本数据集包含三个子数据集，分别是2016-2017年长春积雪-土壤水热地面观测数据、2017-2018年哈尔滨积雪-土壤水热地面观测数据、2018-2019哈尔滨积雪-土壤水热地面观测数据。\n2016-2017年长春积雪-土壤水热地面观测数据包含1个Excel数据表，共53446条记录，观测要素包括积雪密度、积雪深度、积雪温度、雪/土壤温度、雪表温度、雪层温度和雪粒直径以及10分钟和1小时步长的积雪区和裸土区地表以下5cm、10cm、15cm、20cm、40cm、80cm处土壤温湿度观测数据。\n2017-2018年哈尔滨积雪-土壤水热地面观测数据包含2个Excel数据表，共17008条记录，观测要素包括雪密度和液态含水量以及不同积雪厚度(50cm、40cm、30cm、20cm、10cm)覆盖状态下和裸土状态下地表以下5cm、15cm、25cm、35cm处土壤温湿度观测数据。\n2018-2019哈尔滨积雪-土壤水热地面观测数据包含1个Excel数据表，共10180条记录，观测要素包括积雪区和裸土区地表以下10cm、20cm、30cm、40cm、50cm、60cm、70cm、80cm、90cm、100cm处土壤温湿度观测数据。
", "ds_source": "实验中使用了气象观测系统和土壤温湿度监测系统，对土壤的温湿度进行观测。积雪参数采用人工观测。
\n一、2016-2017年长春积雪-土壤水热地面观测数据:在实验区内设有土壤温湿度传感器，自动观测积雪区和裸土区地表以下5cm、10cm、15cm、20cm、40cm、80cm处土壤温湿度观测数据。积雪参数采用人工观测，观测方法如下：\n积雪密度：采用雪铲和电子秤测量3次取3次平均值；\n积雪深度：通过直尺获取,取3次平均值；\n积雪温度、雪/土壤温度、雪表温度：采用手持热红外温度计测量3次取3次平均值；\n雪粒直径：采用手持显微镜拍照记录长轴的半径并取平均值。
\n二、2017-2018年哈尔滨积雪-土壤水热地面观测数据:实验场内设有RR-7210土壤温湿度监测系统，对不同积雪厚度下和裸土状态下的土壤的温湿度进行实时观测。液态水含量和雪密度采用人工观测，观测方法如下：\n液态水含量：通过雪特性分析仪Snowfork获取，取3次平均值；\n雪密度：采用雪铲和电子秤测量3次取3次平均值；
\n三、2018-2019哈尔滨积雪-土壤水热地面观测数据:实验中裸土区降雪后及时清扫，积雪区保持自然降雪状态。气象数据的观测采用试验区安装的 TRM-ZS1型气象生态环境监测系统自动记录。土壤温湿度传感器分别埋设在两块试验场土质均匀处地下10cm、20cm、30cm、40cm、50cm、60cm、70cm、80cm、90cm、100cm处。自然降雪处理地块保持原有的自然状态，裸土区在发生降雪后进行清扫。
", "ds_process_way": "由于停电或者仪器故障等原因，单点定位控制对照实验中土壤温湿度传感器观测存在数据的部分缺失和观测值异常问题，在数据整理过程中，对观测值异常值进行剔除，并标注为NULL值。
", "ds_quality": "数据质量控制阶段分两步：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。第二阶段邀请专家对数据进行审核，审核数据的完整性和准确性。
", "ds_acq_start_time": "2016-10-18 00:00:00", "ds_acq_end_time": "2019-05-01 00:00:00", "ds_acq_place": "吉林省长春市、黑龙江哈尔滨市", "ds_acq_lon_east": 131.16583333333335, "ds_acq_lat_south": 43.096111111111114, "ds_acq_lon_west": 123.915, "ds_acq_lat_north": 46.855555555555554, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 8291289, "ds_files_count": 13, "ds_format": "xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "d894b22f-e869-4fc6-afe5-40b21c652842.jpg", "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.db1661.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-01-06 11:22:11", "last_updated": "2023-03-06 12:57:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2526.2022", "i18n": { "en": { "title": "Northeast farmland snow – Soil Hydrothermal ground observation data set (2017-2019)", "ds_format": "", "ds_source": " <pre><code>\nIn the experiment, the meteorological observation system and soil temperature and humidity monitoring system were used to observe the soil temperature and humidity. Snow parameters are observed manually.\n1、 Ground observation data of snow Soil Hydrothermal in Changchun from 2016 to 2017: a soil temperature and humidity sensor is set in the experimental area to automatically observe the observation data of soil temperature and humidity 5cm, 10cm, 15cm, 20cm, 40cm and 80cm below the surface of snow area and bare soil area. Snow parameters are observed manually, and the observation methods are as follows:\nSnow density: measure 3 times with snow shovel and electronic scale, and take the average value of 3 times;\nSnow depth: obtained with a ruler and averaged for 3 times;\nSnow temperature, snow / soil temperature and snow surface temperature: the hand-held thermal infrared thermometer is used to measure 3 times, and the average value of 3 times is taken;\nDiameter of snow particles: take photos with a hand-held microscope, record the radius of the long axis and take the average value.\n2、 Ground observation data of snow Soil Hydrothermal in Harbin from 2017 to 2018: the experimental site is equipped with rr-7210 soil temperature and humidity monitoring system to conduct real-time observation of soil temperature and humidity under different snow thickness and bare soil state. The liquid water content and snow density are observed manually by the following methods:\nLiquid water content: obtained by snow characteristic analyzer snowfork, and take the average value for 3 times;\nSnow density: measure 3 times with snow shovel and electronic scale, and take the average value of 3 times;\n3、 2018-2019 ground observation data of snow soil water and heat in Harbin: in the experiment, the bare soil area was cleaned in time after snowfall, and the snow area remained in the state of natural snowfall. The observation of meteorological data is automatically recorded by trm-zs1 meteorological ecological environment monitoring system installed in the test area. The soil temperature and humidity sensors are respectively buried at 10cm, 20cm, 30cm, 40cm, 50cm, 60cm, 70cm, 80cm, 90cm and 100cm underground where the soil quality of the two test sites is uniform. The natural snowfall treatment plot shall maintain the original natural state, and the bare soil area shall be cleaned after snowfall.
", "ds_quality": " <pre><code>\nThe data quality control stage is divided into two steps: first, the data provider checks the integrity and accuracy of the data. Data integrity check mainly checks whether the number of observation points and observation fields of data meet the project requirements. On the one hand, abnormal values are judged and eliminated through numerical diagrams. On the other hand, the data storage mode and data format are unified and standardized. In the second stage, experts are invited to review the data to review the integrity and accuracy of the data.
", "ds_ref_way": "", "ds_abstract": " <pre><code>\nThis data set contains three sub data sets, namely, snow cover soil hydrothermal ground observation data in Changchun from 2016 to 2017, snow cover soil hydrothermal ground observation data in Harbin from 2017 to 2018, and snow cover soil hydrothermal ground observation data in Harbin from 2018 to 2019.\nThe ground observation data of snow Soil Hydrothermal in Changchun from 2016 to 2017 includes an Excel data sheet with a total of 53446 records. The observation elements include snow density, snow depth, snow temperature, snow / soil temperature, snow surface temperature, snow layer temperature and snow particle diameter, as well as 5cm, 10cm, 15cm, 20cm, 40cm below the surface of snow area and bare soil area with a length of 10 minutes and 1 hour Soil temperature and humidity observation data at 80cm.\nThe ground observation data of snow Soil Hydrothermal in Harbin from 2017 to 2018 includes two Excel data sheets, with a total of 17008 records. The observation elements include snow density and liquid water content, as well as the observation data of soil temperature and humidity 5cm, 15cm, 25cm and 35cm below the ground surface under different snow thickness (50cm, 40cm, 30cm, 20cm and 10cm) and bare soil.\nThe 2018-2019 ground observation data of snow soil water and heat in Harbin includes an Excel data sheet with a total of 10180 records. The observation elements include the observation data of soil temperature and humidity at 10cm, 20cm, 30cm, 40cm, 50cm, 60cm, 70cm, 80cm, 90cm and 100cm below the surface of snow area and bare soil area.
", "ds_time_res": "", "ds_acq_place": "Changchun City, Jilin Province, Harbin City, Heilongjiang Province", "ds_space_res": "", "ds_projection": "", "ds_process_way": " <pre><code>\n1、 A soil temperature and humidity sensor is set in the experimental area to automatically observe the soil temperature and humidity observation data 5cm, 10cm, 15cm, 20cm, 40cm and 80cm below the surface of snow covered area and bare soil area. Snow parameters are observed manually, and the observation methods are as follows:\nSnow density: measure 3 times with snow shovel and electronic scale, and take the average value of 3 times;\nSnow depth: obtained with a ruler and averaged for 3 times;\nSnow temperature, snow / soil temperature and snow surface temperature: the hand-held thermal infrared thermometer is used to measure 3 times, and the average value of 3 times is taken;\nDiameter of snow particles: take photos with a hand-held microscope, record the radius of the long axis and take the average value.\nThe data quality control stage is divided into two steps: first, the data provider checks the integrity and accuracy of the data. Data integrity check mainly checks whether the number of observation points and observation fields of data meet the project requirements. On the one hand, abnormal values are judged and eliminated through numerical diagrams. On the other hand, the data storage mode and data format are unified and standardized. In the second stage, experts are invited to review the data to review the integrity and accuracy of the data.\n2、 Rr-7210 soil temperature and humidity monitoring system is set in the experimental site to observe the soil temperature and humidity under different snow thickness and bare soil state in real time. The liquid water content and snow density are observed manually by the following methods:\nLiquid water content: obtained by snow characteristic analyzer snowfork, and take the average value for 3 times;\nSnow density: measure 3 times with snow shovel and electronic scale, and take the average value of 3 times;\nThe data quality control stage is divided into two steps: first, the data provider checks the integrity and accuracy of the data. Data integrity check mainly checks whether the number of observation points and observation fields of data meet the project requirements. On the one hand, abnormal values are judged and eliminated through numerical diagrams. On the other hand, the data storage mode and data format are unified and standardized. In the second stage, experts are invited to review the data to review the integrity and accuracy of the data.\n3、 In the experiment, the bare soil area was cleaned in time after snowfall, and the snow covered area remained in the state of natural snowfall. The observation of meteorological data is automatically recorded by trm-zs1 meteorological ecological environment monitoring system installed in the test area.\nThe data quality control stage is divided into two steps: first, the data provider checks the integrity and accuracy of the data. Data integrity check mainly checks whether the number of observation points and observation fields of data meet the project requirements. On the one hand, abnormal values are judged and eliminated through numerical diagrams. On the other hand, the data storage mode and data format are unified and standardized. In the second stage, experts are invited to review the data to review the integrity and accuracy of the 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": [ "分层土壤温度", "分层土壤湿度", "积雪密度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "吉林省长春市", "黑龙江哈尔滨市" ], "ds_time_tags": [ 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "陈秀雪", "email": "", "work_for": "中国科学院东北地理与农业生态研究所", "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": "王广蕊", "email": "", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "卫颜霖", "email": "", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "梁爽", "email": "", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "张丽娟", "email": "", "work_for": "哈尔滨师范大学寒区地理环境监测与空间信息服务黑龙江省重点实验室", "country": "中国" }, { "true_name": "侯仁杰", "email": "", "work_for": "东北农业大学水利与土木工程学院", "country": "中国" } ], "ds_meta_authors": [ { "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" } ], "ds_managers": [ { "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" } ], "category": "积雪" }