## 5 Conclusions  
In this study, we first developed two new GPP models, driven by convenient remote sensing data and climate variables, with an emphasis on the representations of GPP responses to cloud, CO\({}_{2}\), and water stress. Then, the performances of the new models along with other commonly used GPP models were appraised using GPP estimates from global eddy covariance (EC) flux towers. The quality of major spatial data layers that drive the models at the global scale was also evaluated at the EC-towers. Two new models (LUE-EF and LUE-NDWI) showed advantages in simulating daily GPP for most biomes. Meanwhile, they had a stronger ability in capturing the spatial and temporal changes of GPP and the improvements of the new models were more apparent for tropical and northern temperate zones. Then, a new global GPP product was generated using the LUE-NDWI model. The LUE-EF model was not applied on a global scale because of the difficulty in estimating the water stress in the LUE-EF model (i.e., the evaporative fraction of net solar radiation), which cannot be readily derived from satellite observations. Various biases were found in the spatial datasets that feed the models, affecting the simulation of GPP from site to global scales. Addressing these biases is a high priority for earth system science. In addition, large differences were found from 24 global models with long-term GPP averages varying from 92.7 to 178.9 Pg C yr\({}^{-1}\). The newly developed LUE-NDWI model (after correcting input data biases) produced a global mean annual GPP that is very close to the mean level of all the models within the inter-quantile range of GPP from all 24 models. Moving forward, a reduction in model complexity and error accumulation, validation, and calibration of the input data fields are key processes of improving GPP simulation. The newly developed new remote sensing-based GPP model had excellent performance and could be used to estimate GPP across a range of scales. Improving the quality of input data fields should be a major research component in reducing the uncertainty in GPP simulations on regional to global scales. The existent biases of spatial data not only affect GPP simulation but more importantly hinder the simulation and understanding of the earth system. Effective correction of spatial data is critical for reducing the uncertainty in GPP simulations and research on improving data quality should be encouraged. In this regard, our research cautions using currently available spatial data for relevant research. All the authors have made a substantial contribution towards the successful completion of this manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 41971152); and the Hunan Innovative Talent Program (Grant No. 2019 RS1062) to [PERSON]. The data presented in this study are openly available in FigShare at [[https://figshare.com/s/2462e5d7c3c203a044c](https://figshare.com/s/2462e5d7c3c203a044c)]([https://figshare.com/s/2462e5d7c3c203a044c](https://figshare.com/s/2462e5d7c3c203a044c)). We thank the anonymous reviewers for their valuable comments. We gratefully acknowledge FLUXNET, NASA, and NOAA for providing the data that were used in this study. The covariance data used in the study was acquired and shared by the FLUXNET community, CO2 data from Earth System Research Laboratory Global Monitoring Division ([[https://www.esrl.noaa.gov/](https://www.esrl.noaa.gov/)]([https://www.esrl.noaa.gov/](https://www.esrl.noaa.gov/))), and the mete-orological data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2).