## 4 Discussion  
### Evaluation of Model Performance  
Our comprehensive analysis demonstrated the superior capability of ML approaches in estimating forest GPP across four EC flux sites in China, consistently surpassing the performance of conventional linear regression methods. These findings align with and further reinforce the growing body of evidence supporting the advantages of ML-based approaches in ecosystem carbon flux estimation. For instance, [PERSON] et al. reported similar improvements in carbon flux predictions using ML algorithms [54], while [PERSON] et al. documented enhanced accuracy in GPP estimations through advanced ML techniques [55]. More recent studies by [PERSON] et al. and [PERSON] et al. have also corroborated the robust performance of ML methods in capturing complex ecosystem-atmosphere interactions [7; 56]. Irrespective of forest type classification, the XGBoost model exhibited markedly enhanced GPP estimation accuracy and predictive performance across the entire study domain, demonstrating substantial improvements over alternative methodological approaches. The five methods showed varying capabilities in estimating GPP for different forest types, with XGB, a tree-based ML algorithm, showing obvious advantages over the others [57; 58].  
Our comparative analysis reveals that both hyperparameter optimization (HO) and combined hyperparameter-variable optimization (HVCO) significantly enhance XGBoost model performance for GPP estimation (Figure 11). The RGSA effectively identifies optimal parameter configurations, while the variable combination optimization addresses multicollinearity issues evident in the VIF analysis. The selected variable combination (NDVI, Temp, VPD, NR, LAI) exhibits substantially lower multicollinearity than excluded variables like PAR and SR (VIF > 100), contributing to the model's improved stability and accuracy [59]. Notably, NDVI (VIF = 5.17) was selected over EVI (VIF = 8.08) despite EVI's theoretical advantages in dense vegetation, likely because NDVI's lower multicollinearity  
Figure 10: Spatial distribution of correlation coefficient (CC, upper row) and distance (\(D\), lower row) metrics derived from CMP analysis for GOSIF, GLASS, and FLUXCOM products across four study regions (**a-d**). Higher _CC_ values indicate stronger correlation while higher \(D\) values represent greater dissimilarity between observed patterns.  
ity provides greater statistical benefits within the model framework. This optimization strategy resulted in substantial performance improvements, with HVCO achieving 7.79% higher \(R^{2}\) and 13.38% lower \(RMSE\) compared to the unoptimized model, demonstrating that addressing both algorithm parameterization and multicollinearity through strategic variable selection provides a robust framework for forest productivity assessment.  
Our models exhibited notably poorer performance during summer compared to other seasons. This seasonal discrepancy can be primarily attributed to three factors in our study regions. First, increased cloud cover during the East Asian monsoon season significantly reduces optical remote sensing data quality in southern provinces [60]. Second, frequent summer precipitation creates complex soil moisture conditions where moisture-productivity relationships become nonlinear when approaching saturation [61]. Third, dense summer canopies in these forest ecosystems may lead to optical signal saturation, while high atmospheric water vapor content increases uncertainties in satellite data processing [62]. These combined factors limit the models' ability to accurately capture the complex ecosystem dynamics during the peak growing season, suggesting that future improvements could incorporate multi-source remote sensing data to overcome these seasonal limitations.