## 5 Conclusions  
Firstly, this study conducted a long-term time series analysis of the NDVI in China from 2001 to 2023, comprehensively revealing the long-term dynamic changes in vegetation coverage across various regions of China. It showed that, overall, NDVI data for China exhibited significant seasonal fluctuations and a long-term upward trend during 2001-2023, especially noteworthy is the pronounced growth since 2010, largely attributed to a series of ecological restoration policies implemented by the Chinese government, such as the \"Three-North Shelter Forest\" and \"Grain for Green\". Meanwhile, variations among different regions were observed: NDVI significantly increased in southeastern Henan and northwestern Anhui, while it slightly decreased in northwestern Inner Mongolia and northeastern Heihe, and NDVI notably rose in southern Xiaogan and Qianjiang of Hubei, demonstrating the effectiveness of local conservation measures. Further, through STL decomposition, a long-term series analysis of NDVI across different climatic zones in China revealed complex fluctuations, with seasonal components showing clear annual periodicity. During 2001-2010, NDVI values in TMC and TCC climatic zones significantly declined, mainly due to urbanization, industrialization, and overgrazing, but with the implementation of environmental protection policies, vegetation gradually recovered. In PMC and SMC climatic zones, NDVI first decreased and then increased, whereas in TRMC climatic zones, there was a marked recovery after 2010, highlighting the positive roles of policies, technological innovations, and climate change, leading to significant improvements in vegetation coverage. Moreover, different vegetation types exhibited heterogeneity in NDVI across seasons: The NDVI of forest, cropland, and grassland experienced a decline followed by a rebound in spring, peaking in 2014, and then fluctuating again. In summer, the NDVI first increased, then decreased, and rebounded, reaching two peaks in 2012 and 2021. In autumn, there was an overall trend of first increasing and then fluctuating downward, with particularly sharp changes between 2007 and 2021. In winter, there was no clear trend of increase or decrease, mainly exhibiting a fluctuating pattern. In contrast, the NDVI of shrub vegetation remained relatively stable and showed a continuous upward trend throughout the entire observation period, exhibiting dynamic characteristics distinct from other vegetation types. Additionally, using VIP scores as an important tool for variable selection in PLS models, the relationship between NDVI and meteorological factors was thoroughly examined, indicating that from 2001 to 2023, vegetation growth in China was primarily influenced by SWVL1 (79.22% of areas scored > 0.8), SWR (71.86% of areas scored > 0.8), and TP (74.78% of areas scored > 0.8), albeit CO\({}_{2}\)'s impact was relatively minor, yet its long-term effect remains to be monitored. Secondly, this study fully considered the changes in NDVI under different climate change scenarios. By using ensemble learning methods and CMIP6 multimodel data, it assessed the NDVI changes in China from 2023 to 2100 under four emission scenarios (SSP126, SSP245, SSP370, and SSP85). Results indicate that, under all scenarios, China's NDVI displayed a spatial pattern of lower values in the northwest and higher values in the southeast, primarily constrained by precipitation patterns and climatic conditions. Specifically, as greenhouse gas emissions increased, the national average NDVI also sequentially rose, from 0.3041 under the SSP126 scenario to 0.3682 under the SSP58 scenario, suggesting that despite potential negative impacts brought by global warming, vegetation conditions in certain regions are expected to improve under high-emission scenarios.