A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p&lt;0.05) and MOD10C2 during 2001–2019 (r=0.97, p&lt;0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE contains 39 files. The files are organized by year and can be opened by using ENVI software. Spatial Coverage: N: 90, S: 0, E: 180, W: -180<br> Spatial Resolution: 0.05 deg x 0.05 deg<br> Samples = 7200<br> Lines = 1800<br> Temporal Coverage: September 1981 to December 2019<br> Temporal Resolution: 8-day北半球（Northern Hemisphere, NH）积雪范围（Snow Cover Extent, SCE）因其独特的地表特性，是气候变化最重要的指示指标之一。然而，已发表的各类SCE产品存在时间覆盖时长较短、空间分辨率较粗糙、积雪判别方法不统一等缺陷，制约了相关精细化研究的开展。本研究依托先进甚高分辨率辐射计地表反射率（Advanced Very High Resolution Radiometer Surface Reflectance, AVHRR-SR）气候数据记录（Climate Data Record, CDR）与多套辅助数据集，生成了1981—2019年时段内时间一致性优异的8天合成、0.05°分辨率无间隙北半球陆地SCE产品，该产品作为全球陆表卫星数据集（Global LAnd Surface Satellite dataset, GLASS）产品套件的组成部分。 该产品的生成流程共包含五个步骤：第一步，采用搭载多阈值测试的决策树算法，从每日AVHRR-SR CDR数据中提取积雪范围；第二步，融合两套现有每日SCE产品，以兼顾二者的空间覆盖优势；第三步，通过聚合处理获取每个8天合成周期内的最大积雪范围；第四步，结合积雪概率气候学数据集生成GLASS SCE产品；第五步，开展验证流程以评估GLASS SCE产品的质量。 基于1981—2017年的562个全球历史气候学网络（Global Historical Climatology Network）站点数据（相关系数r=0.61，p<0.05）以及2001—2019年的MOD10C2产品（r=0.97，p<0.01）开展的验证结果表明，GLASS SCE产品在积雪频率监测方面具备可靠性能。此外，1982—2018年期间GLASS SCE与地表反照率的交叉对比分析，进一步证实了其在气候变化研究中的应用价值。 GLASS SCE数据集共包含39个文件，按年份进行组织存储，可通过ENVI软件打开。其空间覆盖范围为：北：90°，南：0°，东：180°，西：-180°；空间分辨率：0.05°×0.05°；样本数（Samples）=7200，行线条数（Lines）=1800；时间覆盖范围：1981年9月至2019年12月；时间分辨率：8天。