Evapotranspiration (ET) rates provide a valuable within-season indicator of plant productivity, as well as data on fluxes of water in a landscape. Applying remote sensing for ET estimation has potential to improve the sustainable management of water resources in agricultural settings. Most current ET models, however, rely on ‘dry’ and ‘wet’ pixels within a given scene to partition turbulent fluxes between latent and sensible heat, thus limiting their ability to map ET throughout the growing season at extremely high (meter scale) spatial resolutions. Here, we develop a field-validated surface energy balance model, High Resolution Mapping of EvapoTranspiration (HRMET), which requires only basic meteorological data, spatial surface temperature and canopy structure data. We use HRMET to estimate ET rates over two commercial cornfields in south-central Wisconsin during the 2012 growing season, which was characterized by severe drought. HRMET results indicate that the magnitude of within-field variability in ET rates was primarily driven by water availability. The application of remotely sensed data to precision agriculture has also been hampered by turnaround time between image acquisition and availability. We introduce relative ET (ETR), which enables comparison of ET rates between image dates by normalizing for variability caused by weather and crop stage. ETR also provides an intuitive, index-like metric for evaluating spatial variability in ET on a subfield scale. ETR maps illuminate persistent patterns in ET across measurement dates that may be driven by soil water availability and topography. ETR is used to develop a novel paired-image technique that can map subfield sensitivity classes to stressors such as drought. Sensitivity class mapping can be used to circumvent issues related to turnaround time to facilitate the incorporation of remotely sensed data into precision agriculture.