Long-term series of daily global snow depth (1979-2017)

The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1980~2018, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail.File header:ncols 1383nrows 586xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1

本数据集“长期全球每日积雪深度序列”系基于被动微波遥感数据生成。其时间跨度涵盖1980年至2018年，空间覆盖范围遍及全球陆地。空间分辨率为25,067.53米，时间分辨率为每日。在积雪深度的推导过程中，采用了动态亮度温度梯度算法，该算法充分考虑了积雪特性的空间和时间变化，并建立了18GHz与36GHz之间温度差与实测积雪深度之间的空间与季节动态关系。用于推导积雪深度的长期卫星载被动微波亮度温度数据源自三个传感器（SMMR、SSM/I及SSMI/S），三者之间存在一定的系统不一致性。因此，在积雪深度推导前，对这些亮度温度数据进行了传感器间的校准，以提升其时间一致性。精度分析表明，欧亚大陆积雪深度数据的相对偏差控制在30%以内。数据以每日txt文件形式存储，每个文件包含一个1383*586的积雪深度矩阵，其中每个积雪深度代表一个25,067.53米*25,067.53米的网格。该数据采用EASE-Grid投影，以下为文件头信息，详细描述了投影参数：
ncols 1383
nrows 586
xllcorner -17334193.54
yllcorner -7344787.75
cellsize 25,067.53
NODATA_value -1