1. Introduction
As one of the essential components of the cryosphere [
1,
2,
3], due to its high albedo and low thermal conductivity, snow cover has a substantial impact on the climate system via regulating the surface energy budget, the hydrological cycle, and atmospheric circulation [
4,
5]. The sixth report of the Intergovernmental Panel on Climate Change (IPCC_AR6) stated that the average temperature in the past 10 years is 1.09 °C more than in the late 19th century and further warming has led to seasonal snow cover reduction and earlier snowmelt date. Changes in the snow cover within a range have caused changes in the global climate system [
6]. In addition, the reduction in snow cover may affect soil respiration [
7], which in turn affects food production [
8] and animal migration [
9]. Earlier snowmelt will cause significant changes in the time and amount of snowmelt runoff in spring and may increase peak runoff, which will increase the incidence of disasters, such as floods or droughts [
10]. Therefore, accurate estimation and information on snow cover are essential for evaluating the impacts of climate change and disaster prevention.
Snow cover phenology (SCP) parameters such as snow cover area (SCA), snow cover days (SCD), and snow cover start (SCS) and snow cover melt (SCM) dates can quantify this change in snow cover [
11], and it has become an increasingly valuable indicator of climate change and an important input parameter for climate models [
12]. China is located on Asian east and the Pacific Ocean’s west coast, with a vast territory and complex terrain, and it is the country with the largest snow cover in the middle and low latitudes [
13]. Snowmelt water in China is the source of many large rivers in Asia and plays an important role in the Earth’s climate system [
14,
15]. Therefore, it is of great significance to explore the changes in and distribution of SCP in China [
16]. The attribution of SCP change over the past few decades has also received extensive attention [
17,
18]. Several studies have shown that the distribution of and changes in SCP are susceptible to surface temperature and precipitation [
19,
20,
21] and are greatly affected by topography [
22]. However, in China, most snow cover studies are on small areas. Research on the spatiotemporal characteristics (distribution and variation) of SCP parameters and their sensitivity to meteorological factors (temperature and precipitation) under different conditions (altitude, snow-covered area types, and season) in China is relatively little.
Previous studies have shown that the acquisition of SCP parameters mainly includes two data sources: conventional ground stations and satellite remote sensing observations [
23]. Many scholars have used ground station data to analyze the spatial distribution of snow cover [
24], the trend of snow start and end dates [
25], and the degree of influence of various variables on snow cover [
26]. However, the ground station observation data have the disadvantage of poor spatial integrity, making it challenging to characterize the spatial distribution of snow in the whole region [
27]. With the development of remote sensing technology, this shortcoming has been compensated [
28]. Visible spectral remote sensing (such as Landsat, SPOT, AVHRR, and MODIS snow products) and microwave remote sensing (such as SMMR, SSM/I, AMSE-R, and MWRI snow products) are widely used to obtain snow information and analyze snow cover changes. Some scientists also use products fused from multiple snow cover datasets for analysis [
29]. Although microwave and AVHRR products cover a long time period, their spatial resolution is relatively low. Landsat products have a high spatial resolution but low temporal resolution and are not spatially representative of a large area [
23,
30]. Currently available snow cover products with a high temporal and spatial resolution of long-term series used in the mesoscale are mainly MODIS-related snow products.
Compared with the newly released cloud-free MODIS NDSI dataset (mainly providing NDSI values) [
31] and M*D10A1GL06 product (a snow-and-glaciers-combined product with significantly improved accuracy, mainly for high-mountain Asia,) [
32,
33], as a cloud-free binary snow product with high overall accuracy considering different underlying surfaces using reflectance data, the modified Chinese MODIS daily cloud-gap-filled (CGF) 500 m snow-cover-extent (SCE) dataset (NIEER CGF MODIS SCE) [
34,
35] is more suitable for this study. Furthermore, the validation against 362 China Meteorological Administration (CMA) stations shows that the modified product’s OA is 93.15%, which has increased compared with the MOD10A1F product and the MYD10A1F product by 4 and 9 percentage points, respectively. Both omission error (OE) and commission error (CE) were within 10%, and there was a significant improvement, especially in the forested area. The SCP parameters in MODIS China snow cover phenology data from 2000 to 2020 (NIEER MODIS SCP) [
36,
37] are also high precision, calculated based on the NIEER CGF MODIS SCE product. The validation against CMA stations shows that the root mean square error (RMSE) is within 20 days and the mean absolute error (MAE) is within 8 days for all three snow cover phenology datasets. These improved MODIS CGF snow cover products are beneficial for us to obtain more accurate snow cover information in China.
The objective of this study is to explore the latest terrestrial snow cover phenology changes and respond to the climate in China during 2000–2020. Firstly, the high-precision NIEER CGF MODIS SCE product and the NIEER MODIS SCP product were used to conduct a spatial and temporal analysis of SCP in China during 2000–2020. Secondly, we divided snow cover types according to SCP parameters to explore the characteristics of SCP under different snow types. Finally, using meteorological data (temperature and precipitation) and topographic data, this study discusses the relationship between SCP and climate at different altitudes, snow-covered area types, and seasons. In-depth research and accurate snow cover analysis are expected to provide basic data for climate prediction; agricultural water resources utilization; and information services for warnings about disasters, such as floods and snow disasters.
6. Conclusions
This study analyzes the spatiotemporal characteristics of and change trends in SCP in China from 1 September 2000 to 31 August 2020, based on the NIEER CGF MODIS SCE product. The relationship between SCP, temperature, precipitation, and the influence of altitude are also investigated.
Snow cover distribution is extremely uneven and has apparent spatiotemporal heterogeneity in China. Snowfall mainly starts in autumn, reaches the maximum SCA and SCD in January, and gradually disappears at the end of spring and early summer, except for the perennial snow cover. In the past 20 years, the interannual variation in snow cover in China shows a trend of decreasing SCA, decreasing SCD, advancing SCS, and advancing SCM, and the advance slope of SCM is larger than that of SCS. The snow in China is mainly distributed in northeastern China (including Northeast China and west Inner Mongolia), northern Xinjiang, and the Tibet Plateau. The slope of SCP parameters in China was mainly within ±0.5 d/y. In the statistics of the percentage of SCD, SCS, and SCM changes in the total area of the country, the decreased area (slope < 0) is 17.88%, 6.22%, and 11.02% more than the increased area (slope > 0), respectively. All regions except SFA, SCD, SCS, and SCM are shortened or advanced.
Temperature should be of high concern in SCP analysis compared with precipitation. The correlation of SCP with temperature and precipitation has obvious spatial and seasonal differences. In the main snow cover area, SCD and temperature (spring, autumn, and winter) are negatively correlated in most areas and positively correlated with spring and autumn precipitation, and the areas are similar but opposite in winter. SCS was positively correlated with autumn temperature and negatively correlated with precipitation. SCM was negatively correlated with spring temperature and positively correlated with precipitation. The areas that pass the significance test are mainly in SSA and APA. For SSA, SCD are mainly affected by spring temperature below 1500 m in elevation and are mainly affected by autumn temperature above 1500 m. At different altitudes, SCD are greatly affected by autumn precipitation. At altitudes above and below 3000 m, SCD and winter precipitation show an opposite correlation.
This study explores SCP distribution, changes, and correlations with meteorological and topographic factors in China, providing better results and finer spatial resolution, which contributes to understanding the land surface warming over the past few decades of SCP changes and related climate prediction studies and provides primary support for mountain snow cover.