One of the research goals of the Argonne Boundary Layer Experiments effort at the Walnut River Watershed in Kansas is to improve, test, and implement an operational modeling system that estimates long-term evapotranspiration by coupling satellite remote sensing data with limited surface measurements and by modeling subgrid- or pixel-scale surface energy and water fluxes. The functional relationships involving the satellite data, surface parameters, and associated subgrid-scale flux modeling are described by the PASS (parameterization of subgrid-scale surface fluxes) model. The advantage of this modeling system is that it can make effective use of satellite remote sensing data and can be run for large areas for which flux data do not exist and surface meteorological data are available from only a limited number of ground stations. In this study, the normalized difference vegetation index (NDVI) and surface brightness temperature at each pixel for the Watershed were derived from NOAA-12 and NOAA-14 satellite data and were subjected to atmospheric corrections for five intensive operational days of the 1997 Cooperative Atmosphere-Surface Exchange Study (CASES) experiment. Combining the derived satellite subgrid-scale data with incoming solar irradiance, air temperature, relative humidity, and wind speed measured at the surface grid scale allowed the extractable root zone soil moisture at the subgrid scale to be estimated at the satellite passing time. Diurnal surface energy flux and evapotranspiration were then simulated from the derived subgrid-scale soil moisture and observational data on diurnal incoming solar irradiance, air temperature, relative humidity, and wind speed. In comparison with results from surface flux measurement sites operated by the National Center for Atmospheric Research, the spatial variations were not always well represented initially by the current model. The parameterization schemes currently implemented in PASS to relate NDVI values to roughness length, albedo, and surface conductance can be improved by adding land use information. Improvements in the PASS model can be evaluated by the 1997 CASES experiments and similar efforts