Wang, KC, Liang, SL (2008). "An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture". JOURNAL OF HYDROMETEOROLOGY, 9(4), 712-727.
A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (R-n), a vegetation index, and daytime-averaged/daily maximum air temperature (T-a) or land surface temperature (T-s) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal T-s range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001-06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of R-n, enhanced vegetation index (EVI), daily maximum T-s, and DTsR is about 0.92 for all the sites, the bias is-1.9 W m(-2), and the root-mean-square error (RMSE) is 28.6 W m(-2). The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal T-a range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1 degrees x 1 degrees from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m(-2), the RMSE is 19.8 W m(-2), and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.