409.38 157.47 106.10 0.67 Fluxtower BR 53.48 439.41 215.24 120.60 0.56 Fluxtower res 37.44 529.45 271.75 132.71 0.49-125.36 155.10 R N _ G = H + LE R N _ G = H + LE R N G H LE 445
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 MODIS website(http://modis.gsfc.nasa.gov/) NASA, 1999, The Earth Science Enterprise website (http://www.earth.nasa.gov/) United States Geological Survey(USGS) website (http://www.usgs.gov/) Allen, R.G., I.A. Walter, R.L. Elliott, T.A. Howell, D. Itenfisu, M.E. Jensen, and R.L. Snyder, 2005. The ASCE standardized reference evapotranspiration equation, American Society of Civil Engineers, Reston, V.A. Allen, R.G., M. Tasumi, and R. Trezza, 2007a. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model, Journal of Irrigation and Drainage Engineering, American Society of Civil Engineers, 133(4): 380-394. Allen, R.G., M. Tasumi, A. Morse, R. Trezza, J.L. Wright, W. Bastiaanssen, W. Kramber, I. Lorite, and C.W. Robison, 2007b. Satellitebased energy balance for mapping evapotranspiration with internalized calibration (METRIC)-applications, Journal of 446
Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method Irrigation and Drainage Engineering, American Society of Civil Engineers, 133(4): 395-406. Brutsaert, W., 1975. On a derivable formula for longwave radiation from clear skies, Water Resources Research, 11: 742-744. Choi, M., J.M. Jacobs, and W.P. Kustas, 2008. Assessment of clear and cloudy sky parameterizations for daily downwelling longwave radiation over different land surfaces in Florida, USA, Geophysical Research Letters, 35. Choi, M., W.P. Kustas, M.C. Anderson, R.G. Allen, F. Li, and J.H. Kjaersgaard, 2009. An intercomparison of three remote sensingbased surface energy balance algorithms over a corn and soybean production region(iowa, U.S.) during SMACEX, Agricultural and Forest Meteorology, 149(12): 2082-2097. Cleugh, H.A., R. Leuning, Q. Mu, and S.W. Running, 2007. Regional evaporation estimaters from flux tower and MODIS satellite data, Remote Sensing of Environment, 106: 285-304. Dang, Q.L., H.A. Margolis, M.R. Coyea, M. Sy, and G.J. Collatz, 1997. Regulation of branch-level gas exchange of boreal trees: Roles of shoot water potential and vapor pressure difference, Tree Physiology, 17: 521-535. Goulden, M.L., S.C. Wofsy, J.W. Harden, S.E. Trumbore, P.M. Crill, S.T. Gower, T. Fries, B.C. Daube, S.M. Fan, D.J. Sutton, A. Bazzaz, and J.W. Munger, 1998. Sensitivity of boreal forest carbon balance to soil thaw, Science, 279: 214-217. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83: 195-213. Huete, A.R., K. Didan, Y.E. Shimabukuro, P. Ratana, S.R. Saleska, L.R. Hutyra, W. Yang, R.R. Nemani, and R. Myneni, 2006. Amazon rainforests green-up with sunlight in dry season, Geophysical Research Letters, 33. IPCC, 2007. Climate Change 2007: The Physical Scientific Basis, Working Group Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Clamate Change, Summary for Policymakers. Cambridge University Press, Cambridge, UK, pp. 996. Jang, K., S. Kang, H. Kim, and H. Kwon, 2009. Evaluation of shortwave irradiance and evapotranspiration derived from moderate resolution imaging spectroradiometer (MODIS), Asia-Pacific Journal of Atmospheric Sciences, 45: 233-246. Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos, Trans. R. Soc. London, 273: 593-610. Jones, H.G., 1992. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology. Karma, J.D., T.R. McVicar, and M.F. McCabe, 2008. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surveys in Geophysics, 29: 421-469. Kawamitsu, Y., S. Yoda, and W. Agata, 1993. Humidity pretreatment affects the responses of stomata and CO2 assimilation to vapor pressure difference in C3 and C4 plants, Plant and Cell Physiology, 34: 113-119. Kim, J., D. Lee, J. Hong, S. Kang, S.J. Kim, S.K. Moon, J.H. Lim, Y. Son, J. Lee, S. Kim, N. Woo, K. Kim, B. Lee, and B.L. Lee, 2006. HydroKorea and CarboKorea: Cross-scale studies of ecohydrology and biogeochemistry in a heterogeneous and complex forest catchment of Korea, Ecological Research, 21: 881-889. 447
Korean Journal of Remote Sensing, Vol.28, No.4, 2012 Korea Institute of Construction Technology, 2006. Operation and research on the hydrological characteristics of the experimental catchment. Korea Institute of Construction Technology 2006-062, 182. Landsberg, J.J. and S.T. Gower, 1997. Applications of Physiological Ecology to Forest Management. San Diego, CA: Academic Press. Leuning, R., 1995. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants, Plant, Cell and Environment, 18: 339-355. Marsden, B.J., V.J. Lieffers, and J.J. Zwiazek, 1996. The effect of humidity on photosynthesis and water relations of white spruce seedlings during the early establishment phase, Canadian Journal of Forest Research, 26: 1015-1021. Misson, L., J.A. Panek, and A.H. Goldstein, 2004. A comparison of three approaches to modeling leaf gas exchange in annually droughtstressed ponderosa pine forests, Tree Physiology, 24: 529-541. Mu, Q., F.A. Heinsch, M. Zaho, and S.W. Running, 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment, 111: 519-536. Oren, R., J.S. Sperry, G.G. Katul, D.E. Pataki, B.E. Ewers, N. Phillips, and K.V.R. Schafer, 1999. Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit, Plant, Cell and Environment, 22: 1515-1526. Sandford, A.P., and P.G. Jarvis, 1986. Stomatal responses to humidity in selected conifers, Tree Physiology, 2: 89-103. Schulze, E.D., F.M. Kelliher, C. Korner, J. Lloyd, and R. Leuning, 1994. Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rate, and plant nitrogen nutrition: A global ecology scaling exercise, Annual Review of Ecology and Systematics, 25: 629-660. Sumner, D.M. and J.M. Jacobs, 2005. Utility of Penman-Monteith, Priestley-Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration, Journal of Hydrology, 308:81-104. Twine, T.E., W.P. Kustas, J.M. Norman, D.R. Cook, P.R. Houser, T.P. Meyers, J.H. Prueger, P.J. Starks, and M.L. Wesely, 2000. Correcting eddy-covariance flux underestimates over grassland, Agricultural Forest and Meteorology, 103: 279-300. Van De Griend, A.A. and M. Owe, 1994. Bare soil surface resistance to evaporation by vapor diffusion under semiarid conditions, Water Resources Research, 30: 181-188. Wallace J.S. and C.J. Holwill, 1997. Soil evaporation from tiger-bush in south-west Niger, Journal of Hydrology, 188-189: 443-452. Xu, L. and D. Baldocchi, 2002. Seasonal variations in Carbon, water and energy fluxes in an Oak/Grass Savanna and in Photosynthetic Capacity of Oak Leaf in California, American Geophysical Union, Fall Meeting 2002. 448