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Evaluating the SEBS‐estimated evaporative fraction from MODIS data for a complex underlying surface
Author(s) -
Lu Jing,
Li ZhaoLiang,
Tang Ronglin,
Tang BoHui,
Wu Hua,
Yang Fengting,
Labed Jelila,
Zhou Guoqing
Publication year - 2012
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.9440
Subject(s) - evapotranspiration , energy balance , mean squared error , environmental science , sensible heat , available energy , evergreen , flux (metallurgy) , residual , remote sensing , atmospheric sciences , energy (signal processing) , mathematics , geology , statistics , algorithm , physics , materials science , ecology , botany , metallurgy , biology , thermodynamics
With the complex nature of land surfaces, more attention should be paid to the performance of remotely sensed models to estimate evapotranspiration from moderate and low spatial resolution data. Taking into account the characteristic of a stable evaporative fraction (EF) in the daytime, this paper uses the surface energy balance system (SEBS) to estimate the EF from MODIS data for a subtropical evergreen coniferous plantation in southern China and evaluates the stability of the SEBS model in estimating the EF under complex surface conditions. The results show that the SEBS‐estimated EF is larger than the measured EF partly because of the serious lack of energy‐balance closure. This difference can be largely reduced by the residual energy correction method. More evaporative land cover within the MODIS pixel is a main reason for the overestimated EF. SEBS underestimates sensible heat flux, and the underestimation of surface available energy also contributes to the overestimation of the EF. The EF estimated from MODIS/Terra data is in agreement with that from MODIS/Aqua data with a coefficient of determination ( R 2 ) of 0.552, a mean bias error (BIAS) of 0.028, and a root mean square error (RMSE) of 0.079, which is consistent with the result from in situ measurements. In addition, the estimation of surface available energy from remotely sensed data is evaluated on this complex underlying surface. Compared with in situ measurements, the available energy is underestimated by 28 W m −2 with an RMSE = 50 W m −2 and an R 2 = 0.87. Copyright © 2012 John Wiley & Sons, Ltd.