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Accurate estimation of spatial crop water consumption is needed to increase crop yield, save water and protect soils from degradation, as well as to protect ground water quality through the optimization of fertilizer intake by the crop. Air temperature is a fundamental climate variable, used in models to estimate reference evapotranspiration (ETo). If air temperature varies considerably in space, then wrong ETo predictions can be made for an irrigation district when using data from a single weather station. In this paper, spatial apparent air temperatures were obtained using remotely sensed inputs and ground measurements. The USU airborne video/radiometer multispectral remote sensing system and an Inframetrics 760 scanner were used to obtain high resolution remotely sensed imagery. Data were acquired in two irrigation district locations. One site was located in Smithfield (Utah), and the other in Maricopa (Arizona). A Bowen ratio energy balance system (BR) was located on a wheat field (Smithfield) and an eddy correlation (EC) system on a cotton field (Maricopa). The remote sensing energy fluxes closely matched the measured fluxes, predicting the correct air temperature for the system locations. In both cases the air temperature registered by the energy flux systems, at the time of the flight overpass, was only representative for half of the field. The wheat field with an area of 2.2 ha showed an air temperature variation (.T) of 3 C, and the cotton field with 5.3 ha showed a .T of 6 C. Thus, estimating crop water demand, based on a single weather station and using the reference evapotranspiration method, could overestimate (for some parts of the fields) and underestimate (for other parts) the spatial crops water needs. The DT methodology of SEBAL was tested with the imagery. The results indicate that the coldest pixel has to be substituted by the average crop canopy temperature for the field (irrigated) when computing DT. In light of these findings, it is suggested that accurate spatial estimation of the different variables involved in computing ETo should be investigated through remote sensing and GIS techniques. Widely used ETo equations like the FAO Penman-Monteith and Hargreaves 1985 can be utilized for spatial predictions once their variables have been validated. These variables are Tmax, Tmin (daily), Rn (daily net radiation), G (daily soil heat flux), U (average daily wind velocity spatial distribution), etc. In the case of the energy fluxes, methodologies are needed to accurately integrate the one time (snapshot) remote sensing values to daily values.