The Study of Quality Control for Observing Data of Automatic Soil Moisture
Abstract: The quality control for automatic soil moisture of 10 cm was made by using spike tests (soil volumetric water content was greater than 60% recorded as a mistake, the soil volumetric water content was less than or equal to 0 recorded as a mistake), step tests (the relative humidity of soil dumped 20% recorded as suspicious) and runs of constant values (the relative humidity of soil was the same for a month recorded as suspicious) for the years of 2012, 2013 and 2014. Based on the analysis of four factors, i.e. detection rate, regional distribution, temporal regularity and instrument model, the characteristics of detected data (SWC010 indicated that the 10 cm layer of soil volumetric water content, SRH010 indicated that the 10 cm layer of soil relative humidity) were studied. The conclusions were as following. There was the maximum detection rate for the data that the soil volumetric water content of 10 cm was equal to zero, however, there was the minimum for the 10 cm relative humidity sudden dropped by 20 percent, caused by the soil cracking or by the gap between the sensor and the soil. The numbers of detected data were distinctly different in each province, which ranged from one to over ten thousand data. The number of provinces that had the data for SWC010 less than zero was the smallest, however, the other four types of detected data were common in the most provinces in our country. Considering the temporal regularity, the error data were higher in summer than those in winter for the method that soil volumetric water content of 10 cm was larger than 60 percent, and there were the most detected data in the winter for other quality control methods. The detected data of the 10 cm soil moisture which were less than zero were observed by the only instrument model of DZN2, which was caused by the calibration equations.
文章引用: 吴东丽 , 曹婷婷 , 薛红喜 (2016) 自动土壤水分观测数据质量控制方法及其应用。 土壤科学， 4， 1-10. doi: 10.12677/HJSS.2016.41001
 王磊, 文军, 韦志刚, 等. 中国西北区西部土壤湿度及其气候响应[J]. 高原气象, 2008, 27(6): 1257-1266.
 孙丞虎, 李维京, 张祖强, 等. 淮河流域土壤湿度异常的时空分布特征及其与气候异常关系的初步研究[J]. 应用气象学报, 2005, 16(2): 129-138.
 韩俊杰, 高永刚, 南瑞, 等. 1984-2005年黑龙江省主要农区土壤湿度的变化特征[J]. 中国农业气象, 2009, 30(1): 41-44.
 汪潇, 张增祥, 赵晓丽, 等. 遥感监测土壤水分研究综述[J]. 土壤学报, 2007, 44(1): 157-163.
 郭卫华, 李波, 张新时, 等. FDR系统在土壤水分连续动态监测中的应用[J]. 干旱区研究, 2003, 20(4): 247-251.
 欧阳双, 张其林, 李颖, 等. 地表湿度导致土壤电参数变化对雷电电磁场传播的影响[J]. 气象科技, 2012, 40(6): 1018-1024.
 陈海山, 孙照渤. 陆气相互作用及陆面模式的研究进展[J]. 南京气象学院学报, 2002, 25(2): 277-288.
 张艳丽, 张国珍. 黄土高原典型塬区土壤湿度特征分析[J]. 干旱区资源与环境, 2010, 24(5): 190-195.
 杨袁慧, 师春香, 王炜, 等.一次强降水模拟中土壤湿度初值的影响研究[J]. 气象, 2013, 39(11): 1481-1489.
 吴东丽, 梁海河, 曹婷婷, 等. 中国自动土壤水分观测网运行监控系统建设[J]. 气象科技, 2014, 42(2): 278-282.
 陈海波, 冶林茂, 薛龙琴, 等. GStar-I (DZN2)型自动土壤水分观测仪的维护方法及常见故障解析[J]. 气象与环境科学, 2011, 34(S1): 178-181.
 王良宇, 何延波. 自动土壤水分观测数据异常值阈值研究[J]. 气象, 2015, 41(8): 1017-1022.
 黄飞龙, 李昕娣, 黄宏智, 等. 基于FDR的土壤水分探测系统与应用[J]. 气象, 2012, 38(6): 764-768.
 陈怀亮, 张红卫, 刘荣花, 等. 中国农业干旱的监测、预警和灾损评估[J]. 科技导报, 2009, 27(11): 82-92.