## 3 Results
### Analysis of Annual and Intra-Annual LAI Fluctuations in Different Vegetation Types  
According to the results presented in Figures 7 and 8, the STL method is more effective in reflecting vegetation function specificity. The goodness of fit in Figure 7 indicates a high overall goodness of fit, except for two types of needleleaf forest vegetation. For the same vegetation, the greening speed generally increases and then decreases with an increase in the corresponding CVFZ vegetation category, and the slope reaches its peak at around 4-6 types, while the starting value of annual fluctuations shows an increasing trend of volatility. The greening speed of grasslands varies by up to 8 times from 2.08 \(\times\) 10\({}^{-3}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) to 1.65 \(\times\) 10\({}^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) (Figure 7a). The starting value of the annual fluctuation of LAI in shrublands differs by up to 6 from 0.20 m\({}^{2}\)m\({}^{-2}\) to 1.18 m\({}^{2}\)m\({}^{-2}\) (Figure 7b). Figure 6: The performance of CVFZ in distinguishing LAI time series, 25 June 2000 to 25 June 2022. The legend’s color scheme is consistent with Figure 3.  
Among the different vegetation, the initiation of LAI annual fluctuation was the largest in evergreen broadleaf forests and the smallest in grasslands, at 2.95 m\({}^{2}\)m\({}^{-2}\) and 0.34 m\({}^{2}\)m\({}^{-2}\), respectively (Figure 7e). On average, all vegetation in subregions of MCD-CVFZ showed varying degrees of greening. Savanna subregions had the fastest greening speed between 9.87 \(\times\) 10\({}^{-3}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) and 2.34 \(\times\) 10\({}^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\), while shrub-land subregions had the slowest greening speed between 9.02 \(\times\) 10\({}^{-4}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) and 6.65 \(\times\) 10\({}^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\). Figure 8 illustrates that for the same type of vegetation, the CVFZ is capable of distinguishing growth characteristics and phenological cycles of vegetation. A higher type of CVFZ generally corresponds to a lagging phenological cycle and sometimes even an increase in the phenological cycle. For instance, the vegetation types 10, 11, 12, 13, and 15 in  
Figure 7: LAI time series and linear regression results of annual fluctuations, 25 June 2000 to 25 June 2022. The legend’s color scheme is consistent with Figure 3.  
Figure 8a represent grasslands with a phenological cycle that reaches its peak greenness on Days 200 to 230, while 14, 16, 17, and 18 indicate grasslands with two phenological cycles, and their greenness reaches a second peak on Days 210 to 240. Figure 8b shows shrublands of vegetation type 20 with a not-so-obvious phenological cycle, and 21, 23, and 25 represent shrublands with one phenological cycle. The same trend is observed in broadleaf crops, where the vegetation types 30, 31, and 32 indicate crops with a phenological cycle, whose greenness peaks on Days 210 to 225, while 34, 36, and 39 represent crops with two phenological cycles, and their greenness reaches a second peak at around Day 225 (Figure 8c). Regarding the main phenological cycle, deciduous broadleaf forests have the earliest peak greenness, which occurs at approximately Days 150 to 250 (Figure 8f), which is close  
Figure 8: LAI time series of intra-annual fluctuations, 25 June 2000 to 25 June 2022. The legend’s color scheme is consistent with Figure 3.  
to that of shrublands with a phenological cycle (Figure 8b), while broadleaf crops have the latest peak greenness, which occurs at approximately Days 210 to 250 (Figure 8c). Peak greenness in deciduous needleleaf forests occurs from Days 190 to 250, and the duration period of greenness peaks is shorter and later than that of deciduous broadleaf forests (Figure 8h).