**6. Conclusions**

Considering the difficulty in assessing the effect of snow cover on SOS detection, this study investigated the effect of snow cover on both VI and SOS detection by combining simulation experiments and real satellite data, aiming to determine how snow affects the different VIs and the subsequent SOS detection and how different VIs perform in capturing the SOS for alpine grasslands on the Tibetan Plateau. Five VIs, including NDVI, EVI2, NDPI, NDGI, and NIRv, were used for SOS detection, and their performance was compared.

Based on the simulation experiments, we found that the presence of snow, even at a low SCF, can significantly reduce the values of the five VIs and increase the local gradient of the growth curve, allowing the SOS to be detected. Thus, the bias in the detected SOS due to snow cover depends on both snow phenological parameters (i.e., ESS and SCDc) and the snow-free SOS. An earlier ESS results in an earlier estimate of SOS, while a later ESS results in a later estimate of SOS, and an ESS close to the snow-free SOS results in small bias in the detected SOS.

The analysis from satellite data showed consistent results with those from the simulations. The presence of snow especially reduced the minimum VI values over time, and the detected SOS within the same elevation zone varied with snow parameters such as SCDc and ESS. Generally, an earlier ESS led to an earlier estimate of SOS, while a later ESS led to a later estimate of SOS.

The sensitivity of the five VIs to snow cover in SOS detection is NDPI/NDGI < NIRv < EVI2 < NDVI, which has been tested in both simulation experiments and satellite data analysis. For SOS detection with winter snow cover, NDPI, NDGI, and the physiologicalbased NIRv were rather stable, while NDVI and EVI2 were easily and heavily affected by snow cover. However, the performance of a specific VI in SOS detection also depends on snow phenology parameters such as SCDc and ESS.

These findings will significantly advance our research on the feedback mechanisms between vegetation, snow, and climate change for alpine ecosystems.

**Author Contributions:** Conceptualization, Y.W.; Formal analysis, Y.W. and Y.C.; Funding acquisition, X.Z.; Investigation, Y.W. and P.L.; Methodology, Y.W. and P.L.; Resources, Y.W.; Validation, Y.C.; Visualization, Y.C. and R.Z.; Writing—original draft, Y.W. and Y.C.; Writing—review and editing, Y.W., Y.C., P.L., Y.Z., and B.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 (NSFC), grant number 41901301; The Open Research Fund Program of State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, grant number 2020KFKT-7; and The Natural Science Foundation of Shaanxi Province, grant number 2020JQ-739.

**Data Availability Statement:** Publicly available datasets were analyzed in this study. Satellite reflectance data can be found here: [LP DAAC, http://lpdaac.usgs.gov]; snow cover data can be found here: [NSIDC, http://nsidc.org]; land cover type data can be found here: [http://data.ess. tsinghua.edu.cn]; and DEM data can be found here: [http://www.gscloud.cn].

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

#### **References**

