## 3 Results and Discussion
### Performance of Machine Learning Methods  
The selected five LUE models together with Koppen-Geiger climate zones were fused by the SVM and RF. This process greatly increased the accuracy of daily GPP estimation in four forest types as shown in Figures 3-6, and Figures S1-S3 in the Supporting Information File. Considering \(R^{2}\) as the metric, the SVM and RF increased the accuracy of the GPP estimation by 27% and 28% compared to EC-LUE and decreased the _RMSE_ (gC m\({}^{-2}\) day\({}^{-1}\)) from 2.77 (in EC-LUE) to 1.75 and 1.72. Both SVM and RF explained more than 90% of the GPP variability for DBF, 71% for EBF, 80% for ENF, and 77% for MF, which were significantly more than individual LUE models. Previous studies indicated that the SVM and RF outperformed other techniques and could effectively simulate land surface variables such as evapotranspiration and GPP, and our results were consistent with previous research findings [10; 33; 57; 58].  
The LUE model assumes that the ecosystem GPP is dependent on the absorbed PAR and the actual LUE that is downregulated by environmental constraints such as temperature stress and water limitation. The inputs of LUE models in this study, which were derived from the FLUXNET2015 dataset or satellite observations, could reflect the seasonal variation in environmental factors, so the LUE models could capture the seasonal cycle of forests and then contribute to the performance of the SVM and RF in GPP estimation. Previous studies showed that it was more difficult to simulate GPP in evergreen broadleaf forests because of slight fluctuations in seasonal canopy phenology and environmental conditions [17; 59; 60], and our results drew similar conclusions, as shown in Figure 3 and Figure S1. Although LUE models performed worse in the EBF, the SVM and RF could still significantly increase \(R^{2}\) (from 0.55 to 0.71 and 0.72) and decrease _RMSE_ (from 3.09 to 1.94 and 1.91), which proved that machine learning could be a feasible way to enhance the ability of simulating GPP for evergreen broadleaf forests.  
LUE models are simplified equations describing how vegetation synthesizes organic matter through photosynthesis, which inevitably introduces errors for overlooking the details in the photosynthetic process. Furthermore, the uncertainties in ground measurement that drive LUE models also introduce interferences in GPP estimation. Due to the different input parameters and model structure in calculating temperature and water constraints, LUE models exhibited quite a variability in GPP simulation. Compared to LUE models, machine learning could achieve higher accuracy with the premise of providing sufficient high-quality training data. With sufficient training data and parameterization, machine learning models can automatically learn the implicit relationships between complex physical phenomena and problems without theoretical logic embedding [61; 32; 62]. As nonlinear models, machine learning could provide stronger regression capability than normal linear models, which can explain the outstanding performance in GPP simulations. Based on LUE modeling, machine learning models set up the relationships between inputs and ground-measurement-derived GPP and outperformed all individual LUE models. Therefore, as Figures 3-5 show, compared to the single model, the SVM and RF vastly improved GPP estimation accuracy.