A Daily Snow Cover Dataset for Central Eurasia During Autumn From 2004 to 2021

作者：Junshan Wang, Baofu Li, Yupeng Li, Lishu Lian, Fangshu Dong, Yanbing Zhu, Mengqiu Ma · 发表于：Geoscience Data Journal · 年份：2025 · DOI：10.1002/gdj3.70017 · 被引用次数：1 · 研究领域：Cryospheric studies and observations、Climate change and permafrost、Arctic and Antarctic ice dynamics

ABSTRACT Snow cover is a crucial component of the global climate system, with cloud cover significantly affecting the accuracy of remote sensing snow products. This dataset, leveraging the MODIS daily snow cover product, was crafted through combining Terra and Aqua, temporal Filter, spatial correlation synthesis, combining MODIS and IMS. It encompasses a detailed snow cover dataset for Central Eurasia (0°–160° E, 40°–80° N) for the autumn months (September to November) from 2004 to 2021. Accuracy validation was conducted using ground monitoring station data, indicating an overall accuracy of 89.48%, with snow cover and terrestrial accuracies at 89.52% and 89.47%, respectively. Overestimation and underestimation errors were 9.65% and 0.87%, with 69.75% of stations reporting overestimation errors below 10% and 85.03% reporting underestimation errors below 5%. The dataset exhibits high accuracy in forests, grassland, croplands and urban construction land, while accuracy is relatively lower in shrubland and barren due to fewer samples and low snow cover. This dataset significantly enhances snow and climate variability research, offering a robust foundation for climate change projections.