The Western Boreal Plain of north-central Alberta is prone to water-deficit conditions and is hydrologically sensitive to changes in climate, natural resource extraction and disturbance. Accurate measurement and modelling of the main components of the water balance are important for ecosystem and reclamation management; however, the lack of hydro-meteorological instrumentation found within different land cover types makes quantification of changes to the water balance difficult over large areas. Remote sensing data can provide spatial estimates of land cover distribution and leaf area index (LAI) used as inputs into land surface models. However, land surface models can often suffer from inaccuracies as a result of spatial (coarse pixel) and temporal (discrete acquisition) resolutions, mis-classification and inaccurate representation of LAI using remote sensing data. This study uses high-resolution (1 m × 1 m) Light Detection and Ranging-derived vegetation parameters (land cover type, LAI and 3D vegetation frictional influences on air movement) as inputs into the Penman–Monteith evapotranspiration (ET) model along with measured hydro-meteorological variables. Comparison with eddy covariance (EC) measurements indicated that spatially explicit ET estimates at 1 m resolution (over a 5 km × 5 km study area) provided better estimates compared with bulk average ET estimates per land cover type. ET estimates scaled using spatially variable vegetation inputs only underestimated measured fluxes by 2% and 3% at 22·5 and 3 m EC instrumentation towers, respectively. Bulk averaged ET estimates underestimated measured ET by 5% at the 3 m tower and overestimated EC by 7% at the 22·5 m EC tower. Over coarser scales, the error associated with bulk input parameters can lead to error in overall water balance estimation.