## 5 Conclusions  
In this study, we described the spatial and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001 to 2018 by combining the CASA model and a R\({}_{\rm H}\) model and assessed the effects of land cover change on them through a modeling scenario design. We found that vegetation in the study area mostly acted as a carbon sink from 2001 to 2018. Among them, it is noteworthy that the vegetation within bare areas, which mainly consist of desert ecosystems, has also shown a weak carbon sink in recent years. This is likely to be related to increased regional precipitation during the growing season, CO\({}_{2}\) fertilization, and biocrust distribution. Furthermore, we found that land cover change had an obvious positive influence on vegetation carbon sinks. Among them, the effect of land cover type conversion (abrupt change) contributed to an increase in total NEP of approximately 1.77 Tg C, which reached 15.55% of the original value, and after simultaneously considering the effect of vegetation growth enhancement (gradual change), it contributed to an increase in total NEP of approximately 14.75 Tg C, which reached 129.61% of the original value. For different land cover types, cropland consistently contributed the most (approximately 73.46%) to the increment of NEP in the ARNWC, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks, which is approximately 5.5 times more than in 2001. Thus, our study indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC from 2001-2018, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future. Our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, provide scientific guidance for the rational use and planning of land resources, and regulate human activities under the premise of reducing carbon emissions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15092471/s1](https://www.mdpi.com/article/10.3390/rs15092471/s1)]([https://www.mdpi.com/article/10.3390/rs15092471/s1](https://www.mdpi.com/article/10.3390/rs15092471/s1)), Figure S1: Direct and indirect strategies for modelling scenario design. (a) Comparison of direct and indirect strategies; (b) Differences between the results of direct/indirect strategic thinking adopted in the scenario design and the results of the original scenario.  
Conceptualization, H.T. and G.J.; data curation, H.T.; formal analysis, H.T.; funding acquisition, G.J.; methodology, H.T., G.J. and T.Y.; software, B.C. and X.L.; validation, T.Y.; writing--original draft, H.T.; writing--review and editing, G.J., T.Y., L.Z. and K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Third Xinjiang Scientific Expedition Program (Grant No. 20213 jkk0701) and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19030301).  
Data will be made available on request.  
We would like to thank the editor and anonymous reviewers for their constructive comments and suggestions for improving the manuscript.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.