## 3 Results  
### Spatial and Temporal Variation of NDVI, EVI, and SIF  
Figure 2 shows the temporal changes of the three vegetation remote sensing products from 2000 to 2019. The material transformations of the NDVI, EVI, and SIF all show an increasing trend. The temporal changes of the NDVI and EVI are similar, but there are significant differences in the SIF, and the fluctuation magnitude is also different. The differences are reflected in the decreasing trend of the NDVI and EVI until 2005 and 2010, respectively, and the increasing trend afterward, while the SIF shows a growing trend. This difference is because the NDVI and EVI are based on greenness and reflect vegetation cover, which is influenced by more drivers. In contrast, the SIF is based on photosynthetic changes that provide feedback on vegetation cover, which is influenced by fewer drivers directly. Therefore, the temporal variation (Figure 2) shows that the fluctuations in the NDVI and EVI are more significant than those in the SIF. It is also evident that the SIF responds more directly to vegetation cover changes and is less influenced by other drivers than the NDVI and EVI in terms of temporal variation. Figure 3A-C shows the spatial changes of the remote sensing products for three vegetation types from 2000 to 2019. The results demonstrate that vegetation in China increases from the northwest to the southeast. This finding further confirms our description of the \"study area\" in the Materials and Methods section. Low values in the northwest (such as in Xinjiang, Tibet, and Qinghai) and high values in the south-central and coastal areas (Yunnan, Guangxi, Guangdong, etc.) indicate that the main factors affecting vegetation growth in different regions vary. The natural environment in the northwest primarily affects foliage, while human factors in the southeast mainly influence vegetation. We also found that the differences between the NDVI and EVI mainly exist at high and low values, the differences between the NDVI and SIF primarily exist at high values, and the differences between the EVI and SIF mainly exist at low and high values. This difference demonstrates that the response of the NDVI and EVI to extreme vegetation cover is inferior to that of the SIF and further supports the necessity of selecting multiple vegetation remote sensing products to explore dynamic changes in vegetation in China. Figure 3D-F shows the spatial trends of the NDVI, EVI, and SIF regression equations. The spatial direction of China's vegetation changes from northwest to southeast, which is consistent with the spatial distribution of vegetation. The differences between the NDVI, EVI, and SIF are also evident in the northwest and southeast. There are some differences in the spatial trends of the NDVI and EVI, and there are significant differences between the NDVI, EVI, and SIF. The differences between the NDVI and EVI are significantly improved in the region, whereas the differences between the NDVI and SIF are in slightly degraded areas. The differences between the EVI and SIF are in slightly soil areas and significantly  
Figure 2: Line chart of the time change of China’s annual average NDVI, EVI, and SIF from 2000 to 2019. (**A**): Time variation of NDVI; (**B**): Time variation of EVI; (**C**): Time variation of SIF. improved areas. These differences are due to the distinct responses of different vegetation remote sensing products to the changes in spatial vegetation trends. The traditional vegetation indices, NDVI and EVI, have a poor reaction to spatial distribution and an inadequate response to the shift in the spatial direction. This also further proves that the SIF, as a direct probe of photosynthesis, is sensitive to changes in spatial vegetation trends. Figure 3G-I shows the spatial significance of the NDVI, EVI, and SIF. In most parts of China, the NDVI, EVI, and SIF are significant and still exhibit an increase from the northwest to the southeast.