###### Abstract
Many efforts have been made on the calibration and validation of LUE models in regional scale applications [18]. In LUE models, GPP is estimated as the production of absorbed photosynthetically active radiation (APAR) by plant canopy and the efficiency of converting APAR to CO\({}_{2}\) fixation through plant photosynthesis [19]. Examples of such models include the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP algorithm (i.e., MOD17) [20], the Vegetation Photosynthesis Model (VPM) [21; 22], the light use efficiency model using eddy covariance measurements (EC-LUE) [23], and the Global Production Efficiency Model (GLO-PEM) [24], among others. The differences among these models are mainly the expression of different environmental stress factors used to present the reduction of the potential (or maximal) LUE. The MOD17 algorithm uses the potential LUE (i.e., \(\varepsilon_{max}\)) of different vegetation types from the biological look-up table and uses two scalars of temperature and vapor pressure deficit (VPD) to account for the reduction in \(\varepsilon_{max}\) when cold temperatures and high VPD limit plant function. However, evaluation of the MOD17 GPP by the BigFoot validation approach found that MOD17 GPP seriously underestimated the total GPP in cropland, which was only two-thirds of the GPP estimated by the BigFoot model [25]. Site-level verification studies also showed that MOD17 GPP failed to capture the seasonal and inter-annual changes, with underestimation in peak growing season and overestimation in the early growth and senescence stages [26]. The deviation of MOD17 GPP seasonal variation is mainly due to the inadequate parameterization of moisture constraint [26] and the use of constant value of the potential LUE across crops in different regions. Soil water availability status is an important factor in GPP modeling. Research reveals that in arid and semi-arid regions, soil moisture is the main controlling factor of GPP, and the substantial impact of soil moisture stress may cause the loss of GPP by 40% in arid/semi-arid and sub-humid regions [27]. In addition, the extent of ecosystems subject to soil moisture stress will further increase in a warming climate in the future [28; 29]. However, it is challenging to quantity the influence of soil moisture stress on the estimation of crop LUE over regional or global scales from either modeling or remote sensing observations. Some studies have proposed alternative approaches. For example, the VPM model uses the Land Surface Water Index (LSWI) to estimate the water stress in calculation of LUE, and it performs well in forests [21; 22; 30]. The Production Efficiency Model Optimized for Crops (PEMOC) uses relative change of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) across the seasons to estimate seasonal moisture stress, which assumes that the relative change of FAPAR is mainly related to changes in plant water conditions [18]. The EC-LUE model uses evaporative fraction (EF) to calculate moisture stress at the flux tower site-level, and it was found to be reliable for simulating crop GPP [2; 23]. EF can represent moisture conditions of ecosystems because the decrease in energy allocated to the latent heat flux indicates stronger soil water restriction. However, the application of EF for GPP estimation at large spatial scales with moderate spatial resolution (e.g., at kilometer resolution) was hindered by the lack of reliable datasets of sensible and latent heat fluxes at corresponding spatial scales and spatial resolution. Previous studies show that the evaporative fraction based on microwave vegetation indices can help to indicate synoptic-scale water stress on LUE, but limited to relatively coarse resolutions [31]. With the development of global evapotranspiration algorithms and products at higher resolution and improved accuracy [32; 33], it is possible to use EF to better express the water availability conditions of ecosystems.