黑河生态水文遥感试验：水文气象观测网数据集（大满超级站宇宙射线土壤水分-2017）

该数据集包含了2017年1月1日至2017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 %），其处理的主要步骤包括：
1） 数据筛选
数据筛选共四条标准：（1）剔除电压小于和等于11.8伏特的数据；（2）剔除空气相对湿度大于和等于80%的数据；（3）剔除采样时间间隔不在60±1分钟内的数据；（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。
2） 气压订正
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。
3） 仪器率定
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。
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2012年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。
4） 土壤水分计算
根据公式，计算得到每小时的土壤含水量数据。
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)

This dataset contains observational data collected by the Cosmic-ray Instrument (CRS) from January 1, 2017 to December 31, 2017. The monitoring site is located within the farmland of the Daman Irrigation District, Zhangye City, Gansu Province, with the underlying surface being corn field. The coordinates of the observation point are 100.3722°E, 38.8555°N, with an altitude of 1556 m. The bottom of the instrument probe is 0.5 m above the ground, and the sampling frequency is 1 hour.
Original observational items of the CRS include: voltage Batt (V), temperature T (℃), relative humidity RH (%), atmospheric pressure P (hPa), fast neutron count N1C (counts/hour), thermal neutron count N2C (counts/hour), fast neutron sampling time N1ET (s), and thermal neutron sampling time N2ET (s). The published data are post-processed and calculated results, with the table headers including: Date Time (date and time), P (atmospheric pressure, hPa), N1C (fast neutron count, counts/hour), N1C_cor (pressure-corrected fast neutron count, counts/hour), and VWC (soil volumetric water content, %). The main processing steps are as follows:
1) Data Screening
Four criteria are applied for data screening: (1) Remove data with voltage less than or equal to 11.8 V; (2) Remove data with air relative humidity greater than or equal to 80%; (3) Remove data with sampling time intervals not within 60±1 minutes; (4) Remove data where the fast neutron count changes by more than 200 compared to the counts of the previous and subsequent hours. In addition, missing values are filled with -6999.
2) Pressure Correction
Pressure correction is performed on the original data using the fast neutron pressure correction formula mentioned in the instrument instruction manual to obtain the corrected fast neutron count N1C_cor.
3) Instrument Calibration
Calibration of N0 in the soil moisture calculation formula is required during the process. N0 refers to the fast neutron count under dry soil conditions. Generally, measured soil moisture θm (obtained via soil samples from the measurement source area or via dense wireless soil moisture sensors) and the corrected fast neutron count N from the corresponding period are used to invert N0 through formulas (Zreda et al. 2012).
In this study, the instrument is calibrated using Soilnet soil moisture data from the instrument source area to establish the relationship between soil volumetric water content θv and fast neutron count. Data from four days with distinct dry and wet conditions, specifically June 26–27 and July 16–17, 2012, are selected. For June 26–27, where the soil moisture was relatively low, the average value of measurements at 4 cm, 10 cm, and 20 cm depths is used as calibration data, with a variation range of 22%–30%. For July 16–17, where the soil moisture was relatively high, the average value of measurements at 4 cm and 10 cm depths is used as calibration data, with a variation range of 28%–39%. The final averaged N0 is 3597.
4) Soil Moisture Calculation
Hourly soil moisture data are calculated based on the established formula.
For information on the hydrometeorological network and the site, please refer to Liu et al. (2018); for observational data processing, please refer to Zhu et al. (2015)

搜集汇总

背景与挑战

背景概述

该数据集为2017年大满超级站宇宙射线土壤水分观测数据，包含经过处理的每小时土壤体积含水量信息，适用于农田土壤水分研究。