北半球0.05°分辨率积雪持续时间数据集（2001-2020年）

积雪持续时间SDD（Snow Duration Days）指的是水文年内积雪从出现到消融的时间，是积雪物候的重要特征变量之一。积雪物候是气候系统变化的重要标识，对水文预测、植被生长、生物多样性研究等极其重要。受积雪面积波动的影响，北半球积雪物候在区域和半球尺度也发生了显著的变化。目前，大尺度积雪物候信息多通过遥感反演得到。但是，遥感数据往往因为云污染、传感器饱和、地表亮目标等因素的干扰，存在大量的空缺值。因此，本数据集是在MOD10C2和IMS积雪数据融合的基础上提取得到，克服了物候信息提取过程中空缺值的干扰，减少了积雪物候信息提取的不确定性。该数据时间分辨率为年，空间分辨率0.05°，行列为7200×1800，数据类型为浮点型。通过GHCN站点数据的验证，该数据集与站点物候数据的相关性为0.66。

Snow Duration Days (SDD) refers to the period from the occurrence to the ablation of snow cover within a hydrological year, and is one of the key characteristic variables of snow phenology. Snow phenology serves as a critical indicator of climate system changes, and is extremely important for hydrological forecasting, vegetation growth, biodiversity research and other related fields. Affected by fluctuations in snow cover area, snow phenology in the Northern Hemisphere has also undergone significant changes at regional and hemispheric scales. Currently, large-scale snow phenology information is mostly obtained through remote sensing inversion. However, remote sensing data often have a large number of missing values due to interference from factors such as cloud contamination, sensor saturation, and bright surface targets. Therefore, this dataset is extracted based on the fusion of MOD10C2 and IMS snow cover data, which overcomes the interference of missing values during snow phenology information extraction and reduces the uncertainty of snow phenology information extraction. This dataset has a temporal resolution of 1 year, a spatial resolution of 0.05°, a grid dimension of 7200 rows × 1800 columns, and a data type of floating-point. Validated against GHCN station data, the correlation coefficient between this dataset and station phenology data is 0.66.

搜集汇总

背景与挑战

背景概述

该数据集提供了2001年至2020年北半球积雪持续时间（SDD）的年数据，空间分辨率为0.05°，用于表征积雪物候变化。它基于MOD10C2和IMS积雪数据融合生成，有效减少了遥感数据空缺值干扰，提高了可靠性。数据经GHCN站点验证，与站点物候数据的相关性达0.66，适用于气候、水文和生态研究。