Remote sensing of surface energy fluxes at 101-m pixel resolutions -: art. no. 1221

[1] 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 10(1) 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 10(1) - 10(2) 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.