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Title: SATELLITE ESTIMATES OF EVAPOTRANSPIRATION ON THE 100M PIXEL SCALE

Author
item NORMAN, JOHN - UNIV OF WI, MADISON, WI
item DANIEL, LEANN - UNIV OF WI, MADISON, WI
item DIAK, GEORGE - UNIV OF WI, MADISON, WI
item TWINE, TRACEY - UNIV OF WI, MADISON, WI
item Kustas, William - Bill
item French, Andrew
item Schmugge, Thomas

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/1/2000
Publication Date: 7/24/2000
Citation: N/A

Interpretive Summary: Evapotranspiration (Et) is one of the most important fluxes to monitor if we are interested in the productivity of vegetation. The relationship between transpiration and productivity is so conservative for most vegetation that one can be used to estimate the other. In site-specific farming, the spatial distribution of productivity or yield is of paramount importance, and yield monitors are being used increasingly to characterize the spatial distribution of productivity. Therefore, monitoring transpiration on a regular basis should provide information about seasonable yield expectations long before the final yield is attained. 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 Geostationary Operational Environmental Satellite (GOES) with the high spatial resolution of Landsat satellite to estimate crop Et on the 20-100m scale without using any local observations. Preliminary evaluations measurements, where the 50% error is for bare soil under relatively low Et.

Technical Abstract: 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 20-100m 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 5km Et estimates by using high-spatial-resolution images of vegetation-index and surface temperature, such as from aircraft or Landsat, to produce 30m 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%.