Satellite estimates of evapotranspiration on the 100-m pixel scale

Estimating crop evapotranspiration (Et) from satellite observations has proven to be a challenging task, because the single `snapshot' images routinely obtained from high-spatial-resolution satellites do not provide enough temporal information. A new two-step approach (called disaggregated-ALEXI or DisALEXI) has been developed to combine the high temporal resolution of GOES with the high spatial resolution of Landsat to estimate crop Et on the 20-100-m scale without using any local observations. The first step uses surface brightness-temperature-change measurements about four hours apart in the morning from the GOES satellite to estimate average Et on the scale of about 5 km with an algorithm known as ALEXI. The second step disaggregates the GOES 5-km Et estimates by using high-spatial-resolution images of vegetation-index and surface temperature, such as from aircraft or Landsat, to produce 30-m maps of crop Et. Preliminary evaluations suggest that crop remote Et estimates agree within 15 to 50% of surface measurements, where the 50% error is on a small flux (150 W m^-2). The DisALEXI approach will be useful for adjusting parameters or updating calibrations of high-spatial-resolution models used for management decisions in precision farming, because no local measurements are necessary, and spatial patterns will be determined with high precision even if the absolute accuracy may only be 30%.