**5. Conclusions**

The snow season and vegetation phenological indicators in the Qilian Mountains in the northeastern Qinghai–Tibet Plateau were investigated, and their corresponding relationships were analyzed. In this study, we concluded that snow seasonality metrics have distinct spatial distribution characteristics. The snow season started earlier and lasted longer in the central part of the study area. The LSP metrics varied significantly with elevation and most vegetation growing seasons shortened with elevation. The asymmetry of significant correlation between snow seasonality and LSP metrics indicates the main direction of influence. A more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) may trigger a more flourishing vegetation growing season the following year (earlier start and later end of growing season, longer growing season).

The NDPI we used is less affected by spring snowpack. We set thresholds to remove nonseasonal vegetation and delineated more detailed elevation gradients. We described the effect of QLMA snow seasonality as a curve that varies with elevation. Below 3300 m, later first snowfall leads to an earlier growing season and also extends the growing season length, while the effect of first snowfall above 3300 m is reversed. The intensity of the effect of LSD fluctuates with elevation but does not reverse. The effects of SSL and SCD on LSP are small and insignificant below 3500 m, and their increase mainly benefits the extended growing season of high-elevation vegetation. In addition, the sensitivity of LSP metrics to snow seasonality varies among vegetation types. Our research provides more evidence that the impact of snow varies with elevation and underlying vegetation types.

Hydrothermal conditions, changes in temperature and precipitation, extreme weather events and glacial melt are important factors influencing land surface phenology at high elevations and should be investigated in future studies in conjunction with high-resolution data to develop improved models for analyzing them.

**Author Contributions:** Conceptualization, W.Z., K.Y. and Y.L.; methodology, Y.L. and K.Y.; software, validation, Y.L. and K.Y.; writing—original draft preparation and visualization, Y.L.; writing—review and editing, X.M. and S.G. funding acquisition, W.Z. and K.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, gran<sup>t</sup> number 41977415, and the Fundamental Research Funds for the Central Universities, gran<sup>t</sup> number 265QZ2022001.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We express our gratitude to anonymous reviewers and editors for their professional comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.
