本数据集包含我国典型积雪区2017-2020年3个积雪季21个500m积雪样方的积雪深度、空气温度及雪压数据。积雪样方数据主要采集于我国新疆、青海及东北地区。
\n数据集包含21个文件夹，21数据表，共945条记录。样方数据还包含样方点分布示意图及样方点矢量图（shp或者kmz格式）。
\n数据集命名规则、时间范围及站点经纬度见说明文档。
", "ds_source": "本数据集为自主观测的野外数据。
", "ds_process_way": "数据集来自各单位的野外观测数据。样方数据中经纬度及高程信息来自手持GPS。雪深、雪压数据分别来自量尺和雪筒。雪深数据为三次测量的平均值。雪压数据通过人工称量装在雪筒中的一定体积的积雪重量得到。密度数据通过雪压和雪深数据相除得到。
", "ds_quality": "数据质量控制阶段分两步：
\n首先数据提供者对数据进行了完整性、准确性检查。数据完整性检查主要检查数据的样方内的观测点数目及观测字段是否达到项目要求。数据准确性检查一方面通过数值图示判断异常值并进行剔除，另一方面对数据的存储方式、数据格式进行统一与规范。第二阶段是数据提供者对其它组的数据进行交叉检查，再次审核数据的完整性和准确性，并对不同区域的数据进行整合。其中，数据质量控制的数据自检表及数据分析图一并存于数据集文件夹中。
\n最后整合各样方数据，得到完整、准确、格式一致的500m样方数据集。
", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "新疆、青海、东北", "ds_acq_lon_east": 125.61944444444444, "ds_acq_lat_south": 34.358333333333334, "ds_acq_lon_west": 81.91666666666667, "ds_acq_lat_north": 47.63805555555555, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 459940666, "ds_files_count": 446, "ds_format": "xlsx，jpg", "ds_space_res": "", "ds_time_res": "日、时、分", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1d3ae5e6-6c4a-4095-a2b1-17a2189ab4e7.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "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.I-SNOW.db0012.2021", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-10-21 17:48:12", "last_updated": "2026-05-28 17:46:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2492.2022", "i18n": { "en": { "title": "snow survey dataset from measurement routes of 500m samples in the typical regions of China during 2017-2020", "ds_format": "xlsx，jpg", "ds_source": "This dataset is field data obtained from autonomous observation.
", "ds_quality": "The data quality control stage is divided into two steps:
\r\nFirst, the data provider checked the integrity and accuracy of the data. The data integrity check mainly checks whether the number of observation points and observation fields within the data square meet the project requirements. On the one hand, the data accuracy check determines and eliminates abnormal values through numerical illustrations, and on the other hand, it unifies and standardizes the data storage method and data format. The second stage is for the data provider to cross-check data from other groups, review the integrity and accuracy of the data again, and integrate data from different regions. Among them, the data self-test table and data analysis chart for data quality control are stored in the data set folder.
\r\nFinally, the various square data are integrated to obtain a complete, accurate and consistent 500m square data set.
", "ds_ref_way": "", "ds_abstract": "This dataset contains snow depth, air temperature and snow pressure data from 21 500-meter snow cover quadrants in three snow cover seasons from 2017 to 2020 in typical snow cover areas in my country. Snow cover sample data are mainly collected in Xinjiang, Qinghai and Northeast China.
\r\nThe dataset contains 21 folders, 21 data tables, and a total of 945 records. The spline data also includes a spline point distribution diagram and a spline point vector diagram (shp or kmz format).
\r\nSee the description document for the naming rules of the dataset, time range, and site latitude and longitude.
", "ds_time_res": "", "ds_acq_place": "Xinjiang, Qinghai and Northeast China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The data sets come from field observation data from various units. The latitude, longitude and elevation information in the sample data comes from handheld GPS. Snow depth and snow pressure data come from measuring scales and snow cylinders respectively. Snow depth data is the average of three measurements. Snow pressure data is obtained by manually weighing a certain volume of snow contained in a snow cylinder. Density data is obtained by dividing snow pressure and snow depth data.
", "ds_ref_instruction": "" } }, "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": [ "积雪样方", "雪深", "积雪密度", "雪压", "500m" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆", "东北地区" ], "ds_time_tags": [ 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李兰海", "email": "lilh@ms.xjb.ac.cn", "work_for": "中国科学院新疆生态与地理研究所", "country": "中国" }, { "true_name": "车涛", "email": "chetao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "肖建设", "email": "xiaojianshe@126.com", "work_for": "青海省气象科学研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "戴礼云", "email": "dailiyun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "车涛", "email": "chetao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }