Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, USA.
Nong Lam University, Ho Chi Minh City, Vietnam.
Sci Data. 2019 Jan 15;6:180300. doi: 10.1038/sdata.2018.300.
This article presents a cloud-free snow cover dataset with a daily temporal resolution and 0.05° spatial resolution from March 2000 to February 2017 over the contiguous United States (CONUS). The dataset was developed by completely removing clouds from the original NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Area product (MOD10C1) through a series of spatiotemporal filters followed by the Variational Interpolation (VI) algorithm; the filters and VI algorithm were evaluated using bootstrapping test. The dataset was validated over the period with the Landsat 7 ETM+ snow cover maps in the Seattle, Minneapolis, Rocky Mountains, and Sierra Nevada regions. The resulting cloud-free snow cover captured accurately dynamic changes of snow throughout the period in terms of Probability of Detection (POD) and False Alarm Ratio (FAR) with average values of 0.955 and 0.179 for POD and FAR, respectively. The dataset provides continuous inputs of snow cover area for hydrologic studies for almost two decades. The VI algorithm can be applied in other regions given that a proper validation can be performed.
本文提供了一份从 2000 年 3 月到 2017 年 2 月美国大陆地区（CONUS）每天都有的、空间分辨率为 0.05°的无云积雪数据集。该数据集是通过一系列时空滤波器以及变分插值（VI）算法，从原始 NASA 的中分辨率成像光谱仪（MODIS）积雪面积产品（MOD10C1）中完全去除云得到的；滤波器和 VI 算法是通过自举测试进行评估的。该数据集在西雅图、明尼阿波利斯、落基山脉和内华达山脉地区与 Landsat 7 ETM+ 雪盖图进行了验证。在整个研究期间，基于探测概率（POD）和误报率（FAR），该无云积雪数据集的结果准确地捕捉到了积雪的动态变化，其 POD 和 FAR 的平均值分别为 0.955 和 0.179。该数据集提供了近 20 年来水文研究中积雪面积的连续输入。只要能够进行适当的验证，就可以将 VI 算法应用于其他地区。
