| 基于差分的土壤墒情自动监测数据误差修正方法 |
| |
| 引用本文： | 郝星耀,冯仲科,赵春江,李淑华,高秉博.基于差分的土壤墒情自动监测数据误差修正方法[J].农业工程学报,2015,31(3):152-158. |
| |
| 作者姓名： | 郝星耀 冯仲科 赵春江 李淑华 高秉博 |
| |
| 作者单位： | 1. 北京林业大学林学院,北京 100083; 2. 北京农业信息技术研究中心,北京 100097; 3. 国家农业信息化工程技术研究中心,北京 100097; 4. 农业部农业信息技术重点实验室,北京 100097;,1. 北京林业大学林学院,北京 100083;,2. 北京农业信息技术研究中心,北京 100097; 3. 国家农业信息化工程技术研究中心,北京 100097; 4. 农业部农业信息技术重点实验室,北京 100097;,2. 北京农业信息技术研究中心,北京 100097; 3. 国家农业信息化工程技术研究中心,北京 100097; 4. 农业部农业信息技术重点实验室,北京 100097;,2. 北京农业信息技术研究中心,北京 100097; 3. 国家农业信息化工程技术研究中心,北京 100097; 4. 农业部农业信息技术重点实验室,北京 100097; |
| |
| 基金项目： | 863计划"作物需水信息采集与智能控制灌溉技术"（2011AA100509） |
| |
| 摘 要： | 土壤墒情自动监测设备能够快速、高效、连续地观测土壤墒情数据,但由于受安装调试水平、设备自身状态、以及田间环境变化的影响,在长期连续监测中输出数据的准确性和稳定性会逐渐降低,不利于墒情监测业务的开展。本文以北京市昌平区土壤墒情的人工和自动同步观测数据为基础,通过分析土壤墒情自动监测数据的误差特点,构建了一元一次、一元二次和一元三次差分方程对自动监测数据进行误差修正,并对修正后的误差特征进行分析。结果表明:经过差分修正后,20 cm深度的绝对误差均值减小了34%和24%,40 cm深度的绝对误差均值减小了67%和54%,自动监测数据误差显著下降;3种差分方程中线性差分方程表现最优;修正后的误差统计特性符合简单随机误差,可以通过求算数平均数的方法进一步降低误差。通过差分方法来修正自动监测数据简单易行,能有效的提高自动监测数据精度,充分能够发挥人工和自动监测的优势,提高监测体系整体性能。
|
| 关 键 词： | 土壤 墒情 误差分析 自动监测 误差修正 差分方程 |
| 收稿时间： | 2014/9/28 0:00:00 |
| 修稿时间： | 2014/12/30 0:00:00 |
Error correction method of automatic monitoring soil moisture data based on differential equation |
| |
| Hao Xingyao,Feng Zhongke,Zhao Chunjiang,Li Shuhua and Gao Bingbo.Error correction method of automatic monitoring soil moisture data based on differential equation[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(3):152-158. |
| |
| Authors: | Hao Xingyao Feng Zhongke Zhao Chunjiang Li Shuhua and Gao Bingbo |
| |
| Institution: | 1. College of Forestry, Beijing Forestry University, Beijing, 10083, China; 2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China;,1. College of Forestry, Beijing Forestry University, Beijing, 10083, China;,2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China;,2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; and 2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; |
| |
| Abstract: | Abstract: In practical work of soil moisture monitoring, the automatic monitoring devices provide faster, more efficient and continuous observations compared with manual drying method. Thus, the automatic monitoring, which bases on Frequency Domain Reflectometry (FDR) or Time Domain Reflectometry (TDR), is becoming the main technical means to achieve the goal of fast and continuous monitoring. However, the automatic monitoring data is less stable and accurate because of installation and tuning situation, equipment aging and farm environment changing. Automatic monitoring data error can be divided into two categories: random error and systematic error. Random error complies normal distribution and its mean tends to zero. Systematic error does not have statistical characteristics, but it usually has certain regularity in value and continuity in time. For the time series data of automatic monitoring, error from one time point is relevant to the data errors of previous and subsequent time points. Thus, the random error can be reduced by averaging multiple measurements and the impact of systematic error can be reduced by differential equation correction. Soil moisture values measured by manual drying method are generally considered as the most accurate and reliable data, therefore it provides the possibility to correct the values measured by automatic monitor. The method proposed in this study is based on the numerical analysis of errors, regardless the specific causes. This scheme avoids the complex process of locating each error source and analyzing its numerical impact. The data analyzed in this paper included soil moisture values measured by manual drying method and automatic devices in the same period and same area but with different time intervals. The manual data was acquired every half-month and automatic data was acquired hourly. The daily means of automatic monitoring data were calculated to match manual data. Taking the manual data as true values, the errors of automatic monitoring data in corresponding days can be calculated. The error analysis results showed that the automatic monitoring soil moisture data contains apparent systematic error and the absolute mean error exceeded 5% of the operational requirement. The differential equations to correct the automatic monitoring data were established in forms of linear equation, quadratic equation and cubic equation. The argument of equations is measuring time and the dependent variable of equations is error estimation. The automatic monitoring data errors before and after correction were compared. The result indicated that the absolute mean error decreased 34% and 24% in 20 cm depth and decreased 67% and 54% in 40cm depth, and all the absolute mean errors satisfy the 5% of the operational requirement. Moreover, the error after correction is normally distributed. The residual error is mainly the simple random error which could be further reduced by calculating the monthly average and ten days average. Among the three equation forms, linear differential equation has the best correction performance altogether. The correction method using differential equation is easy to implement and can effectively improve the accuracy of automatic monitoring data. With this method, the synchronistical observation of soil moisture can give full play to the advantages of both manual and automatic data collecting methods and improve the overall performance of monitoring system. |
| |
| Keywords: | soils moisture error analysis automatic monitoring error correction differential equation |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
