## 4 Discussion  
Overall, the trend values indicated by the four vegetation proxies appeared to show similar change patterns, indicating a strong consistency among different data sources in capturing the temporal variability of vegetation on the QTP. All the data demonstrate that vegetation on the QTP has been greening in the last two decades, as about 70-80% of the total pixels indicated an upward trend [6; 24; 38; 50]. In the context of global warming and increasing emissions of carbon dioxide, vegetation activity on the QTP has enhanced substantially [51]. Carbon dioxide is known to stimulate vegetation growth through the combined effects of the direct physiological effect and the indirect effect of climate change induced by itself [52]. Consequently, these two factors contribute to favorable conditions for vegetation growth on the QTP and result in the greening phenomenon observed. It has to be stressed that EVI, NDVI, and LAI data more or less indicate that vegetation located in the southwestern QTP is browning [50; 51; 53]. Nevertheless, this phenomenon is more predominant, as indicated by the SIF data. Such phenomena arose particular concern for associated vegetation, since SIF is reported to be more correlated with vegetation photosynthesis activity than vegetation index [8; 54]. Moreover, the biodiversity of the QTP will decrease in future if global warming is not curbed [55]. This would make vegetation in the southwestern QTP more vulnerable in the near future and needs special attention. Time-invariant and time-variant vegetation sensitivity derived from LAI data were relatively different when compared with those obtained from the other three vegetation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Solar Radiation**} & \multicolumn{2}{c}{**Water Availability**} \\ \cline{2-7}  & **Trend** & **Sign** & **Trend** & **Sign** & **Trend** & **Sign** \\ \hline Forests & Downward & Positive & Upward & Positive & Upward & No sign \\ Grasslands & Downward & Positive & Downward & Negative & No trend & Positive \\ BSVs & Downward & Positive & Downward & Negative & Upward & Positive \\ \hline \hline \end{tabular} Note: “BSVs” denotes “barren or sparsely vegetated areas”.  
\end{table} 
Table 1: A summary of the trends and corresponding impact directions for vegetation sensitivity in forests, grasslands, and BSVs. The overall trend for vegetation sensitivity was deduced from the averages and medians of the trend values shown in Figure 8. The overall impact direction indicated by the sign of regression coefficients was determined based on Figure 9. Since four vegetation proxies were used, we jointly considered the four results to obtain an overall assessment of the vegetation sensitivity trends and the corresponding impact directions indicated by the signs of regression coefficients. proxies. Pixels with such discrepancies were mainly found in the southeastern QTP, where forests account for a large proportion of the land cover. Although EVI, NDVI, and SIF can well capture the seasonal variability of LAI, they are reported to provide premature estimates of the onset of the growing season in forests [56]. Moreover, the relationship between LAI and vegetation index is found to change with the leaf seasonality, leaf orientation, and growth stage of vegetation cover [57]. These factors may lead to the different performance of LAI in comparison with EVI, NDVI, and SIF. As a vegetation proxy that is drawing more and more attention, SIF has an evaluation performance highly consistent with those of NDVI and EVI. Different from NDVI, EVI, and LAI, SIF is directly linked to photosynthetic activity and has an advantage over vegetation indices in describing plant physiological responses to water stress [54; 58]. In addition, SIF is more sensitive to climate variables than vegetation indices [59] and shows a premature start of season relative to the vegetation index [60].