These approaches can be broadly grouped into four categories: (1) empirical and semi-empirical methods [7; 8]; (2) surface energy balance models (SEB) (e.g., the surface energy balance algorithm for land (SEBAL), the surface energy balance system (SEBS) and mapping ET with internalized calibration (METRIC), the two-source energy balance model (TSEB), and the simplified two-source energy balance model (STSEB) [9; 10; 11; 12; 13; 14]; (3) vegetation index approaches (e.g., vegetation index combined with the Penman-Monteith (PM) method and the Priestley-Taylor (PT) method) [15; 16]; (4) data assimilation combined with land surface models and observations [17; 18]. RS-based models have been developed and applied over a wide range of spatial scales from local to global, performing consistently with ground measurements by the relative error of 10-30% for daily ET and 5% for seasonal and annual ET [19; 20; 21; 22; 23]. The focus of this study is on the physically-based SEBS model, which calculates the sensible heat flux (H) using a single-source bulk transfer equation and derives the evapotranspiration from the evaporative fraction [10]. It is less site-specific compared to a dual-source model such as TSEB and does not require subjective intervention by the model user, such as in SEBAL and METRIC techniques, where selecting hot and cold endmembers within the scene is required [24]. The SEBS model has been extensively applied and validated with a variety of methods in different regions and climates, having a good utility to create multi-scale high spatial and temporal resolution ET datasets [25; 26; 27; 28; 29]. However, many studies have reported that the SEBS model overestimates ET in the range of 0.5-3.0 mm d\({}^{-1}\) due to the underestimation of sensible heat flux in semi-arid and arid regions where water limiting situations prevail [30; 31; 32]. [PERSON] et al. (2012) adapted heat transfer additional damping (kB\({}^{-1}\)) in SEBS by a scaling factor retrieved from passive microwave soil moisture products (Advanced Microwave Scanning Radiometer-Earth Observing System, AMSE-R, 0.25\({}^{\circ}\)), with an overall improvement on sensible heat flux of 40 W m\({}^{-2}\) under water-limited conditions [33]. The passive microwave soil moisture data has a coarser spatial resolution (-25 km) compared to the MODIS (moderate resolution imaging spectroradiometer) thermal bands with 1 km resolution, which would induce error and uncertainty from multiple-resolution data [34]. [PERSON] et al. (2015) applied this approach using soil moisture data with a higher spatial resolution retrieved from airborne PLMR (a dual polarization L-band (1.4 GHz) passive microwave radiometer, 700 m) in the irrigated oases of arid regions in Northwest China and obviously overcame the ET overestimation [35]. However, the expensive airborne radar could not become a conventional data source to support irrigation management. Hence, inexpensive and readily available RS data should be considered to solve the problem of consistency in the process of calculating ET. High spatial resolution imagery (<100 m) is commonly considered necessary to distinguish the differences in ET among agricultural fields. However, some high spatial resolution imagery (e.g., Landsat 30 m, ASTER (the advanced spaceborne thermal emission and reflection radiometer) 15 m) is only available at two weeks or greater temporal resolution, which makes its application problematic in areas with high cloud cover and dynamic land cover [36]. The Chinese HJ-1 (the environmental and disaster monitoring and forecasting with a small satellite constellation) can provide global coverage every two days with a spatial resolution of 30 m. The characteristics of short repeat interval and high spatial resolution are very useful for monitoring the regional land surface variables, such as vegetation indexes (VI), drought indexes, land surface temperature (LST), and ET [37; 38; 39; 40; 41].