Reliable cloud masks in Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have a high potential to improve the retrieval of snow properties. However, cloud–snow confusion is a popular problem in MODIS snow cover products, especially in boreal forest areas. A large amount of forest snow is misclassified as clouds because of the low normalized difference snow index (NDSI), and excessive cloud masks limit the application of snow products. In addition, ice clouds are easily misclassified as snow due to their similar spectral characteristics, which leads to snow commission errors. In this paper, we quantitatively evaluated the cloud–snow confusion in Northeast China and found that snow-covered forests and transition zones from snow-covered to snow-free areas are prone to being misclassified as clouds, while clouds are less likely to be misclassified as snow. A temporal-sequence cloud–snow-distinguishing algorithm based on the high-frequency observation characteristics of the Himawarri-8 geostationary meteorological satellite is proposed. In the temporal-sequence images acquired from that satellite, the NDSI variance in cloud pixels should be greater than that of snow because clouds vary over time, while snow is relatively stable. In the MODIS snow cover products, the cloud pixels with NDSI variance lower than a threshold are identified as cloud-free areas and attributed their raw NDSI value, while the snow pixels with NDSI variance greater than the threshold are marked as clouds. We applied this method to MOD10A1 C6 in Northeast China. The results showed that the excessive cloud masks were greatly eliminated, and the new cloud mask was in good agreement with the real cloud distribution. At the same time, some possible ice clouds which had been misclassified as snow for their spectral characteristics similar to those of snow were identified correctly.

Abstract. Snow phenology, recurrent seasonal patterns in snow cover and snowfall, has been significantly affected by global warming. Through the interaction with the climate, the dynamic variability of snow phenology affects the regional climate environment, vegetation ecosystem, soil properties, agricultural water resources, snow disasters and animal migration. First, this study compares the advantages, disadvantages and applicability of different sources of observation data and the principal research methods involved in studying snow phenology. Then, this work discusses the spatiotemporal variability and changing trends of snow phenology in the Northern Hemisphere, and summarizes the relationship between climate, vegetation and snow phenology. Finally, this review highlights the key areas related to snow phenology that require further study. Overall, during the past 50 years in the Northern Hemisphere, the snow cover end date (SCED) has shown a significantly advanced trend, the snow cover onset date (SCOD) has also been occurring slowly earlier, and the snow cover days (SCD) has shortened, but these two trends are not significant. The snow phenology variation is closely related to climate factors, atmospheric circulation, vegetation status and some spatial factors. Snow cover impacts climate change through interactions with atmospheric circulation systems. The rise in temperature will delay the SCOD, and the SCED is closely related to the temperature of the snowmelt season. The interaction between seasonal snow cover and climate will either stimulate or impede vegetation growth. With the change in snow cover, especially the decrease in snow cover in the melting stage can impact the climate change, the rise in temperature will change the growth conditions and extend the vegetation growth season. The relationship between snow cover and vegetation is inconsistent in different elevations and latitudes. Snow phenology variation is very complex and is the result of the combined action of many factors. Additionally, snow phenology will also have a great impact on the cryosphere. Therefore, we must understand snow phenology variation and prepare for future changes.