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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX

Authors
item Choi, Minha -
item Kustas, William
item Anderson, Martha
item Allen, Richard -
item Li, Fuqin -
item Kjaersgaard, Jeppe -

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 17, 2009
Publication Date: December 4, 2009
Citation: Choi, M., Kustas, W.P., Anderson, M.C., Allen, R.G., Li, F., Kjaersgaard, J.H. 2009. An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX. Agricultural and Forest Meteorology. 149:2082-2097.

Interpretive Summary: Reliable estimation of the surface energy balance from field to farm, and from watershed to regional scales is critical in many applications related to weather forecasting, irrigation scheduling and water resource management and climate modeling. Remote sensing-based methods have been developed in order to provide a way to map spatially-distributed energy fluxes over a landscape. The purpose of this study was to compare energy flux maps using three models having operational capabilities over a corn and soybean production region in central Iowa containing a network of ground truth energy flux towers. The models include the Two-Source Energy Balance (TSEB) model, the Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC), and the Trapezoid Interpolation Model (TIM). These three models have different levels of complexity in modeling soil-plant-atmosphere energy exchange and methodologies for defining key model variables. The patterns of the heat fluxes and evapotranspiration (ET) using satellite data are compared to the tower network and consistency in the ET patterns from the three models over the region is assessed. It is found that there are significant discrepancies with tower measurements in the output of heat flux and ET from TIM, while TSEB and METRIC yield satisfactory agreement. However, there are also significant differences in model output found between TSEB and METRIC when comparing heat flux and ET patterns over the region, particularly in regions under partial vegetation cover conditions. These discrepancies between TSEB and METRIC appear to be largely due to the internal calibration procedure in METRIC that assumes that both wet and dry extremes exist within a satellite scene even for rain fed agricultural landscapes. More of these intercomparison studies are planned and should ultimately lead to improvements in the algorithms used by the various models when applied to different landscapes and climatic zones. They can also provide an opportunity for incorporating the strengths of the different approaches in the development of a hybrid remote sensing-based energy balance/ET model with significantly greater utility.

Technical Abstract: Reliable estimation of the surface energy balance from local to regional scales is crucial for many applications including weather forecasting, hydrologic modeling, irrigation scheduling, water resource management, and climate change research, just to name a few. Numerous models have been developed using remote sensing, which permits mapping of the surface energy balance in a spatially-distributed manner over large areas. This study compares flux maps generated over a relatively simple agricultural landscape in central Iowa comprised of soybean and corn fields with three different remote sensing-based surface energy balance models: the Two-Source Energy Balance (TSEB) model, Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC), and the Trapezoid Interpolation Model (TIM). The three models have different levels of complexity and input requirements, but all have operational capabilities. METRIC and TIM make use of the remotely sensed surface temperature-vegetation cover relation to define key model variables linked to wet and dry hydrologic extremes, while TSEB uses these remotely sensed inputs to define component soil and canopy temperatures and aerodynamic resistances. The models were run using Landsat 5 and 7 imagerycollected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX) in 2002 and model results were compared with observations from a network of 10 flux towers deployed within the study area. There was reasonable agreement between modeled and measured net radiation (RN) and soil heat flux (G) with relatively low root-mean-square-error (RMSE ~20–30 W/m2). However, more significant discrepancies were observed for the turbulent heat fluxes of sensible heat (H) and latent heat (LE), with RMSE values reaching nearly 150 W/m2. While TSEB and METRIC yielded similar and reasonable agreement with measured heat fluxes (RMSE ~50-75 W/m2), TIM showed generally poor agreement with RMSE > 100 W/m2. Despite the good agreement between TSEB and METRIC at the specific tower locations, a spatial intercomparison of gridded model output (i.e., comparing output on a pixel-by-pixel basis) revealed significant discrepancies in modeled turbulent heat flux patterns that were largely correlated with vegetation density. Generally, the largest discrepancies, primarily a bias in H, between these two models occurred in areas with partial vegetation cover and a leaf area index (LAI) < 2.0. A significant reduction in the bias in H between METRIC and TSEB was achieved by adjusting the minimum LE assumed for the hot/dry hydrologic extreme condition in METRIC. However, this caused a significant increase in bias in LE between the models, suggesting care must be taken when applying models requiring wet and dry extremes over uniformly vegetated landscapes. Clearly, a model intercomparison of the flux patterns on a pixel-by-pixel basis produced over a landscape is required in order to assess model uncertainty in surface energy balance estimation through remote sensing.

Last Modified: 7/24/2014
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