Many approaches have been developed for measuring or estimating actual evapotranspiration (ET_a), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ET_a. Several remote sensing methods can be used to estimate ET_a at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ET_a estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ET_a and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ET_a that are currently applied successfully in the United States. The METRIC model can produce maps of ET_a at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ET_a against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ET_a. The SSEBop model has been used to produce maps of ET_a over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ET_a estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ET_a estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth Engine^TM has been used to implement METRIC with automated calibration for regional-scale estimates of ET_a using Landsat data. The U.S. Geological Survey also is using Google Earth Engine^TM to implement SSEBop for estimating ET_a in the United States at a continental scale using Landsat data.