To understand the spatiotemporal variations of water consumption and improve water resource management in the HRB, the robust access of LE has become the focus of attention of various institutions and researchers, which would be helpful to maintain the healthy and sustainable development of regional economy [10]. Satellite remote sensing can obtain continuous spatial surface information in very short period and has played an increasingly important role in LE estimation at the regional scale [8; 11; 12]. In recent decades, with the development of satellite remote sensing technique for monitoring surface LE-related variables, there are many satellite-based LE models available including empirical regression models and physical models. The empirical regression methods are simple and operable, and have been widely applied to estimate regional and global LE [13; 14; 15]. Machine learning (ML) method has the excellent ability in prediction and has been widely used to generate LE products (i.e., WB-MTE [16]). Similarly, [PERSON] et al. used random forest methods to produce a global set of LE products and [PERSON] et al. applied nine machine learning (9 ML) models to develop FLUXCOM LE products [17; 18]. Physical LE models include the surface energy balance models [19], the Penman-Monteith model [20; 21], the Priestley-Taylor model [22; 23], and temperature-vegetation index feature spatial model [24]. These physical models simulate the physical process of terrestrial LE and can be considered as the reliable methods to generate global LE products, such as the MOD16 LE product from the Moderate-resolution Imaging Spectroradiometer (MODIS) data released by National Aeronautics and Space Administration (NASA) [20; 25], the Global Land Evapotranspirationpiration Amsterdam Model (GLEAM) LE product [26; 27], the Breathing Earth System Simulator (BESS) LE product [28; 29], the Penman-Monteith-Leuning (PML)_V2 LE product [30], and the Global Land Surface Satellite (GLASS) LE product [31; 32]. Although many satellite-based LE products show relative consistencies in temporal and spatial patterns, there are still substantial differences in inter-comparison and validation against in situ flux networks [33]. The study from the Global Soil Wetness Project-2 (GSWP-2) concluded that there were large discrepancies among 15 LE products and the differences of mean annual global LE were close to 50% [34]. Similarly, a comparison of eight satellite-based ET models over China showed that the mean annual ET of them varied from 535 to 852 mm year\({}^{-1}\)[14]. [PERSON] et al. also found substantial differences between five process-based and empirical/semi-empirical LE algorithms against ground based LE measurements over Europe [35]. The differences among these global or quasi-global LE products will be more obvious at regional or river basin scale [32; 36]. As one of the inland river basins in northern China, the Heihe River basin also show large discrepancies in annual averaged ET from nine models, ranging from 80 to 229 mm year\({}^{-1}\)[37]. Therefore, it is urgent to comprehensively assess the existing satellite-derived terrestrial LE products in Heihe River basin to inform their applications in local watershed water resources management. Recently, the establishment of long-term EC flux tower networks (e.g., FLUXNET) has substantially advanced the evaluation of terrestrial LE. However, EC in situ measurements suffer from the energy imbalance problem, which leads to inaccuracies in the interpretations [38]. For ground-based validation, the scaling issue is also an important problem. The spatial scale mismatch between EC measurement coverage and satellite pixels may cause great uncertainties [37]. Furthermore, the observation periods of most EC sites are less than one decade, which is not conducive to the long-term evaluation [39]. Alternatively, the water balance method derived ET (WBET) can be used as a long-term LE evaluation method at basin scale [40].