Satellite Remote Sensing Based Estimation of Land Surface Evapotranspiration in Great Plains

The objective of this study was to assess the operational characteristics and performance of the Surface Energy Balance Algorithm for Land (SEBAL) model for estimating actual ET (ET_c) and other energy balance components, and mapping spatial distribution and seasonal variation of actual ET on a large scale in Great Plains climatic conditions. A total of seven cloud free Landsat images from May 2005 through October 2005 were processed to estimate the various components of energy balance equation and to determine the ET for the part of the Great Plains in the states of Nebraska and Kansas. The predictions from SEBAL model were compared with the Bowen Ratio Energy Balance System (BREBS) measured fluxes. The ET_c maps generated by model showed a very good progression of ET_c with time during the growing season in 2005 as the surface conditions continuously changed. The predictions for some surface energy fluxes were very good. SEBAL overestimated the soil heat flux as compared to BREBS. Local calibration of this flux significantly improved the predicted values. Overall, a very good correlation was found between the BREBS measured and SEBAL estimated ET_c for all images analyzed with a good r² of 0.73 and a low RMSD of 1 mm d^–1. The model was able to predict growing season cumulative daily corn ET reasonably well within 5% of measured values. While our results showed that SEBAL can be a viable tool to assess and quantify spatiotemporal distribution of ET on large scales as well as estimating surface energy fluxes, its operational assessment for estimating sensible heat flux and ET_c, especially during the drier periods needs further investigation.