## 4 Conclusions  
Based on meteorological and eddy covariance flux measurements at 45 FLUXNET2015 forest sites and remote sensing data, we ran LUE GPP models (i.e., VPM, EC-LUE, GLO-PEM, CASA, and C-Fix) to estimate forest GPP and then fused these individual models based on Koppen-Geiger climate zones to improve the ability of simulating GPP. Our results showed that EC-LUE outperformed other LUE models, giving the highest \(R^{2}\) between simulated GPP and site-derived GPP on a daily scale. The performance of VPM and GLO-PEM were quite good too, while CASA and C-Fix showed extra uncertainties in simulation accuracy. The five-fold cross-validation showed that the SVM and RF could greatly improve estimation accuracy by 27% and 28%, respectively. A further analysis at each FLUXNET2015 site indicated that the RF could capture more magnitudes of GPP variations with a lower \(RMSE\), so the RF was the best fusion method for simulating forest GPP. Despite the possible errors from ground measurements, satellite observations, and LUE model structure, machine learning, especially RFs, could be a good alternative option to enhance the ability of GPP simulation beyond that of individual LUE models at site scale. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/fl5091615/s1](https://www.mdpi.com/article/10.3390/fl5091615/s1)]([https://www.mdpi.com/article/10.3390/fl5091615/s1](https://www.mdpi.com/article/10.3390/fl5091615/s1)), Figure S1: The scatter plots of the coefficient of determination (\(R^{2}\)), root-mean-square error (\(RMSE\), gC \({}^{-2}\)day\({}^{-1}\)), and relative predictive error (\(RPE\)) across the evergreen broadleaf forest (EBF) biome between daily site-derived GPP (gC m\({}^{-2}\)day\({}^{-1}\)) at flux sites and the estimates from five individual models, MODIS's GPP product, and machine learning methods (SVM and RF); Figure S2: The scatter plots of the coefficient of determination (\(R^{2}\)), root-mean-square error (\(RMSE\), gC m\({}^{-2}\)day\({}^{-1}\)), and relative predictive error (\(RPE\)) across the evergreen needlele forest (ENF) biome between daily site-derived GPP (gC m\({}^{-2}\)day\({}^{-1}\)) at flux sites and the estimates from five individual models, MODIS GPP's product, and machine learning methods (SVM and RF); Figure S3: The scatter plots of the coefficient of determination (\(R^{2}\)), root-mean-square error (\(RMSE\), gC m\({}^{-2}\)day\({}^{-1}\)), and relative predictive error (\(RPE\)) across the mixed forest (MF) biome between daily site-derived GPP (gC m\({}^{-2}\)day\({}^{-1}\)) at flux sites and the estimates from five individual models, MODIS's GPP product, and machine learning methods (SVM and RF); Table S1: The information of the 45 selected FLUXNET2015 forest sites; Table S2: The Koppen-Geiger climate classes; Table S3: The summary of the statistics (\(R^{2}\), \(RMSE\) (gC m\({}^{-2}\) day\({}^{-1}\)), and \(RPE\)) between modeled and FLUXNET2015's GPP across the 45 forest sites; Table S4: The optimum parameters of the RF and SVM across the DBF, EBF, ENF, and MF. **Author Contributions:** Conceptualization, Y.F. and Z.T. ; methodology, Z.T. ; software, Z.T. and Y.F. ; validation, C.Y. and Q.Z. ; formal analysis, Z.T. ; investigation, Z.T. ; resources, Z.T. ; data curation, C.Y. ; writing--original draft preparation, Z.T. and Y.F. ; writing--review and editing, C.Y., T.Z., E.K. and N.Y.K. ; visualization, Y.F. and Z.T. ; supervision, Y.F. ; project administration, Z.T. ; funding acquisition, Z.T. and Y.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Bureau of Statistics of China (grant number 2023 LY063), China University of Labor Relations (grant number 24 XYJS020), and the Disciplinary Funding of Beijing Technology and Business University (grant number STKY202311).