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Title: SURFACE ENERGY FLUXES OVER EL RENO, OKLAHOMA USING HIGH RESOLUTION REMOTELY SENSED DATA

Author
item FRENCH, ANDREW - NASA/GSFC
item Schmugge, Thomas
item Kustas, William - Bill
item BRUBAKER, KAYE - UNIVERISTY OF MD
item Prueger, John

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/29/2003
Publication Date: 6/24/2003
Citation: French, A.N., Schmugge, T.J., Kustas, W.P., Brubaker, K.L., Prueger, J.H.2003. Surface energy fluxes over El Reno, Oklahoma using high resolution remotely sensed data. Water Resources Research. 39(6):1164, doi:10.1029/2002WR001734.

Interpretive Summary: Mapping the spatial distribution of evapotranspiration (ET) over heterogeneous landscapes is a goal sought by hydrologists, agronomists, and meteorologists alike. ET is an important boundary condition for soil moisture modeling, estimation of vegetation health and modeling the surface boundary layer. But creating accurate ET maps remains elusive because of complex surface processes and the inability to retrieve good estimates of all the physically important parameters. One way to improve ET mapping accuracy at all scales is to observe land surfaces at higher resolutions, <100m or finer using remote sensing. Only at higher resolutions can remote sensing data potentially distinguish between dominant land surface types, such as clusters of vegetation, bare soil, and water bodies, and thereby derive plausible values for surface properties, such as aerodynamic roughness. Remotely sensed thermal infrared data from an aircraft platform have been successfully applied to this problem. The data were acquired over the USDA/ARS Grazing lands research station in El Reno OK.

Technical Abstract: 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. But resolution of remote sensing data, needed to observe patterns of biophysical variables, is commonly too coarse (>1km) to distinguish between land cover types that constrain ET. Accuracy of ET estimates can be improved by using higher resolution (<100m)remote sensing data, since it 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 mid-day surveys, conducted from 29 June to 2 July 1997, as part of the Southern Great Plains 1997 Experiment (SGP97), collected 12m resolution images in the visible, near infrared and thermal infrared. Surface temperature and vegetation density maps, created from these surveys, were combined with surface micro-meteorological 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 40-80W m2. 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 (15-90m resolution) and MODIS (250m-1km resolution).