## 3 Results  
### Interannual Vegetation Dynamics Based on LAI, EVI, NDVI, and SIF Products  
This section explores interannual vegetation dynamics of the QTP using LAI, EVI, NDVI, and SIF products. Figure 2 gives an overall picture of the temporal variability of vegetation dynamics for three land-cover types, while Figure 3 presents associated spatial patterns of the vegetation dynamic trends.  
Figure 2: Temporal averages of interannual **(a)** LAI, **(b)** EVI, **(c)** NDVI, and **(d)** SIF values classified by forests (the green lines), grasslands (the red lines), and barren or sparsely vegetated (BSV) areas (the brown lines). The linear fit line along with its regression equation are presented for each case. The symbol “\(\ast\)” denotes that the regression coefficient passed the significance test (\(\alpha<0.05\)).  
Figure 2 demonstrates that the temporal variability of the vegetation dynamics indicated by the four vegetation proxies was similar for the three land-cover types. Overall, all three land-cover types exhibited an upward trend regarding the temporal variability of vegetation dynamics. Only one case exhibited a significant downward trend, i.e., the BSV case described by EVI data, shown in Figure 2b. The trends for all cases were statistically significant except for one case, i.e., the BSV case indicated by SIF data. In general, forests appeared to have the largest upward trend, followed by grasslands and BSVs. It is noticeable in Figure 2d that SIF data appeared to increase substantially in the year 2020. Keep in mind that the SIF data ended at 31 July 2020 and that, consequently, the temporal average was relatively higher than the SIF averages for other years.  
Overall, the spatial patterns were similar for the temporal trends of vegetation dynamics indicated by the four vegetation products. Generally, upward trends were observed in 69%, 77%, 79%, and 81% of the total pixels for the EVI, SIF, LAI, and NDVI datasets, respectively. For EVI, NDVI, and SIF data, the upward trends gradually decreased from the eastern to western QTP, and pixels with negative trends were mostly distributed in the southern and southwestern areas of the QTP (Figure 3a-c). By contrast, pixels with negative trends were sparsely distributed in the central to southern areas of the QTP for LAI data (Figure 3d). It is worth mentioning that grasslands located in the southwestern QTP experienced more or less decreasing trends, especially for the results indicated by the SIF data in Figure 3d.  
In general, the dotted lines in Figure 4 demonstrate that temperature exhibited an increasing trend, while solar radiation presented a decreasing trend over 2000-2021 on the QTP. The regression coefficients of the fit lines were significant for temperature (Figure 4d) and solar radiation (Figure 4f). By contrast, water availability displayed an insignificant decreasing trend, shown in Figure 4e. Spatially, pixels with significant increasing trend values were clustered in the central to southern QTP for temperature. For solar radiation, as shown in Figure 4c, an insignificant increasing trend was found in western areas, northern areas, and southeastern areas of the QTP. Compared with temperature and solar radiation, water availability exhibited a rather scattered spatial distribution, and no explicit spatial pattern was observed in Figure 4b.  
Figure 3: Spatial distribution of the trend values for (**a**) LAI, (**b**) EVI, (**c**) NDVI, and (**d**) SIF data on the QTP. The black dots denote trend values of corresponding areas that passed the significance test (\(\alpha<0.1\)).