## 4 Discussion
### Investigation of the Mechanism Responsible for the Effect of Snow Cover on Vegetation  
Snowpack affects vegetation in two ways: on the one hand, it affects soil temperature, which is the temperature effect; on the other hand, it affects soil moisture, which is the water effect. These two effects affect vegetation phenology, which ultimately affects NDVI or GPP (Gross Primary Productivity). At the same time, there are many parameters of snowpack that are affected not only by snow depth but also by snow area, snow phenology, and snow water equivalent [35]. The relationship between snow cover and vegetation is complex, and snow cover often acts on vegetation through intermediate links to influence the phenology of vegetation; thus, the effect of snow cover is indirect. Generally speaking, snow cover affects vegetation by influencing other variables, such as soil temperature, soil moisture, and surface temperature, thereby affecting the phenology of vegetation [13]. In order to clarify the positive and negative feedback relationships between the snow cover and vegetation on the QTP in a systematic manner, we explored the mechanism that allows snow cover to influence vegetation. Based on the results obtained by correlation analysis and lag analysis, we can clarify the main factors that allow snow to affect the phenology of vegetation on the plateau and determine the relationships between factors with direct and indirect effects. We can also quantitatively analyze the specific effects of the snow/soil temperature and humidity/ground temperature on the vegetation rejuvenation period. A structural equation model will be constructed to explore the relationship between the phenology of vegetation in response to changes in snow cover. Thus, it will be possible to further clarify the factors that might determine the effects of snow cover on vegetation on the QTP, particularly the phenology of vegetation, to better understand the relationship between snow cover and vegetation. This will provide data references and support scientific decision-making directions for exploring environmental changes on the QTP under climate change scenarios, as well as for the future development of plateau animal husbandry and the protection and restoration of the ecosystem.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Mean** & **PRE** & & & **TMP** & & & **RAD** & \\  & **SD** & **R\({}^{2}\)** & **Mean** & **SD** & **R\({}^{2}\)** & **Mean** & **SD** & **R\({}^{2}\)** \\ \hline NDVI & 1.13 & 1.00 & 0.2 & 1.52 & 0.93 & 0.42 & 2.60 & 0.62 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lag length (months) of the NDVI to SND (snow depth), PRE (precipitation), TEM (temperature), and RAD (solar radiation).  
Meanwhile, the data and methods employed in this study may also have potential limitations. For instance, the change in snow cover in this study refers to snow depth, which is the primary characteristic and indicator of snow cover, but it is not the sole element reflecting snow conditions. Snow area, snow onset date, snow end date, and snow duration can all serve as supplementary indicators for further detailed research. Additionally, this study uses NDVI as an indicator of vegetation growth, but it can be substituted with NPP (Net Primary Productivity) or the green-up period of vegetation. Establishing a correlation study based on dates, such as the correlation between the snow end date and the green-up period of vegetation, could also provide a better explanation for the effects of snow cover on vegetation.