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Estimation of surface evaporation map over Southern Great Plains using remote sensing data
Author(s) -
Jiang Le,
Islam Shafiqul
Publication year - 2001
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2000wr900255
Subject(s) - evaporation , remote sensing , potential evaporation , environmental science , advanced very high resolution radiometer , flux (metallurgy) , surface (topology) , vegetation (pathology) , residual , radiometer , meteorology , geology , computer science , algorithm , geography , mathematics , engineering , medicine , materials science , satellite , geometry , pathology , aerospace engineering , metallurgy
We propose a simple scheme to estimate surface evaporation over large heterogeneous areas using remote sensing data. Our approach is based on an extension of the Priestley‐Taylor equation and a relationship between remotely sensed surface temperature and vegetation index. The required parameters for this approach are derived from advanced very high resolution radiometer NOAA 14 data over the southern Great Plains (SGP) in 1997. Compared to ground flux observations by the Atmospheric Radiation Measurement program, our six case studies varying from early spring to late summer over the SGP domain show that the proposed method provides better estimation accuracy for surface evaporation than the original Priestley‐Taylor method. It appears that the proposed method can estimate evaporation over large areas with significantly less complexity than that of the traditionally used residual method based on aerodynamic resistance. The uncertainty in the estimation of surface evaporation for the proposed approach is closely related to the inaccuracy in deriving net radiation and soil heat flux from remote sensing data. Results suggest that the proposed approach is more reliable and easily applicable for operational estimation of evaporation over large areas where ground‐based data are not readily available.