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Surface energy fluxes over El Reno, Oklahoma, using high‐resolution remotely sensed data

Accurate estimation of spatial distributions of evapotranspiration (ET) is a goal sought by hydrologists, agronomists, and meteorologists but is difficult to achieve. The usual approaches to estimating ET employ remote sensing observations and a surface energy flux model. However, resolution of remote sensing data, needed to observe patterns of biophysical variables, is commonly too coarse (>1 km) to distinguish between land cover types that constrain ET. Accuracy of ET estimates can be improved by using higher‐resolution (<100 m) remote sensing data since they can distinguish clusters of vegetation from bare soil fields and water bodies. A demonstration of this potential is shown using aircraft‐based remote sensing observations over a study site at El Reno, Oklahoma. Five midday surveys, conducted from 29 June to 2 July 1997, as part of the Southern Great Plains 1997 Experiment (SGP97), collected 12 m resolution images in the visible, near infrared, and thermal infrared. Surface temperature and vegetation density maps, created from these surveys, were combined with surface micrometeorological observations and with a two source energy balance model. Results from El Reno show that flux estimates with respect to ground‐based eddy covariance observations can be accurate to within 40–80 W m−2. This means that the high spatial resolution observations can potentially produce ET estimates similar in quality to ground‐based point measurements. Additional work, needed to show how high‐resolution remote sensing estimates can be related to coarser resolution observations, is underway using the satellite sensors ASTER (15–90 m resolution) and MODIS (250 m to 1 km resolution).