## 1 Introduction  
Land surface evapotranspiration (ET) consists mainly of evaporation (E) from soil and vegetation surfaces and transpiration (T) from plant canopies; these are all linked to the water, energy, and carbon cycles [1]. Studies have shown that around 58-65% of precipitation returns to the atmosphere through terrestrial ET, and that transpiration of vegetation accounts for more than 65% of terrestrial ET [2]. When converted to energy, this ET is equivalent to 51-58% of the incident energy due to ambient net radiation [3]. As a result, ET plays an important role in determining regional and global water balances and strongly influences the development of complex regionally coupled hydrology, ecology, and atmospheric systems [1]. The accurate estimation of ET is crucial not only for the study of the global and regional effects of climate change [4, 5], but also for studying resource utilization, crop yield prediction, drought monitoring, and weather forecasting [6]. Generally, ET is measured on the ground using field lysimeters, eddy covariance systems and large-aperture scintillometers, providing spatial representatives from hundreds of meters to several kilometers. The instrumental techniques mentioned above may only be used at the appropriate spatial scales, which are generally small [7]; many are campaign mode, and the observations are hard to extend to larger regions, such as watersheds, due to the typically strong spatial heterogeneity in vegetation transpiration and soil evaporation. Therefore, ground-based observations are insufficient to fulfill the needs of estimating regional- and global-scale ET. With the development of remote sensing technology, surface model simulations based on remote sensing data have proven to be an excellent method for estimating ET at regional to global scales [8; 9; 10]. A large number of remote-sensing-modeled ET datasets have been released, over a wide range of spatial and temporal scales. For instance, some of the available global-scale data, with a temporal resolution of 8 days and spatial resolution from 500 to 1000 m, include MOD16 (the Moderate-Resolution Imaging Spectroradiometer, spanning 2000-present), an important terrestrial ET product based on a modified Penman-Monteith approach. The effect of soil moisture in MOD16 is indirectly expressed through relative humidity (RH), vapor pressure deficit (VPD), and leaf area index (LAI), resulting in uncertainty in regional ET estimation. PML-V2 (Penman-Monteith-Leuning) (spanning 2000-2020) couples transpiration of vegetation with GPP (gross primary productivity) according to the stomatal conductance theory, and the parameters in PML-V2 are rate-determined from observations at 95 eddy-related flux stations around the globe and extended by vegetation type. In this way, the PML-V2 model was able to efficiently estimate the effect of increasing atmospheric CO\({}_{2}\) concentration on ET [11]. The GLASS (Global Land Surface Satellite) ET dataset was produced by merging five process-based ET algorithms, adopting the concept of algorithmic aggregation with high accuracy [12]. Although the above datasets can meet the spatial precision needs of ET analysis at the watershed scale, the 8-day temporal scale is too coarse to make timely, informed irrigation management decisions for agriculture. Despite the availability of ET products with high temporal resolution\(-\)such as the daily ET product CLEAM (Global Land Evaporation Amsterdam Model), for instance, which calculates potential evaporation by the Priestley-Taylor formula based on observations of surface net radiation and near-surface air temperature\(-\)these cannot be used for field-scale ET analysis, because the spatial resolution is too coarse at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\).