Regional evapotranspiration (ET), including water loss from plant transpiration and soil evaporation, is essential to understanding interactions between land-atmosphere surface energy and water balances. Vapor pressure deficit (VPD) and surface air temperature are key variables for stomatal conductance and ET estimation. We developed an algorithm to estimate ET using the Penman-Monteith approach driven by Moderate Resolution Imaging Spectroradiometer (MODIS)-derived vegetation data and daily surface meteorological inputs including incoming solar radiation, air temperature, and VPD. The model was applied using alternate daily meteorological inputs, including (1) site level weather station observations, (2) VPD and air temperature derived from the Advanced Microwave Scanning Radiometer (AMSR-E) on the EOS Aqua satellite, and (3) Global Modeling and Assimilation Office (GMAO) reanalysis meteorology-based surface air temperature, humidity, and solar radiation data. Model performance was assessed across a North American latitudinal transect of six eddy covariance flux towers representing northern temperate grassland, boreal forest, and tundra biomes. Model results derived from the three meteorology data sets agree well with observed tower fluxes (r > 0.7; P < 0.003; root mean square error of latent heat flux <30 W m-2) and capture spatial patterns and seasonal variability in ET. The MODIS-AMSR-E-derived ET results also show similar accuracy to ET results derived from GMAO, while ET estimation error was generally more a function of algorithm parameterization than differences in meteorology drivers. Our results indicate significant potential for regional mapping and monitoring daily land surface ET using synergistic information from satellite optical IR and microwave remote sensing.