###### Abstract  
Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO\({}_{2}\) flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001-2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m\({}^{-2}\) d\({}^{-1}\) in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R\({}^{2}\), KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP. g 202214172220214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172214172141722141 productivity is vital to the agricultural management, crop yield forecast, and global carbon budget assessment [3]. At present, the relatively matured GPP models based on remote sensing satellite data at the canopy scale are mainly divided into two major categories: empirical models and process-based models [4]. The empirical models are relatively simple and easy for regional and global application but have high uncertainty or low accuracy. Most empirical modes are based on the statistical relationship between terrestrial productivity and climate variables or vegetation indexes such as the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), near infrared radiation from vegetation (NIRv) [5; 6; 7; 8], solar induced fluorescence (SIF) [9; 10; 11], or Vegetation Optical Depth (VOD) [12; 13; 14; 15; 16]. The process-based GPP models are supposed to estimate GPP with higher accuracy because they generally consider detailed ecological processes, but they are difficult to apply at high resolution on global scale due to model complexity and many unknown parameters. The widely used light-use-efficiency based model (LUE model) [17], mainly driven by remote sensing data, is a semi-empirical model for GPP estimate because it not only considers the response mechanism of photosynthetic efficiency to environmental conditions, but also easily operate on large scales. It can effectively quantify spatiotemporal variation of GPP at regional and global scales if model parameters can be calibrated properly.