**1. Introduction**

Northeast China, one of the three major areas with snow cover in China [1,2], is an important agricultural production base. Snow cover can affect the scale and yield of agriculture by changing soil moisture, insultation from deep frost and prevention of microbial decomposition of organic matter. In addition, Snow accumulation and melting are important for storing and releasing water [3,4]. Snow phenology can describe seasonal snow cover variations and is also used to study the relationship between snow cover and climate change; parameters include the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) [5,6]. According to the statistics, snow phenology generally has regular interannual variations [7]. Therefore, assessing snow phenology and its driving factors in Northeast China is essential for water resource managemen<sup>t</sup> and agricultural development in this region.

However, in contrast with research on large-scale snow phenology, systematic studies of snow phenology in the region have been limited thus far. Chen et al. [8] used the MODIS snow products across Northeast China to study the spatiotemporal variations in snow cover. Ding and Gao [4] studied the SCD in Northeast China based on meteorological

**Citation:** Guo, H.; Wang, X.; Guo, Z.; Chen, S. Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China. *Remote Sens.* **2022**, *14*, 262. https://doi.org/ 10.3390/rs14020262

Academic Editor: Alexander Kokhanovsky

Received: 2 November 2021 Accepted: 4 January 2022 Published: 7 January 2022

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station data. Yang et al. [9] used MODIS data to explore only space-time distributions of SCD. Qiao et al. [10] used MODIS data to investigate the variations in snow phenology and their impact on vegetation growth in forested areas, which occupies 40% of the total area. Shi et al. [11] discussed the SCOD and SCED in the Mollisol areas across the northeastern plains of China based on snow depth data. In addition, Huang et al., Ke et al., and Ma et al. [5,6,12] used meteorological station data to analyze the snow phenology changes in snow-covered areas of China, including Northeast China.

When studying snow phenology based on meteorological observation stations, the stations give snow depth data for only the surrounding area [13]. Moreover, due to the scarcity and uneven distribution of stations, there are large discontinuities in the spatial distribution of the obtained snow phenology data, especially in forested areas and alpine regions [5,14–16]. The snow depth data retrieved by passive microwaves have a long time series, but the resolution is low [17], which is not appropriate for local regional research. The same is true of the Northern Hemisphere snow cover extent (NHSCE), which is more suitable for studying large-scale snow cover variations [18,19]. In contrast, MODIS data have not only higher spatial resolution but also higher temporal and spectral resolutions [20]. Therefore, MODIS is an ideal data source for studying continuous snow phenology, whether at the global or regional scale. For cloud contamination, many scholars have also studied and recovered data under clouds through a series of methods and achieved high cloud removal accuracy [21–26].

Based on the above situation, we analyzed the snow phenology of Northeast China from 2001 to 2018 in this study using the MODIS snow product. First, the daily cloud-free snow products were obtained through the conditional probability interpolation method based on a space-time cube, and accuracy was verified by ground observations. Based on this work, we explored the spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the NDVI, and we then discussed the roles of major factors in driving snow phenology.
