Many applications exist within the fields of agriculture, forestry, land management, and hydrologic assessment for routine estimation of surface energy fluxes, particularly evapotranspiration (ET), at spatial resolutions of the order of 101 m. A new two-step approach (called the disaggregated atmosphere land exchange inverse model, or DisALEXI) has been developed to combine low- and high-resolution remote sensing data to estimate ET on the 101--102 m scale without requiring any local observations. The first step uses surface brightness-temperature-change measurements made over a 4-hour morning interval from the GOES satellite to estimate average surface fluxes on the scale of about 5 km with an algorithm known as ALEXI. The second step disaggregates the GOES 5-km surface flux estimates by using high-spatial-resolution images of vegetation index and surface temperature, such as from ASTER, Landsat, MODIS, or aircraft, to produce high-spatial-resolution maps of surface fluxes. Using data from the Southern Great Plains field experiment of 1997, the root-mean-square difference between remote estimates of surface fluxes and ground-based measurements is about 40 W m-2, comparable to uncertainties associated with micrometeorological surface flux measurement techniques. The DisALEXI approach was useful for estimating field-scale, surface energy fluxes in a heterogeneous area of central Oklahoma without using any local observations, thus providing a means for scaling kilometer-scale flux estimates down to a surface flux-tower footprint. Although the DisALEXI approach is promising for general applicability, further tests with varying surface conditions are necessary to establish greater confidence.