## 1 Introduction  
Terrestrial evapotranspiration (ET) is an important biophysical process in the land-atmosphere system. As the second largest component of the global hydrological circle, it returns around 60-70% of precipitation to the atmosphere [1]. However, it is also one of the most uncertain components in the global water cycle [2; 3; 4]. Thus, accurately estimating ET is necessary for better quantification and allocation of water resources, which is of great significance to the sustainable utilization and management of global water resources in an era of growing climate change. Generally, the Penman-Monteith (PM) algorithm and the Priestley-Taylor (PT) algorithm are the two most commonly used methods for estimating ET, though the two algorithms have been proposed for more than four decades [5; 6; 7; 8; 9; 10]. Extensive studies have been conducted to improve the PM algorithm from different aspects, such as stomatal conductance [7], soil moisture [10], temperature difference between day and night [7; 10; 11], etc. However, to date, accurate estimation of actual ET at regional scales is still challenging [12]. In general, the methods for estimating regional ET can be grouped into four categories: upscaling of observed ET data, basin ET estimation based on the water balance theory, meteorological-data-derived ET and remote-sensing-data-derived ET. Although measurements such as the evaporator, scintillometer, Bowen ratio measuring system and flux tower with eddy covariance can provide accurate and dense vapor exchange at site and local scales, upscaling the observed ET to meet the accuracy demand at regional and global scales is hard [13]. Firstly, footprints of these measurements are rather small to represent a regional extent. Additionally, regions covered by enough ET measurements for various land cover types are very limited around the globe due to the expensive cost of instrument installation and maintenance. Actual ET estimation based on the water balance theory can provide an accurate regional result using measured precipitation and runoff datasets [14]. However, it cannot provide gridded ET results which may be more needed in water management, such as irrigation and reservoir operations. The meteorological-data-derived methods simulate actual ET based on robust physical mechanism models, such as the aerodynamic algorithm, the PM algorithm and the PT algorithm. The aerodynamic model dynamically integrates atmospheric water demand (i.e., vapor pressure deficit), and air and plant constraints (i.e., canopy resistance and aerodynamic resistance) [15]. The PM model calculates ET based on the principle of energy balance and water vapor transmission, and fully considers atmospheric physics and vegetation physiological characteristics [5; 11; 16; 17]. The PT model is a simplified PM algorithm, which uses a dimensionless coefficient (0-1.26) instead of the complicated surface resistance and aerodynamic resistance in PM model [18; 19]. These ET algorithms usually require meteorological forcing data with high temporal resolution, such as European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA) interim, Climatic Research Unit (CRU) reanalysis, a Global Land Data Assimilation System (GLDAS) dataset, Reanalysis datasets from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR), and Global Modeling and Assimilation Office Modern-Era Retrospective analysis for Research and Applications (GMAO-MERRA). However, subjected to the coarse spatial resolution of these meteorological forcing data, the spatial resolution of their derived ET is also coarse. Remotely sensed imagery, with its extensive data archive at much lower cost and higher spatial resolution, has been extensively applied for ET estimation at regional scales. The Surface Energy Balance (SEB) model and the surface temperature-vegetation index triangle model (Ts-VI) are also often used in remote-sensing-derived ET calculation in addition to the PM and PT algorithms [20; 21].