Land surface fluxes have been estimated from remotely sensed data at high pixel resolutions (approximately 60 m) with reasonable accuracy when compared to ground measurements (French et al., 2003; Kustas and Norman, 1999). The remote sensing input used to model land surface fluxes may consist of land surface temperature and a vegetation index. Remotely sensed land surface temperature estimates are strongly affected by atmospheric effects and the generally unknown land surface emissivity. These effects mean that land surface temperature estimates can vary by 1-3 degrees. Similarly, the visible and infra-red bands that are used to calculate vegetation indices are affected by the atmosphere and radiance data should be corrected before calculating such indices. This study investigates the impacts on modelled land surface fluxes of using atmospherically corrected and uncorrected remote sensing input data in two different energy balance models. Two energy balance models were selected for testing. The first (SEBAL) is a one-source model that calculates the sensible heat flux, net radiation and soil heat flux at each remote sensing pixel and estimates the latent heat flux as the residual term in the energy balance. The second (TSM) is a two-source model that uses a vegetation index to partition the land surface temperature between the vegetation and soil at each pixel and then evaluates the energy balance separately over the two land surface components. Land surface temperature was estimated from both atmospherically corrected and uncorrected Landsat ETM+ band 6 data. Atmospherically corrected and uncorrected reflectance in the red and near-infrared bands was used to calculate a vegetation index. These remotely sensed data were used as input in the two different models to estimate the energy balance components at the land surface. The resulting latent heat flux (LE) across the study region is shown in Figure 1. (Figure Presented) While atmospheric corrections are important for obtaining accurate estimates of the normalised difference vegetation index (NDVI), the impact on the modelled flux of a 5-15% change in NDVI was less than 10 Wm -2 for both models. The atmospheric corrections for the land surface temperature caused the greatest impact on the modelled energy balance components. An increase of 2 degrees in land surface temperature at the pasture sites caused the sensible heat flux estimated by the SEBAL model to be reduced by approximately 10% (or 20 Wm-2) while the TSM sensible heat flux increased by as much as 50-175 Wm-2 at the pasture sites. These results indicate the importance of making appropriate atmospheric corrections to thermal remotely sensed data for land surface flux estimation.