## 3 Results  
### Model Comparison and Accuracy Evaluation  
Based on the same set of sample data, we compared the performance of four different models--MLR, RF, MLPRegressor, and XGBoost--during the training process. Comparative analysis using multiple evaluation metrics showed that the XGBoost model demonstrated superior predictive performance. In terms of model evaluation, XGBoost achieved the lowest MAE and RMSE while having an R\({}^{2}\) value closest to the theoretical optimum of one, as shown in Figure 3a-d, indicating its excellent prediction accuracy and stability. The comparison results suggest that, for the global GOME-2 SIF data, XGBoost outperforms the commonly used RF algorithm in terms of accuracy. Although MLPRegressor achieves comparable prediction results to XGBoost, it requires significantly longer training time.  
Figure 2: Research flowchart.  
When building the model using the XGBoost algorithm, hyperparameter tuning is typically required before model fitting to avoid issues such as overfitting. Grid search provides a list of values for each specified hyperparameter to be optimized and tests all possible combinations of these values [39]. This method is characterized by exhaustive exploration of parameter combinations. If the step size is small enough and the parameter range is wide, the model can theoretically approach the global optimum. However, due to the enormous computational load, this process is very slow. Therefore, this study adopted a hybrid approach combining manual tuning and grid search to optimize parameters. One hyperparameter was optimized at a time, using the best-fitting result from the previous step to guide the tuning of the next. The hyperparameters were tuned sequentially in the following order: n_estimators, min_child_weight, max_depth, and learning_rate. Final values were selected based on the best-fitting results. The impact of each hyperparameter on training performance is shown in Figure 3e-g). For each parameter, training metrics reached a peak before declining; the peak was selected as the optimal hyperparameter value. The final list of optimal hyperparameters is shown in Table 2.  
Figure 3: Model training results. (**a–d**) represents the training results of four different models: MLR, RF, MLPRegressor, and XGBoost; (**e–g**) shows the parameter tuning process of XGBoost, and (**h–j**) shows the performance evaluation of the XGBoost model under different land use types.  
In addition to evaluating the overall performance of the model at the global scale, we further assessed its testing performance across different ecosystems based on land cover classification data to verify regional robustness. The results showed that the model exhibited strong predictive accuracy across all ecosystem types, as shown in Figure 3h-j. The land cover categories corresponding to the classification codes are listed in the caption of Figure 1. Among them, the model performed best in forest ecosystems, while performance in water bodies and open shrulands was relatively lower. This discrepancy may be attributed to the high spatial heterogeneity in sparsely vegetated areas, which increases intra-pixel noise and reduces model stability. Notably, the model performed poorly in evergreen broadleaf forests (IGBP = 2), where the R\({}^{2}\) value was relatively low. This finding is consistent with the results reported by [PERSON] et al. (2014). As shown in the global land cover map (Figure 1), this vegetation type is mainly distributed in tropical rainforest regions such as the Amazon. These areas are frequently affected by cloud cover, resulting in higher noise levels in remote sensing data ([PERSON] et al., 2014). In addition, the subtle seasonal changes in the canopy structure of evergreen broadleaf forests make it difficult for moderate-resolution sensors (e.g., MODIS) to detect fine spectral differences, which in turn affects model accuracy ([PERSON] et al., 2014).