## 5 Summary and Conclusions  
This paper used the 2000-2019 datasets from vegetation remote sensing products to explore vegetation dynamics in China from different perspectives. It explored the variability of the drivers of vegetation by applying regression equation models and by considering air temperature, precipitation, relative humidity, and nine land use type factors. In addition, the spatial distribution of vegetation remote sensing products in 2020-2025 was predicted using the CA-Markov model. The main findings are as follows:  
1. Throughout the study period, the vegetation indices, NDVI, EVI, and SIF, all exhibited increasing trends. The SIF demonstrated a more direct response to vegetation cover changes and was less influenced by other driving factors. The SIF outperformed the NDVI and EVI in detecting vegetation trend changes, particularly regarding sensitivity.
2. Vegetation cover changes are driven by multiple meteorological factors, such as temperature, precipitation, and relative humidity. These factors exhibited a strong spatial correlation with the distribution of the vegetation remote sensing products. Among these factors, the SIF showed a higher sensitivity to temperature compared to the NDVI and EVI, while the NDVI and EVI displayed greater sensitivity to precipitation and relative humidity.
3. Within the study area, land use types revealed a gradient from northwest to southeast, which is consistent with the spatial distribution of the vegetation remote sensing products. For green vegetation types, the three remote sensing products exhibited varying sensitivity levels, with the SIF demonstrating the highest sensitivity to green vegetation types.
4. Overall, the future vegetation outlook in China is promising, especially in the southeastern regions where significant vegetation improvement trends are evident. However, the vegetation conditions in some northwestern areas remain less favorable, necessitating the reinforcement of ecological construction and improvement measures. Additionally, a significant positive correlation exists between population size, GDP, and vegetation remote sensing products.  
Data curation, Y.H.; formal analysis, Y.H. and Z.C.; investigation, Y.H.; software, Y.H.; writing--original draft, Y.H. and P.Z.; writing--review and editing, Y.H., Y.L. and P.Z.; methodology, Y.L. and P.Z.; resources, Y.L., P.Z. and J.D.; supervision, Y.L., P.Z. and J.D.; funding acquisition, Y.L.; conceptualization, P.Z.; visualization, Z.C., J.W. and K.Y.; validation, J.W. and K.Y.; 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 No. 41761081), the Basic Research Program of Yunnan Province (Grant No. 202201 AU070112), the Kunming University of Science Technology introduced talent research start-up fund project (Grant No. KKZ3202021055), and the Yunnan Province Philosophy and Social Science Planning Project (Grant No. PY202129).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the researchers who have provided the open-source algorithms, which have been extremely helpful to the research in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.