*5.3. Performance of Five VIs under Snow Conditions*

Our study showed that the effects of snow cover on the five VIs were NDPI < NDGI < NIRv < EVI2 < NDVI. For SOS detection, NDPI and NDGI were rather stable even with winter snow cover, which verified their abilities to minimize the effect of snow cover for alpine grasslands, such as those also found for the American prairie [65]. Our study further revealed the variations in the ΔSOS with snow phenology parameters for NDPI and NDGI. For short and medium snow (i.e., SCDc ≤ 64), preseason snow ending prior to the snow-free SOS caused insignificant biases in the SOS detected by NDPI and NDGI. For long snow (i.e., SCDc ≥ 96) or late snow that ends far later than the snow-free SOS, the biases in the SOS detected by NDPI and NDGI are significant. These findings increase our knowledge about the specific conditions under which the NDPI and NDGI are reliable for SOS detection with snow cover. The traditional NDVI and EVI2 are easily and heavily affected by snow cover. Either an early or a late snow season can cause significant bias in the detected SOS. The physiological-based NIRv could derive SOS dates highly consistent with those detected by NDVI and EVI2 and was less sensitive to snow cover than NDVI and EVI2, indicating its great potential for phenological detection in alpine grasslands. These findings with respect to the performance of different VIs under snow conditions were consistent with the study of Yang et al. [21].

#### *5.4. Limitations and Future Improvements*

There are several state-of-the-art methods to smooth the temporal profiles of VIs and extract phenological metrics [11,52,66–68]. Only one of them was used in this study. More methods can be used and evaluated in further studies. However, we assumed that the conclusion can hold for other SOS detection method because the affecting mechanism of snow in increasing the local gradient of the growth curve is still valid. A previous study using the derivative-based method for SOS detection achieved similar conclusions that snow cover would advance the SOS, but prolonged snow duration would delay the SOS date [38].

The design of the simulation experiments made a series of simplifications of the actual situation. One major simplification is that light cannot penetrate the snow layer. This assumption is representative of most cases with a thick snow layer but may not apply to thin snow layers. However, thin snow can melt or form into a thick snow layer quickly, which will cause negligible effects on the time curves of the VIs. This assumption is thus reasonable, yet further studies can consider the case of temporary thin snow. The other major simplification is that we only considered the presence or absence of snow cover without considering the snowmelt process. Snowmelt can also last for several days and affect vegetation phenology [39,69]. Although such simplification would cause a sharp increase in the time curve of the VIs at the ESS, temporal smoothing performed prior to the SOS detection can locally smooth the VI temporal curve and remedy the problem. In addition, the simulation experiments showed that a small SCF, such as 25%, can cause a large reduction in the VI value. Although the snowmelt process leads to a gradual decrease in SCF, the largest increase in the VI value is expected to be at the stage when SCF decreases from 25% to 0%, which is a relatively short time interval. Therefore, the presence and immediate melting of snow in our simulation experiments is reasonable, and more complex situations can be considered in future work.

For the debate on whether NDVI-based spring phenology trends are overestimated on the Tibetan Plateau [6,7,30,70], we suggest that snow phenology, particularly ESS, should be given much attention in related studies. Normally, NDPI, NDGI, and NIRv would be less affected by snow cover, and their performance in detecting long-term phenology trends can be further investigated. Based on the findings in this study, we also expect to decouple the effect of snow cover on satellite signals and on vegetation physiological phenology, which will enhance our understanding of vegetation–climate feedbacks.
