## 3 Results  
### Evaluation of the Accuracy of the GPP Estimation Model  
Data from observations conducted on 96 sample plots of forest, cultivated land, and shrubland, along with 84 grassland sample plots, were randomly partitioned into training and validation datasets at a ratio of 7:3. Based on the GOSIF dataset and the LUE, XGBoost, and RF methods, we constructed GPP estimation models for diverse vegetation types. Their performance for various vegetation types was evaluated using a validation dataset, and the optimal GPP estimation model was selected. The GPP\({}_{\text{RF}}\) model demonstrated significant advantages across all evaluation indicators (Table 3, Figure 3). The R\({}^{2}\) values of the validation results for the GPP\({}_{\text{RF}}\) model were greater than those of the corresponding GPP\({}_{\text{XGBoost}}\) and GPP\({}_{\text{LUE}}\) models for various vegetation types. Specifically, the R\({}^{2}\) of the cultivated land GPP\({}_{\text{RF}}\) was 0.081 higher than the R\({}^{2}\) of the GPP\({}_{\text{LUE}}\) and 0.071 higher than the R\({}^{2}\) of the GPP\({}_{\text{XGBoost}}\); the R\({}^{2}\) of the forest land GPP\({}_{\text{RF}}\) was 0.015 higher than the R\({}^{2}\) of the GPP\({}_{\text{LUE}}\) and 0.011 higher than the R\({}^{2}\) of the GPP\({}_{\text{XGBoost}}\); the R\({}^{2}\) of the grass land GPP\({}_{\text{RF}}\) was 0.274 higher than the R\({}^{2}\) of the GPP\({}_{\text{LUE}}\) and 0.009 higher than the R\({}^{2}\) of GPP\({}_{\text{XGBoost}}\); the shrub land GPP\({}_{\text{RF}}\) had an R\({}^{2}\) that was 0.069 higher than the R\({}^{2}\) of GPP\({}_{\text{LUE}}\) and 0.020 higher than the R\({}^{2}\) of GPP\({}_{\text{XGBoost}}\). This suggests that the RF model is well suited and exhibits high prediction accuracy in arid zone ecosystems with relatively complex climates. By contrast, the R\({}^{2}\) values of the validation results for the three GPP estimation models were lower. In addition, the RMSE and MAE values of the GPP\({}_{\text{RF}}\) model validation results were lower for \begin{table}
\begin{tabular}{c c} \hline \(|\)**SAI1 Value** & **The Degree of Drought Anomalies** \\ \hline \(|\)SAI\(|\leq\) 0.5 & normal \\ 0.5 \(<\) \(|\)SAI\(|\leq\) 1 & mildly abnormal \\ 1 \(<\) \(|\)SAI\(|\leq\) 1.5 & moderately abnormal \\ 1.5 \(<\) \(|\)SAI\(|\leq\) 2 & severely abnormal \\ \hline \end{tabular}
\end{table} 
Table 2: Classification of drought anomalies. the four vegetation types. Specifically, the RMSE and MAE of GPPR for cultivated land were 11.874 and 12.670 lower than those of GPPR, and 22.392 and 16.210 lower than those of GPPR. For forest land, the GPPR RMSE and MAE were lower by 1.749 and 2.612 compared to GPPLUE, and by 2.599 and 3.693 compared to GPPXGBoost, respectively. In the case of grass land, the GPPR model exhibited lower RMSE and MAE values of 7.397 and 5.546, compared to GPPLUE, and 4.115 and 2.475, respectively, compared to GPPPXGBoost. Additionally, for shrub land, the RMSE and MAE of GPPR were notably lower than those of GPPLUE by 1.707 and 3.281, compared to GPPXGBoost, and were lower by 1.945 and 0.197, respectively. These results highlight the superior predictive accuracy of the GPPR across different vegetation types. In conclusion, the RF model showed excellent performance in estimating the GPP in the arid region of Xinjiang. We conducted a validation process to ascertain the precision and applicability of the RF model for estimating the GPP in Xinjiang; this involved the random selection of 200 sample points for each vegetation type to ensure a comprehensive and representative dataset for analysis. We then compared the RF model estimation results for these sample points with the corresponding pixel values derived from the MODIS GPP product (Figure 4). Our findings revealed a strong concordance between the RF model estimates for the four vegetation types and the GPP product, with R\({}^{2}\) values > 0.700.