Evapotranspiration (ET) is a vital variable for land‐atmosphere interactions that links surface energy balance, water, and carbon cycles. The in situ techniques can measure ET accurately but the observations have limited spatial and temporal coverage. Modeling approaches have been used to estimate ET at broad spatial and temporal scales, while accurately simulating ET at regional scales remains a major challenge. In this study, we upscale ET from eddy covariance flux tower sites to the regional scale with machine learning algorithms. Five machine learning algorithms are employed for ET upscaling including artificial neural network, Cubist, deep belief network, random forest, and support vector machine. The machine learning methods are trained and tested at 36 flux towers sites (65 site years) across the Heihe River Basin and are then applied to estimate ET for each grid cell (1 km × 1 km) within the watershed and for each day over the period 2012–2016. The artificial neural network, Cubist, random forest, and support vector machine algorithms have almost identical performance in estimating ET and have slightly lower root‐mean‐square error than deep belief network at the site scale. The random forest algorithm has slightly lower relative uncertainty at the regional scale than other methods based on three‐cornered hat method. Additionally, the machine learning methods perform better over densely vegetated conditions than barren land or sparsely vegetated conditions. The regional ET generated from the machine learning approaches captured the spatial and temporal patterns of ET at the regional scale.

A unique and intensive flux observation matrix was established during May to September of 2012 in an oasis–desert area located in the middle reaches of the Heihe River basin, China. The flux observation matrix included 22 eddy covariance systems belonging to the first thematic experiment of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. The energy balance closure ratio (EBR) was assessed and possible mechanisms were investigated using remote sensing data. The results showed that 1) the EBR was in the range of 0.78–1.04 at all sites with an average EBR of 0.92, and 2) the calculated daily EBR exhibited better performance than the 30-min averages. 3) The heat storage cannot be ignored during the crop growing season. An improvement of approximately 6% in the total closure was found after considering the heat storage terms (canopy and photosynthesis storage) in the energy budget at the maize surface, and the canopy and photosynthesis showed approximately equal contributions of 3% for each storage term. The results also showed that 4) the land heterogeneous surface had a significant effect on the EBR. The EBR decreased with land surface heterogeneity increasing (taking the standard deviation of the surface temperature in the eddy covariance system source area as an index). The EBR also decreased when irrigation occurred and increased after irrigation was completed. The advection or secondary circulation broke the closed system of the energy balance given the phenomenon of EBR increasing when the advection or secondary circulation occurred.