## 5 Conclusions  
Two widely used machine learning algorithms, i.e., RF and ANN, were employed to estimate global GPP. We dynamically selected variables by removing the ones with the lowest importance and then re-learning, which allows us to achieve the highest possible prediction accuracy using as few explanatory variables as possible, considerably improving prediction efficiency. We systematically compared the performance divergencies of different VIs in predicting GPP and determined the best way to combine each vegetation index with other explanatory variables, reflecting the close relationship between plant traits and ecosystem functions. Under the data-driven approach, we investigated divergencies in the performance of GPP models developed based on three VIs (i.e., SIF, NIRv, and LAI). We identified which biophysical properties, environmental factors, and climatic variables paired with individual VIs had the best predictive power for GPP and elucidated the potential mechanisms. We show that each vegetation index is a crucial driver of simulated spatial and temporal variability in GPP, and the SIF-based model performs best when modeled using a single vegetation index. However, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy. Furthermore, we consistently found that preseason temperature was selected as an important factor for estimating GPP in all six models. For the NIRv- and LAI-based GPP prediction models, solar radiation is the most critical climatic factor. We found that plant traits provide crucial canopy structure information, which effectively enhances the accuracy of all GPP models. Climatic variables are essential factors for improving the accuracy of NIRv- or LAI-based GPP models, but not for SIF-based models. Our study provides valuable information on the configuration of data-driven models designed to improve the accuracy of global GPP predictions and provides insights into the underlying physiological and ecological mechanisms involved. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs14246316/s1](https://www.mdpi.com/article/10.3390/rs14246316/s1)]([https://www.mdpi.com/article/10.3390/rs14246316/s1](https://www.mdpi.com/article/10.3390/rs14246316/s1)), Figure S1: Evaluation of the simulated GPP against flux tower measurements; Figure S2: PFT-specific models' performance for each PFT along different latitude gradients using the RF method; Figure S3: Universal models' performance for each PFT along different latitude gradients using the BPNN method; Figure S4: PFT-specific models' performance for each PFT along different latitude gradients using the BPNN method; Figure S5: The sample size for each PFT along different latitude gradients; Figure S6: Latitudinal profiles of annual mean GPP for 2003-2018; Figure S7: Interannual variability and trends for the period of 2003-2018; Figure S8: Comparisons of interannual variability in GPP among different GPP products; Figure S9: Spatial patterns of global GPP trends for 2003-2019; Figure S10: The performance of BPNN models using different combinations of predictors; Figure S11: The relationship between the number of features selected and the performance of the random forest models; Table S1: FLUXNET2015 Tier1 sites used in this study; Table S2: Model codes and their corresponding predictors. Conceptualization, Z.Z. ; methodology, W.Z. and Z.Z. ; software, W.Z. ; validation, W.Z. and Z.Z. ; formal analysis, W.Z. ; investigation, W.Z. and Z.Z. ; resources, W.Z. ; data curation, W.Z. ; writing--original draft preparation, W.Z. ; writing--review and editing, Z.Z. ; visualization, W.Z. ; supervision, Z.Z. ; project administration, Z.Z. ; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.