## 1 Introduction  
Vegetation, acting as a critical interface between the atmosphere and the lithosphere, is an indispensable component of terrestrial ecosystems. It plays a pivotal role in regulating the global carbon cycle, mitigating greenhouse gas effects, protecting water resources,and reducing diurnal temperature variation [1; 2; 3; 4]. Through the process of photosynthesis, vegetation converts solar energy into chemical energy, serving as an energy source for nearly all terrestrial life while absorbing carbon dioxide and releasing oxygen, thereby maintaining the balance of these two gases in the atmosphere [5; 6; 7; 8; 9]. This process is not only essential for modulating global climate patterns but also directly impacts air quality by helping to mitigate the health risks associated with air pollution [10]. As a result of long-term interactions between climatic conditions, human activities, and topographic features, vegetation ecosystems provide valuable indicative information [11; 12; 13; 14]. Given the increasingly significant impact of climate change on vegetation development, monitoring these changes has become a crucial area of focus [15]. Advancements in remote sensing technology have opened new avenues for the dynamic monitoring of vegetation. As this technology continues to evolve, an increasing number of researchers are utilizing satellite remote sensing data to track and analyze patterns of vegetation change. This approach is characterized by ease of acquisition, broad coverage, spatial continuity, and a reduction in the impact of topography on observational results compared with traditional methods, thereby significantly advancing in-depth studies in Earth sciences [16; 17]. Among the various technical indices used to assess vegetation status, the normalized difference vegetation index (NDVI) stands out due to its strong correlation with vegetation physiological characteristics. NDVI is a critical metric for gauging the condition and extent of vegetation growth [18; 19; 20], commonly employed to reflect aspects such as vegetation cover, type, and leaf area index (LAI), which are quantitative features of vegetation. Research by [PERSON] et al. has shown that NDVI is strongly correlated with net primary productivity (NPP) [21], photosynthetic capacity, and LAI, effectively revealing changes in vegetation growth. As a simple yet powerful tool, NDVI bridges the physical properties of the Earth's surface with biological processes, holding irreplaceable importance in ecology, agriculture, forestry, and environmental science [22; 23; 24]. It effectively monitors vegetation health and reflects changes in the growth cycle, finding broad application in crop management, forest resource assessment, land use planning, and environmental protection [25; 26]. NDVI serves as a key parameter for measuring vegetation cover and density, supporting agricultural production optimization, forest management, and responses to natural disasters [27; 28]. As a sensitive indicator of climate change, NDVI reveals patterns of vegetation response to variations in climatic factors, providing crucial evidence for understanding past climate changes and predicting future trends [29; 30]. Due to its universal applicability and ease of acquisition, NDVI has become an essential tool for assessing vegetation status [31; 32]. Given these advantages, NDVI stands out as the most applicable index for evaluating vegetation dynamics [33]. Through its application, scientists can achieve a more precise understanding of the spatiotemporal distribution of vegetation and its responses to environmental changes. In recent years, scientists have conducted long-term sequence analysis using NDVI. Research conducted globally indicates that the NDVI has shown an overall upward trend. [PERSON] et al. demonstrated [34], based on a global NDVI dataset from 1982 to 2011, that except for Oceania, NDVI significantly increased across all other continents.