Evapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets. Satellite‐based vegetation index approaches have used remotely sensed vegetation and reanalysis meteorological properties with surface energy balance models to estimate global ET (MOD16 ET). We reconstructed satellite retrievals using in situ meteorology (Argonne‐ET) and evaluated them using a dense network of surface turbulent heat flux measurements. Argonne‐ET resolves spatial heterogeneity of ET across the U.S. Southern Great Plains that is not well characterized by MOD16; MOD16 ET exhibits spatial autocorrelation (Moran’s I significantly >0 in May–Aug.), whereas in situ and Argonne‐ET exhibit characteristics of a random spatial process (Moran’s I not significantly different from 0). The skill in resolving ET temporal variability is not significantly different between MOD16 and Argonne‐ET (correlation coefficient = 0.75 and 0.72, respectively). However, the root‐mean‐square errors were significantly lower for Argonne‐ET (36 W/m2) than MOD16 (43 W/m2), and MOD16 exhibits substantial bias in annual ET relative to in situ measurements (−38%). This is attributed to overestimation in the dry canopy surface resistance (rs) parameterization. Using rs constrained to the range of typical measured values, Argonne‐ET substantially reduces the bias in the annual ET (+1%). The improved ET estimates are critical for regional water budget analyses. The methodology presented herein also demonstrates the ability to retrieve high temporally resolved (30 min; cf. 8‐day MOD16) ET that can be used for development of processed‐based diagnostics of model biases and to elucidate avenues to improve ET model parameterizations.