CAJ | 학술논문

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。
토양상정자동감측설비능구쾌속、고효、련속지관측토양상정수거，단유우수안장조시수평、설비자신상태、이급전간배경변화적영향，재장기련속감측중수출수거적준학성화은정성회축점강저，불리우상정감측업무적개전。본문이북경시창평구토양상정적인공화자동동보관측수거위기출，통과분석토양상정자동감측수거적오차특점，구건료일원일차、일원이차화일원삼차차분방정대자동감측수거진행오차수정，병대수정후적오차특정진행분석。결과표명：경과차분수정후，20 cm심도적절대오차균치감소료34%화24%，40 cm심도적절대오차균치감소료67%화54%，자동감측수거오차현저하강；3충차분방정중선성차분방정표현최우；수정후적오차통계특성부합간단수궤오차，가이통과구산수평균수적방법진일보강저오차。통과차분방법래수정자동감측수거간단역행，능유효적제고자동감측수거정도，충분능구발휘인공화자동감측적우세，제고감측체계정체성능。
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 equationsto 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.