Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2013
Publication Date: 8/1/2013
Citation: Gowda, P., Howell, T.A., Paul, G., Colaizzi, P.D., Marek, T.H., Su, B., Copeland, K.S. 2013. Deriving hourly evapotranspiration (ET) rates with SEBS: A lysimetric evaluation. Vadose Zone Journal. 12(3):1-11. Interpretive Summary: Surface Energy Balance System (SEBS) is one of the major energy balance methods for deriving evapotranspiration (ET) rates from remote sensing data. It requires minimal amount of ancillary data for deriving ET. However, the SEBS has never been evaluated for its ability to estimate ET using lysimetric measurements. In this study, we evaluated the SEBS for estimating ET using Landsat 5 satellite data for summer crops in the Southern High Plains. Estimated hourly ET rates were compared against measured data from four large weighing lysimeters maintained by the USDA-ARS Conservation and Production Laboratory in Bushland, Texas. Performance statistics indicated that SEBS performance was excellent in estimating hourly ET in the Southern High Plains.
Technical Abstract: Numerous energy balance (EB) algorithms have been developed to use remote sensing data for mapping evapotranspiration (ET) on a regional basis. Adopting any single or combination of these models for an operational ET remote sensing program requires a thorough evaluation. The Surface Energy Balance System (SEBS) was evaluated for its ability to estimate hourly ET rates of summer tall and short crops grown in the Texas High Plains using fifteen Landsat 5 Thematic Mapper scenes acquired during 2006-2009. Performance of SEBS was evaluated by comparing estimated hourly ET values with measured ET data from four large weighing lysimeters each located at the center of a 4.3 ha field in the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. The performance of SEBS in estimating hourly ET was excellent for each crop type evaluated under both irrigated and dryland conditions. A locally derived, surface albedo-based soil heat flux (G) model further improved the SEBS performance through improved G estimates. Root mean square error and mean bias error were 0.11 and -0.005 mm h-1, respectively, and the Nash-Sutcliff model efficiency was 0.85 between the measured and calculated hourly ET. Considering the excellent performance with a minimal amount of ancillary data as compared to with other EB algorithms, SEBS is a promising tool for use in an operational ET remote sensing program in the semi-arid Texas High Plains. However, a thorough sensitivity and error propagation analyses of input variables to quantify their impact on ET estimations for the major crops in the Texas High Plains under different agro-climatological conditions are needed before adopting the SEBS into operational ET remote sensing programs for irrigation scheduling or other purposes.