## 5 Conclusions  
This study employed various modelling approaches, including the LUE, XGBoost, and RF models, to improve the accuracy of GPP estimates; this is particularly crucial for understanding the dynamics of GPP changes, assessing carbon sequestration potential, and monitoring vegetation health in Xinjiang. Additionally, by examining the relationship between GPP and SPEI across different timescales, this study provides important insights into how drought conditions affect vegetation GPP. It offers valuable information for ecological conservation and climate resilience planning. The main conclusions are as follows:  
1. RF Model Outperforms Others in GPP Estimation:  
Among the models evaluated, the RF model based on GOSIF data demonstrated superior performance in estimating GPP in Xinjiang and was characterised by higher R\({}^{2}\) values and lower RMSE and MAE values than those of the GPP\({}_{\text{LUE}}\) and GPP\({}_{\text{XGBoost}}\) models; this underscores the performance of the RF model in estimating the GPP in complex arid environments.
2. GPP Seasonal Trends and Spatial Heterogeneity in Drought Year:  
The 2009 drought year in Xinjiang showed a seasonal trend in the vegetation GPP, peaking in July and declining after that. Spatial variations in GPP were observed, with higher values concentrated in specific regions, such as the Iii Valley, northern Tianshan Mountains, and oasis areas of the Tarim Basin.  
(3) GPP Sensitivity to Drought Conditions:  
The strongest correlation between vegetation GPP and SPEI at a 9-month timescale was observed, emphasising the temporal dynamics of drought in influencing GPP. Notably, grassland GPP exhibited the greatest sensitivity to drought conditions at the 9-month SPEI timescale, highlighting the importance of considering drought duration in assessments.  
(4)Influence path of TEM and PRE on GPP:  
TEM and precipitation PRE positively influenced the overall vegetation GPP in Xinjang, directly affecting GPP changes and indirectly modifying the LAI and FAPAR, thereby affecting GPP.  
In the following study, we will not only focus on typical drought years but also consider the response relationship between vegetation GPP and drought across longer time series. Further validation using a longer time series datasets is required to confirm the robustness of the model. Furthermore, when analysing the influencing factors of GPP, we not only consider natural factors but also take full account of the impact of human activities.  
C.X.: Conceptualisation, Methodology, Resources, Investigation, Writing--original draft preparation, and Visualisation. M.Z.: Conceptualisation, Formal analysis, Writing--review and editing, Project administration, and Funding acquisition. Y.Z.: Writing--review and editing, Investigation, Supervision, and Funding acquisition. K.L.: Validation, Visualisation, and Data curation. J.Z.: Data curation and Project administration. S.Y.: Validation and Visualisation. L.Z.: Resources and Data curation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers 42261013 and 42077419) and the Natural Science Foundation of the Xinjang Uygur Autonomous Region (grant number 2023D01A49).  
The raw data supporting the conclusions of this article will be made available by the authors without undue reservations.  
The authors declare no conflicts of interest.