本数据集为中国陆域逐日25km积雪密度数据，时间范围覆盖2013-2020年，地理范围为71.97°-136.74°E，16.31°-54.31°N，为TIF格式的栅格数据，共包含2186个TIF文件。\n
积雪密度数据集是基于国家科技基础资源调查专项“中国积雪特性及分布调查”项目获取的2017-2019年地面调查数据和积雪站观测数据、以及2013-2020年中国气象站观测数据，结合卫星遥感积雪产品数据、地形要素以及再分析数据提供的气象要素，利用时空加权与机器学习相融合的积雪密度时空模拟模型生产。该数据集反映了中国陆域大尺度积雪密度时空分布信息，适用于积雪特性、雪水资源、积雪灾害等方面研究与应用。", "ds_source": "
数据集生产共使用三类数据，包括地面观测数据、卫星遥感数据和再分析数据。其中，地面观测数据为2013-2020年全国气象观测站的逐日地面雪深和雪压观测资料，以及国家冰川冻土沙漠科学数据中心（http://www.ncdc.ac.cn/）提供的2017-2019年全国积雪观测站和地面调查的地面雪深和雪压观测数据；卫星遥感数据包括国家冰川冻土沙漠科学数据中心（http://www.ncdc.ac.cn/）提供的积雪反照率、积雪面积等，以及SRTM数字高程模型和MODIS土地植被分类产品（MCD12Q1）；再分析数据为ERA-5 Land数据集（https://cds.climate.copernicus.eu），包括风速、气温等气象要素、植被叶面积指数等植被要素和降雪量、融雪量等积雪要素。", "ds_process_way": "
数据集使用时空加权神经网络模型生产得到逐日积雪密度，模型输入变量包括积雪因素、气象因素、地形因素和植被因素，以及地面观测真值，输出变量为逐日积雪密度。首先需要提取观测站点的积雪密度及其对应位置上的影响因素作为样本，采用十折交叉验证的方法确定最优模型，进而估算得到2013-2020年全国逐日积雪密度。", "ds_quality": "
数据集经十折交叉验证，模型总体的R2、MAE和RMSE分别为0.531、0.028和0.043g/cm3。（注：由于2019-2020年地面站点样本偏少，导致模型生产得到的积雪密度数据效果不如2013-2018年的数据）", "ds_acq_start_time": "2013-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 136.73999999999998, "ds_acq_lat_south": 16.310000000000002, "ds_acq_lon_west": 71.97, "ds_acq_lat_north": 54.309999999999995, "ds_acq_alt_low": -238.0, "ds_acq_alt_high": 8537.0, "ds_share_type": "apply-access", "ds_total_size": 17788961, "ds_files_count": 2, "ds_format": "GeoTIFF", "ds_space_res": "25000m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "正轴等角圆锥", "ds_thumbnail": "121b0f6b-0c76-4869-bd6f-f96ea452ba0e.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "张学良,王华东,肖鹏峰,车涛,郑照军,戴礼云,栾文博.中国区域逐日积雪密度数据集（2013-2020）.国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn), 2023.", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.isnow.db2711.2023", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2023-01-25 16:54:30", "last_updated": "2026-05-20 17:32:04", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2711.2023", "i18n": { "en": { "title": "Daily snow density data set in China (2013-2020)", "ds_format": "GeoTIFF", "ds_source": "
The dataset production uses three types of data, including ground observation data, satellite remote sensing data, and reanalysis data. Among them, the ground observation data includes daily ground snow depth and snow pressure observation data from national meteorological observation stations from 2013 to 2020, as well as the National Glacier, Frozen Soil and Desert Science Data Center（ http://www.ncdc.ac.cn/ ）The ground snow depth and snow pressure observation data provided by the national snow observation stations and ground surveys from 2017 to 2019; Satellite remote sensing data includes the National Glacier, Frozen Soil and Desert Science Data Center（ http://www.ncdc.ac.cn/ ）The provided snow albedo, snow cover area, as well as SRTM digital elevation model and MODIS land vegetation classification product (MCD12Q1); Reanalysis data for ERA-5 Land dataset（ https://cds.climate.copernicus.eu ）Including meteorological factors such as wind speed and temperature, vegetation factors such as vegetation leaf area index, and snow accumulation factors such as snowfall and snowmelt.", "ds_quality": "
The dataset was subjected to ten fold cross validation, and the overall R2, MAE, and RMSE of the model were 0.531, 0.028, and 0.043g/cm3, respectively. (Note: Due to the small sample size of ground stations in 2019-2020, the snow density data produced by the model is not as effective as the data from 2013-2018.)", "ds_ref_way": "", "ds_abstract": "
This dataset is the daily 25km snow density data of China's land area, covering the time range from 2013 to 2020, with a geographical range of 71.97 ° -136.74 ° E and 16.31 ° -54.31 ° N. It is a raster data in TIF format and contains a total of 2186 TIF files.\r\n
The snow density dataset is based on the ground survey data and snow station observation data obtained from the \"China Snow Characteristics and Distribution Survey\" project of the National Science and Technology Basic Resources Survey from 2017 to 2019, as well as the observation data from the China Meteorological Station from 2013 to 2020. It combines satellite remote sensing snow product data, terrain elements, and meteorological elements provided by reanalysis data to produce a spatiotemporal simulation model of snow density that combines spatiotemporal weighting and machine learning. This dataset reflects the spatiotemporal distribution information of large-scale snow cover density in China's land area, and is suitable for research and application in snow cover characteristics, snow water resources, snow cover disasters, and other aspects.", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The dataset uses a spatiotemporal weighted neural network model to produce daily snow cover density. The input variables of the model include snow cover factors, meteorological factors, terrain factors, vegetation factors, as well as ground observation truth values. The output variable is daily snow cover density. Firstly, it is necessary to extract the snow density of observation stations and the influencing factors at their corresponding locations as samples, and use the ten fold cross validation method to determine the optimal model, and then estimate the daily snow density of the country from 2013 to 2020.", "ds_ref_instruction": "Zhang Xueliang, Wang Huadong, Xiao Pengfeng, Che Tao, Zheng Zhaojun, Dai Liyun, Luan Wenbo. China Daily Snow Density Data Set (2013-2020). National Glacier and Frozen Desert Science Data Center (www.ncdc. ac.cn), 2023" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "积雪密度" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "张学良", "email": "zxl@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张学良", "email": "zxl@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张学良", "email": "zxl@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "category": "积雪" }