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United States Department of Agriculture

Agricultural Research Service

Title: Comparison/Validation of Remote Sensing-Based Surface Energy Balance Models Over the Agricultural Landscapes

item Choi, Minha
item Kustas, William - Bill
item Anderson, Martha

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/22/2008
Publication Date: 4/22/2008
Citation: Choi, M., Kustas, W.P., Anderson, M.C. 2008. Comparison/validation of remote sensing-based surface energy balance models over the agricultural landscapes [abstract]. Abs. 8, BARC Poster Day.

Interpretive Summary:

Technical Abstract: Accurate characterization of surface energy fluxes over a range of spatial and temporal scales is critical for many applications in agriculture, hydrology, meteorology, and climatology. Over the past several years, there has been a major effort devoted to the development and refinement of remote sensing-based energy balance models that provide spatially-distributed ET maps operationally using satellite data. Validation of the surface energy flux maps is typically performed using a handful of tower-based flux observations, and hence little is known about the reliability of the maps for the majority of the scene. Very few studies have attempted to inter-compare energy balance models over the same agricultural site in order to quantify and identify the possible uncertainty in surface energy flux estimation using different modeling approaches. In this study, several remote-sensing based energy balance models with Land Surface Temperature (LST) as a key boundary condition and having different algorithm complexity are evaluated using remote sensing imagery and ground-truth data from the 2002 Soil Moisture/ Atmosphere Coupling EXperiment (SMEX02/SMACEX) in Iowa, U. S. The SMACEX was designed to provide a variety of observations of vegetation, soil moisture, and atmospheric conditions under a wide range of temporal and spatial scales. Through such inter-comparison and validation, model differences associated with algorithms and model complexity, sensitivity to uncertainty in model inputs, and other factors will be presented and each model’s strengths and limitations will be described.

Last Modified: 08/21/2017
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