**5. Discussions**

#### *5.1. Response of Snow Phenology to Climate*

Figure 14 shows the annual variations in the mean temperature and snow phenology from HY2001 to HY2017. Except for the SCOD, there were clear increases in both the SCD and SCED and a decrease in temperature. According to statistics, SCD and SCED were both strongly negatively correlated with the mean temperature; at the 95% confidence level, the correlation coefficients were −0.73 and −0.57, respectively. The correlation with the SCOD was moderate (*r* = 0.41), which can be explained that besides mean temperature, the effect of latitude on SCOD was also grea<sup>t</sup> (*q* = 0.55).

The spatial pattern of correlation between the mean temperature and snow phenology is presented in Figure 15. For 99.91% of the pixels, the mean temperature was negatively correlated with the SCD, which means the lower the mean temperature was, the longer the SCD in the whole study area; 59.40% of the pixels showed strong negative correlations, and areas with weak negative correlation were mainly distributed in the northern Daxingan Mountains and Xiaoxingan Mountains (Figure 15a). The correlations between the SCOD and mean temperature were mainly positive, and the weak positive correlations accounted for a large proportion (40.81%). Areas with strong positive correlations accounted for only 8.19% and were distributed mainly in the Xiaoxingan Mountains and Changbai Mountains (Figure 15b). The correlations between the mean temperature and SCED were similar to those of the SCD, and most regions had negative correlations (93.86%). The difference was that the proportion of strong negative correlations was relatively small (33.06%), and the average correlation of the SCED was −0.38, while that of the SCD was −0.52 (Figure 15c).

**Figure 14.** Annual variations in the mean temperature and snow phenology from HY2001 to HY2017.

**Figure 15.** Spatial pattern of the correlations between the mean temperature and snow phenology. (**a**) SCD, (**b**) SCOD, (**c**) SCED.

#### *5.2. Geographical and Vegetation Controls on Snow Phenology*

To quantitatively investigate the latitudinal zonation of snow phenology further, statistics were analyzed in combination with the underlying surface conditions. The slope and the regularity between snow phenology and latitude in the nonforested and forested areas further quantitatively proved the latitudinal zonation of snow phenology, as shown in Table 4 and Figure 16. In Northeast China, as the latitude increased by 1 degree, the average SCD increased by 10.2 d, the SCOD advanced by 3.82 d and the SCED was delayed by 5.41 d. Generally, the change rate in forested areas was slower. The change rate in the SCD, SCOD and SCED were 5.41 d/degree, −2.02 d/degree and 2.91 d/degree, respectively, which were closely related to the closed environments of the forested areas themselves.


**Table 4.** Linear slope and R<sup>2</sup> of the mean SCD, SCOD, and SCED with latitudes from 39.22◦N to 53.22◦N in the nonforested and forested areas in Northeast China.

Note: The slope represents the change rate in the days when the latitude increased by 1◦ (d/degree).

**Figure 16.** Snow phenology related to latitude in the nonforested and forested areas. (**a**) SCD, (**b**) SCOD, (**c**) SCED.

The higher the latitude was, the longer the SCD, the earlier the SCOD and the later the SCED, in both nonforested and forested areas. At the same latitude, the SCD of forested areas was higher than that of nonforested areas. At high latitudes, the SCOD of forested areas was later than that of nonforested areas. For the SCED, at low latitudes, the snow melting time in forested areas was later than that in nonforested areas, and the SCED was basically the same at high latitudes; these changes can be explained by the observations that snow melting in high-latitude areas was mainly affected by temperature and that the SCED in this region was in approximately April, so the rapidly increasing temperature led to the melting of snow in the forested and nonforested areas.

In contrast to the mean temperature, the NDVI denoting vegetation greenness was positively correlated with the SCD, SCOD and SCED, accounting for 68.69%, 59.22% and 58.68%, respectively. However, the weak correlation ( −0.3 < *r* <0.3) between snow phenology and the NDVI was dominant, with 69.85%, 74.99% and 72.65% of pixels (Figure 17a–c), which illustrates that the NDVI was not the main factor affecting snow phenology.

**Figure 17.** Spatial pattern of correlations between the NDVI and snow phenology. (**a**) SCD, (**b**) SCOD, (**c**) SCED.

#### *5.3. Comparison with Previous Results*

Shi et al. [11] reported strong spatial heterogeneity in snow phenology in the Mollisol region of Northeast China. The SCD increased from southwest to northeast gradually, and snow cover began to accumulate in mid-November and completely melted in late March during 1978–2016. In the western Changbai Mountains and northern Daxingan Mountains, the SCOD always began in early October, and the SCED in the western Daxingan Mountains, Xiaoxingan Mountains, and Changbai Mountains always occurred in May [8]; which was similar to the results of this study. In the forested areas of Northeast China, the SCOD were later, and the SCED were earlier in the plains. The SCOD occurred between late November and mid-November in the regions with high altitudes, and the SCED occurred later with increasing latitude and altitude. The earlier SCOD and later SCED led to an increase in the SCD, especially in high mountain areas. In most areas, the SCOD experienced an advancing trend, the SCED exhibited an obvious delaying trend, and the SCD showed an opposing trend from south to north in Northeast China from 2004 to 2018 [10].

This paper found that SCD and SCED increased, and SCOD basically did not change in Northeast China from 2001 to 2018. However, previous studies on snow phenology in China found that due to the increase in temperature, the SCOD in most areas were delayed, and SCED were advanced [5,6,12]. These contradictions may have been caused by the inconsistency of the time span. Although snow cover will decrease under the background of global warming, the decrease of mean temperature in a short time has led to the increase of snow cover. Studies have shown that there are strong correlations between meteorological and geographic factors and snow cover [39–41]. Vegetation change was also closely related to snow phenology [10,42,43]. Temperature and precipitation could affect snow cover variations [44,45]. However, in this research, the results showed that temperature was the

main factor affecting the variations in snow phenology, and precipitation had little effect. In the three basins of Songhua River in Northeast China, temperature, precipitation and altitude were considered to be the three most important factors [9]. Huang et al. [12] studied the snow cover variations across China from 1951 to 2018 based on snow depth dataset and used model to analyze the driving effect of multiple factors on snow cover phenology and found that the most important factors influencing the SCD, SCOD and SCED were annual coldest monthly minimum temperature, altitude and annual mean temperature, respectively. This difference may have been caused by inconsistencies in time scales, study areas, data and research methods. In addition, we both found that precipitation had little effect on snow phenology.
