A two-layer model based on a representation of the soil-canopy-atmosphere was developed and used to estimate sensible heat flux in a sparse, arid region (the Goshute Valley, Nevada, USA). In the model, the spatial distribution of the aerodynamic resistances of the different vegetation and soil types is accounted for using the height, specific area, temperature, and emissivity of vegetation and soil classes. Surface temperatures were mapped using an airborne thermal scanner. Calibrated high-resolution multispectral video imagery (pixel size or 0.15 m) acquired from an aircraft was used to extract different vegetation and soil classes using the supervised classification scheme. Measurements of sensible heat flux (H) were made simultaneously using eddy covariance and Bowen ratio techniques with towers set-up in different vegetation types and density. Model simulations indicate that estimated values of sensible heat flux agreed well with observed H, and that deviations between observed versus modelled H were generally less than the expected measurement error of H at the ground-based flux stations. The model sensitivity to the soil resistance, ras, and soil temperature variations, canopy resistance, rac, canopy temperature, Tc, leaf area index (LAI), roughness coefficient (z om), and the wind speed (U2) was estimated. In addition, the model was tested for a condition with no vegetation, assuming T SFC was all due to soil. The good agreement between the observed and estimated sensible heat flux suggests that local-scale sensible heat flux maps can be produced using airborne sensors and utilized in mesoscale atmospheric models.