## 4 Discussion  
Temporal and Spatial Distribution of Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Sun/Solar-Induced Chlorophyll Fluorescence  
Examining changes in vegetation dynamics in China is crucial for enhancing ecological vulnerability assessments, particularly in the context of climate change [21; 22; 23]. In this study, we considered not only the NDVI and EVI, which primarily represent structural information such as vegetation cover and greenness, but also the SIF, which accurately reflects the physiological activities of vegetation. This approach allowed us to comprehensively investigate the dynamic changes in Chinese vegetation from multiple perspectives. Evidence suggests that vegetation cover in China has increased over the past two decades (Figures 2 and 3), which is consistent with the findings of previous related studies [24; 25]. Our results indicated that, according to the NDVI (as well as the EVI and SIF), China's vegetation area increased by approximately 13.3% to 26.5%. Most of the upward trend occurred in the southeast and east-central regions. We observed that the spatial distribution pattern of the NDVI (as well as the EVI and SIF) trends aligned with the overall spatial distribution pattern. Moreover, we found significant differences in the NDVI, EVI, and SIF trends in southeastern Tibet and western Sichuan, where the NDVI and EVI declined while the SIF improved. Unlike the vegetation degradation in northwest China caused by harsh natural environments, the NDVI and EVI degradation in southeastern Tibet and western Sichuan may be attributed to the influence of human activities. As a result, the greenness-based vegetation indices, NDVI and EVI, provide more direct feedback on the impact of human activities, and the vegetation response is influenced by a greater number of drivers (Figure 5). northwest and northeast China. The degradation of vegetation in certain areas of northeast China might be related to the rapid decline in air temperature and precipitation since 2000 [26], while the arid climate in northwest China, which is situated on a plateau, could worsen water shortages in some regions, limiting vegetation growth [27]. Moreover, land use type changes positively influence vegetation recovery in specific areas [28]. On a regional scale, Shandong, Henan, and Hebei provinces, which are central agricultural regions in China, exhibited a slight enhancement in vegetation cover. These regions have transitioned from \"nomadic\" to \"semi-agricultural and semi-pastoral\" areas, with some nomadic areas being converted into arable land. However, more arable land has also been transformed concurrently. Prior studies have shown that more arable land has been converted into land with natural attributes instead of being restored with social features, primarily due to reforestation, grass restoration, and urban expansion [29]. We also observed differences in the NDVI, EVI, and SIF responses based on the woodland land use type. The SIF demonstrated a weaker correlation with forests than the NDVI and a stronger correlation than the EVI. We attribute this to the diversity of woodland styles (including non-green vegetation) causing some discrepancy in the sensitivity between the NDVI and woodland. Concurrently, the EVI is a vegetation index product based on the NDVI that was improved for forests. Therefore, the impacts of climate change and land use type complexities have a crucial effect on the spatial distribution of the NDVI, EVI, and SIF changes in Chinese vegetation. It is also essential to mention that the NDVI, EVI, and SIF have strong correlations with population size and even stronger correlations with GDP. The influence of population density on vegetation is predominant in densely populated regions, cities, and peri-urban areas. China's balanced population growth exceeded 20% over the last 20 years.