## 1 Introduction
In China, studies on vegetation changes have particularly focused on ecological reserves or ecologically vulnerable areas. Research mainly concentrates on regions such as the southwestern area [35; 36; 37], northern regions [38; 39], coastal zones [40; 41], the middle and upper reaches of the Yellow River [42], the Loess Plateau [43], the Ruoergai Wetland [44], and the Sanjiangyuan National Conservation Area [45]. However, most current studies focus on vegetation changes in specific regions or types of ecosystems, and there is a lack of comprehensive analysis of vegetation dynamics across China as a whole or across different regions. Climate is one of the most important factors influencing vegetation condition. [PERSON] et al. reported the trends of the annual FVCmax and FVCmean datasets over the past 30 years using the Mann-Kendall method and Sen's slope estimator and suggested that the primary reason for the significant increase in NDVI in most regions is global warming [46]. As global temperatures rise, the atmospheric water vapor content increases, leading to greater precipitation [47], which in turn contributes to an increase in regional NDVI. [PERSON] et al. found that temperature is the climatic factor driving vegetation changes in southern regions [48], whereas precipitation regulates vegetation growth changes in northern areas. [PERSON] et al. established a significant positive correlation between plateau vegetation and surface heat sources on the plateau [49]. [PERSON] et al. used land surface models CLM4.5-CNDV to study the feedback of plateau vegetation to future climate change [50], finding that an increase in plateau vegetation cover shows good consistency with changes in precipitation and soil conditions. Surface water cycle components (precipitation) may become the most important climatic factor limiting vegetation growth. [PERSON] et al. confirmed that seasonal differences exist in the effect of climate change on vegetation growth within the Yellow River's source conservation area [51]. It is evident from the existing literature that there is a significant correlation between NDVI values and climate change in relevant studies. However, current research often focuses on the impact of temperature and precipitation, while giving insufficient consideration to other potential factors. Accurately predicting NDVI is crucial for guiding regional ecological restoration and environmental management. Machine learning models, which have become popular methods today, are capable of modeling highly complex nonlinear relationships, unlike traditional statistical methods. [PERSON] et al. proposed a convolutional neural network model combined with feature engineering, which effectively improved the accuracy of NDVI predictions for the next three months in multiple complex regions [52]. [PERSON] et al. developed a combined prediction model based on three independent prediction models in order to improve the performance of NDVI predictions for the Yellow River Basin in China [53]. [PERSON] et al. developed an innovative NDVI prediction model and conducted an in-depth study on the response mechanism of NDVI to climate factors [54]. Although these studies have successfully predicted future NDVI values, they have largely failed to adequately consider the variations in NDVI under different climate change scenarios. In order to address the shortcomings of previous studies, this research makes contributions in the following aspects. Firstly, this study will conduct a long-term time series analysis of NDVI in China over nearly 20 years, providing a detailed analysis of spatial and temporal variation patterns, revealing its long-term trends, and exploring its relationship with multiple important meteorological factors. Secondly, a stacking model will be employed, using Random Forest, XGBoost, and Support Vector Regression (SVR) as base models, with a linear regression model serving as the meta-model to determine the optimal weighting of these base models.