## 4 Discussion
### Key Influencing Factors of Forest GPP  
Among the key environmental and biophysical variables (LAI, Temp, NR, VPD, and NDVI) utilized in estimating GPP of forest ecosystems in China, LAI emerges as the most influential determinant (Figure 12). LAI represents the total leaf area within a specific region and is not only a crucial indicator of vegetation cover and growth status but also a key factor in determining the photosynthetic potential of plants [63; 64; 65]. LAI reflects the ability of vegetation to intercept solar radiation [66], which is directly related to the absorption of PAR. Higher LAI means more leaf area involved in photosynthesis, thereby increasing the photosynthetic rate, which plays a decisive role in the increase in GPP. Therefore, LAI is positively correlated with GPP (\(R\) = 0.79 as shown in Figure 4). Additionally, [PERSON] et al. reported that even using LAI alone as a variable can effectively estimate GPP in different forest types in North America, further highlighting the significant variable LAI in GPP estimation [11]. Other variables, such as Temp, NR, and VPD, are also important for estimating vegetation photosynthesis [67]. For example, results in [PERSON] et al. demonstrated that GPP responds significantly to Temp, VPD, soil moisture supply, light saturation, cloud cover, and CO\({}_{2}\), and most of these responses are nonlinear [68]. Although EVI has a higher correlation with GPP than NDVI, it was not selected as a parameter in the  
Figure 11: Comparison between predicted and EC-based GPP under three optimization strategies: (**a**) Unoptimized (UO), (**b**) Hyperparameter Optimized (HO), and (**c**) Hyperparameter and Variable Combination Optimized (HVCO). Black solid lines represent fitted regression lines, red dashed lines show 1:1 relationships, and color intensity indicates data point frequency. Statistical metrics (\(R^{2}\), \(RMSE\), \(MAE\), and \(BIAS\)) demonstrate progressive model improvement from UO to HVCO. optimal variable combination. This may be due to the high correlation between EVI and LAI (\(R\) = 0.77), leading to multicollinearity issues that reduce the model's estimation performance. Furthermore, NDVI had the lowest importance, which might be due to data uncertainties and issues related to Cubic Spline interpolation. However, explanatory variable combinations with NDVI excluded might result in a worse model performance. The comparison of model performance between four-factor (LAI, Temp, NR, and VPD) and five-factor (LAI, Temp, NR, VPD, and NDVI) approaches revealed interesting patterns in GPP estimation accuracy. The slight reduction in model performance when excluding NDVI suggests that while NDVI contributes to model accuracy, the four-factor model still maintains robust predictive capability (Figure 13). This may be attributed to several reasons. First, most research sites are located in southern regions, where vegetation types and growth conditions may differ in their correlation with NDVI compared to other areas [69]. Given the abundant vegetation cover and extended growth cycles in these regions, NDVI, as an indicator of vegetation health and activity, may not fully capture its sensitivity and discriminative ability under varying vegetation conditions in this study. Second, although NDVI's relative importance in the model is limited, it still provides essential information on vegetation status, which is crucial for predicting GPP [70]. NDVI reflects vegetation biomass and photosynthetic activity, indispensable factors in GPP estimation. Third, in the ML model, no significant multicollinearity issues were observed between NDVI and other environmental factors (Figure 4). This indicates that NDVI can serve as a complementary factor, working in conjunction with other variables without causing information redundancy. Figure 12: Relative contribution of biophysical factors influencing forest GPP.