Estimation of actual crop evapotranspiration $({\mathrm{ET}}_{\mathrm{c}})$ using high-resolution aerial remote sensing data is important to detect water stress, map, and manage water resources in precision agriculture. High-resolution ${\mathrm{ET}}_{\mathrm{c}}$ can be estimated using land surface energy balance models and remotely sensed land surface temperatures (LST) obtained using manned or unmanned aerial vehicles equipped with thermal cameras. In this study, three remote sensing ${\mathrm{ET}}_{\mathrm{c}}$ models were compared, i.e., Two-Source Energy Balance (TSEB) model, Mapping Evapotranspiration with Internalized Calibration (METRIC), and Surface Energy Balance Algorithm for Land (SEBAL) models. Thermal images were obtained using an airplane flying daily over almond orchards in California during the 2020 growing season. The LST images were obtained at 0.5 m spatial resolution. Model comparisons indicated that all three models produced latent heat fluxes and net radiation estimates that agreed with eddy covariance measurements in the order of TSEB (R² of 0.89 for LE and 0.88 for R_n), METRIC (R² of 0.81 for LE and 0.86 for R_n), and SEBAL (R² of 0.81 for LE and 0.83 for R_n). However, METRIC and SEBAL overestimated the latent heat fluxes while underestimating the sensible heat fluxes as compared to the two-source model (TSEB). The root mean square error (RMSE) of the instantaneous latent and sensible heat fluxes were less than 38 W m⁻² for TSEB, and were within 58 W m⁻² for METRIC and SEBAL models. The TSEB model’s good performance can be attributed to its partitioning of surface temperature between soil, crop cover in the inter rows, and almond tree canopies. Overall, the results suggest that both one and two-source surface energy models originally developed for satellite imagery are able to estimate instantaneous turbulent fluxes and spatial variability in ${\mathrm{ET}}_{\mathrm{c}}$ using high-resolution imagery. In addition, systematic variations in LST due to variable rate irrigation scheduling as depicted in the high spatial resolution imagery provided confidence in the spatially distributed latent heat flux maps estimated by the energy balance models. This study shows that high-resolution aerial imagery combined with energy balance models originally developed for satellite remote sensing can be used to accurately estimate site specific ET_c that is critical to achieving precision irrigation management in almond orchards and other crops.