该数据集包含了2012年9月20日至2013年12月31日的宇宙射线仪器（crs）观测数据。站点位于甘肃省张掖市大满灌区农田内，下垫面是玉米地。观测点的经纬度是100.3722E, 38.8555N，海拔1556m，仪器探头底部距地面0.5m，采样频率是1小时。宇宙射线仪器的原始观测项目包括：
电压Batt（V）、温度T（℃）、相对湿度RH（%）、气压P（hPa）、快中子数N1C（个/小时）、热中子数N2C（个/小时）、快中子采样时间N1ET（s）及热中子采样时间N2ET（s）。
发布的数据为经过处理计算后的数据，数据表头包括：Date Time（日期 时间）、P（气压 hPa）、N1C（快中子数 个/小时）、N1C_cor（气压订正的快中子数 个/小时）和VWC（土壤体积含水量 %）。
", "ds_source": "宇宙射线仪器的原始观测项目包括：
\n电压Batt（V）、温度T（℃）、相对湿度RH（%）、气压P（hPa）、快中子数N1C（个/小时）、热中子数N2C（个/小时）、快中子采样时间N1ET（s）及热中子采样时间N2ET（s）。
\n发布的数据为经过处理计算后的数据，数据表头包括：Date Time（日期 时间）、P（气压 hPa）、N1C（快中子数 个/小时）、N1C_cor（气压订正的快中子数 个/小时）和VWC（土壤体积含水量 %）
", "ds_process_way": "1） 数据筛选
\n数据筛选共四条标准：
\n（1）剔除电压小于和等于11.8伏特的数据；
\n（2）剔除空气相对湿度大于和等于80%的数据；
\n（3）剔除采样时间间隔不在60±1分钟内的数据；
\n（4） 剔除快中子数较前后一小时变化大于200的数据。此外缺失数据用-6999补充。
\n2） 气压订正
\n\n根据仪器说明手册中提到的快中子气压订正公式，对原始数据进行气压订正，得到订正后的快中子数N1C_cor。
\n3） 仪器率定
\n在计算土壤水分的过程中需要对计算公式中的N0进行率定。N0为土壤干燥条件下的快中子数，通常使用测量源区内的土样得到实测土壤水分（或者通过比较密集的土壤水分无线传感器获取）θm（Zreda et al. 2012）和对应时间段内的快中子校正数据N，再通过公式反求得到N0。
\n在此，根据仪器源区内的Soilnet土壤水分数据对仪器进行率定，建立土壤体积含水量θv和快中子之间的关系。分别选取干湿状况差异比较明显的6月26日-27日和7月16日-17日四天的数据，其中6月26日-27日率定数据显示土壤水分较小，因此选取4厘米、10厘米和20厘米的三个值平均值作为率定数据，其变化范围为22%-30%，而7月16日-17日率定数据显示土壤水分较大，因此选取4厘米、10厘米的两个值平均值作为率定数据，其变化范围为28%-39%，最后平均N0为3597。
\n4） 土壤水分计算
\n根据公式，计算得到每小时的土壤含水量数据。
\n水文气象网或站点信息请参考Liu et al. (2018)，观测数据处理请参考Zhu et al. (2015)。
\n", "ds_quality": "数据质量良好
", "ds_acq_start_time": "2012-09-20 00:00:00", "ds_acq_end_time": "2013-12-31 00:00:00", "ds_acq_place": "大满超级站", "ds_acq_lon_east": 100.37194444444444, "ds_acq_lat_south": 38.85527777777778, "ds_acq_lon_west": 100.37166666666666, "ds_acq_lat_north": 38.855555555555554, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 477549, "ds_files_count": 3, "ds_format": "excel", "ds_space_res": null, "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "0ca503ea-189c-417b-aaae-e3d402213656.png", "ds_thumb_from": 0, "ds_ref_way": "刘绍民，黑河生态水文遥感试验：大满超级站宇宙射线土壤水分观测网数据集（2012-2013），国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2020，doi：10.12072/ncdc.NIEER.db0047.2020", "paper_ref_way": "", "ds_ref_instruction": "本数据由“黑河生态水文遥感试验（HiWATER）”产生，用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "c94b3578-20da-4346-9de9-c702b6ca8983", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596 ", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": null, "quality_level": 3, "publish_time": "2021-08-27 15:27:20", "last_updated": "2021-08-27 15:27:20", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.2020.1592", "i18n": { "en": { "title": "Heihe River eco hydrological remote sensing experiment: hydrometeorological observation network data set (cosmic ray soil moisture of Daman super station-2013)", "ds_format": "", "ds_source": "The original observation items of cosmic ray instruments include:
\nVoltage batt (V), temperature T (℃), relative humidity Rh (%), air pressure P (HPA), fast neutron number N1c (piece / hour), thermal neutron number n2c (piece / hour), fast neutron sampling time n1et (s) and thermal neutron sampling time n2et (s)
\nThe published data is the data after processing and calculation. The data header includes: date time, P (air pressure HPA), N1c (fast neutrons / hour), N1c_ Cor (barometric corrected fast neutrons / hour) and vwc (soil volume water content%)
", "ds_quality": "Good data quality
", "ds_ref_way": "", "ds_abstract": "The data set contains the cosmic ray instrument (CRS) observation data from September 20, 2012 to December 31, 2013. The station is located in the farmland of Daman Irrigation District, Zhangye City, Gansu Province, with corn field as the underlying surface. The longitude and latitude of the observation point are 100.3722e, 38.8555n, the altitude is 1556M, the bottom of the instrument probe is 0.5m from the ground, and the sampling frequency is 1 hour. The original observation items of cosmic ray instruments include:
\nVoltage batt (V), temperature T (℃), relative humidity Rh (%), air pressure P (HPA), fast neutron number N1c (piece / hour), thermal neutron number n2c (piece / hour), fast neutron sampling time n1et (s) and thermal neutron sampling time n2et
\nThe published data is the data after processing and calculation. The data header includes: date time, P (air pressure HPA), N1c (fast neutrons / hour), N1c_ Cor (barometric corrected fast neutrons / hour) and vwc (soil volume water content%)
", "ds_time_res": "日", "ds_acq_place": "", "ds_space_res": "", "ds_projection": "", "ds_process_way": "1) Data filtering
\nThere are four criteria for data filtering:
\n（ 1) Eliminate data with voltage less than or equal to 11.8 volts
\n（ 2) Eliminate the data of air relative humidity greater than or equal to 80%
\n（ 3) Eliminate the data whose sampling time interval is not within 60 ± 1 min
\n（ 4) Eliminate the data that the number of fast neutrons changes more than 200 compared with the previous hour. In addition, missing data were supplemented with - 6999
\n2) Barometric correction
\nAccording to the fast neutron pressure correction formula mentioned in the instrument instruction manual, the pressure correction is carried out on the original data to obtain the corrected fast neutron number N1c_ cor。
\n3) Instrument calibration
\nIn the process of calculating soil moisture, N0 in the calculation formula needs to be calibrated. N0 is the fast neutron number under the condition of soil drying. Usually, the measured soil moisture is obtained by using the soil sample in the measurement source area (or obtained by relatively dense soil moisture wireless sensor) θ M (zreda et al. 2012) and fast neutron correction data n in the corresponding time period, and then N0 is obtained by reverse calculation through the formula
\nHere, the instrument is calibrated according to the soilnet soil moisture data in the instrument source area, and the soil volume moisture content is established θ V and fast neutrons. The data of June 26-27 and July 16-17 with obvious differences in dry and wet conditions are selected respectively. The calibration data from June 26-27 shows that the soil moisture is small, so the average values of 4cm, 10cm and 20cm are selected as the calibration data, with a variation range of 22% - 30%, while the calibration data from July 16-17 shows that the soil moisture is large, so 4cm The average of the two values of 10cm is used as the calibration data, and its variation range is 28% - 39%. Finally, the average N0 is 3597
\n4) Soil moisture calculation
\nAccording to the formula, the hourly soil water content data are calculated
\nFor hydrometeorological network or station information, please refer to Liu et al. (2018), and for observation data processing, please refer to Zhu et al. (2015)
", "ds_ref_instruction": " " } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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, "ds_topic_tags": [ "水文气象观测网", "宇宙射线土壤水分", "土壤水分" ], "ds_subject_tags": [ "大气探测" ], "ds_class_tags": [], "ds_locus_tags": [ "中游人工绿洲试验区", "黑河流域", "大满超级站" ], "ds_time_tags": [ 2012, 2013 ], "ds_contributors": [ { "true_name": "刘绍民", "email": "smliu@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "朱忠礼", "email": "zhuzl@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院", "country": "中国" }, { "true_name": "刘绍民", "email": "smliu@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "徐自为", "email": "xuzw@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院", "country": "中国" } ], "category": "气象" }