Daily Fractional Snow Cover (FSC) Data set over High Asia

High Asia is Asian high altitude area with the Tibetan Plateau as the main area, and is also an important distribution area of low and middle latitude mountain snow. The dynamic changes of snow in this area have important effects on water, energy balance and regional climate. As the seasonal snow in the Tibetan Plateau has the characteristics of short storage time and thin snow cover, it is urgent for the understanding of water circulation to monitor the snow cover on day time scales. The dataset is based on MODIS normalized snow index data with the spatial resolution of 500 meters, combing with the terrain data and advantages of a variety of snow cover estimation algorithm under the cloud cover, gradually realizes re-estimation of snow cover under the conditions of cloud cover, meets the need of cloud under 10％ and finally produces “Daily Fractional Snow Cover (FSC) Data set over High Asia (2002–2018)”. Then select binary snow products under cloud free conditions as reference to carry out comparison of cloud distribution and total area time series of two products, the results show that fractional snow cover products accord with binary snow products in both space and time. In the case of the winter of 2013, when the value of fractional snow cover is greater than 50％, the relevance of two products is up to 0.8628. This data set is expected to provide high-temporal-resolution data input for the dynamic monitoring of snow over high Asia and the research of climate environment, hydrological and energy balance and disaster assessment.高亚洲（High Asia）是指以青藏高原为核心的亚洲高海拔区域，同时也是中低纬度山地积雪的重要分布区。该区域积雪的动态变化对水文、能量平衡以及区域气候均具有重要影响。鉴于青藏高原季节性积雪具有积雪存留时间短、雪盖较薄的特征，开展日尺度积雪监测对于明晰水循环过程至关重要。本数据集以空间分辨率500米的MODIS归一化积雪指数数据为基础，结合地形数据与多种云下积雪覆盖估算算法的优势，逐步实现云下积雪覆盖的反演，满足云量低于10%的监测需求，最终生成《高亚洲逐日积雪分数覆盖（FSC）数据集（2002–2018）》。随后选取无云条件下的二值积雪产品作为参考基准，对两类产品的云量分布与积雪总面积时序特征开展对比分析，结果表明积雪分数覆盖产品在空间与时间维度上均与二值积雪产品具有良好一致性。以2013年冬季为例，当积雪分数覆盖值大于50%时，两类产品的相关系数可达0.8628。本数据集有望为高亚洲积雪动态监测、气候环境与水文能量平衡研究以及灾害评估提供高时间分辨率的数据支撑。