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陈杏子, 洪 明, 高 瑞, 景彦强, 肖 键, 李 杭.施肥对TDR土壤水分传感器测量精度的影响研究水资源与水工程学报[J].,2024,35(3):217-224
施肥对TDR土壤水分传感器测量精度的影响研究
Effect of fertilization on measurement accuracy of TDR soil moisture sensor
  
DOI:10.11705/j.issn.1672-643X.2024.03.25
中文关键词:  施肥  TDR土壤水分传感器  测量误差  标定模型  土壤含水率
英文关键词:fertilization  TDR soil moisture sensor  measurement error  calibration model  soil moisture content
基金项目:新疆自治区重点研发任务项目(2022B02011-1);新疆自治区重大科技专项(2020A01003-2);新疆水利科技专项资金项目(XSKJ-2023-18)
作者单位
陈杏子1,2, 洪 明1,2, 高 瑞1,2, 景彦强1,2, 肖 键1,2, 李 杭1,2 (1. 新疆农业大学 水利与土木工程学院 新疆 乌鲁木齐 830052 2.新疆水利工程安全与水灾害防治重点实验室 新疆 乌鲁木齐 830052) 
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中文摘要:
      为明析施肥对时域反射法(TDR)土壤水分传感器测量精度的影响规律,并提出适宜的标定方法,以提高其测量精度。以TDR土壤水分传感器作为研究对象,考虑土壤质地、土壤含水率、施肥量3个因素,选取新疆棉花主产区喀什的壤质砂土、克拉玛依的粉质壤土、昌吉的粉砂质黏壤土,设5个土壤含水率水平,5个施肥水平,共计75个处理;以烘干法为基准,建立了TDR土壤水分传感器标定模型,并进行验证。结果表明:在不同土壤含水率条件下,TDR传感器误差随着含水率的增加整体呈增大的趋势。同一含水率时,壤质砂土条件下的TDR传感器误差随施肥量的增加整体呈现增大的趋势;粉质壤土条件下的TDR传感器误差随施肥量的增加呈先减小后急剧增大的趋势;粉砂质黏壤土条件下的TDR传感器误差随施肥量的增加呈先增大后减小再增大的趋势。建立了TDR土壤水分传感器输出值与施肥量、含水率之间的多元回归预测模型并采用Logistic模型进行了标定,壤质砂土、粉质壤土、粉砂质黏壤土试验条件下标定后的平均绝对误差分别为-0.18%、1.46%、0.01%,表明考虑施肥量及含水率的变化对TDR传感器检测精度的提高有积极作用。
英文摘要:
      In order to improve the TDR soil moisture sensor’s measurement accuracy, the impact of fertilization on the measurement accuracy was analyzed and appropriate calibration methods were proposed. Taking the TDR soil moisture sensor as the research object, three factors of soil texture, soil moisture content and fertilization amount were considered. Loamy sand in Kashgar, silty loam in Karamay and silty clay loam in Changji which are the main soil types in the cotton producing areas of Xinjiang were selected, five soil moisture levels and five fertilization levels were set for the three different types of soil, accounting for a total of 75 treatments. Then, a calibration model for TDR soil moisture sensor was established based on the drying method and the simulation results were validated. The results show that under different soil moisture conditions, the error of the TDR sensor increased with the increase of moisture content. At the same water content, the error of the TDR sensor under the condition of loamy sand increased with the increase of fertilizer application. The error of the TDR sensor under the condition of silty loam decreased first and then increased sharply with the increase of fertilizer amount. Under the condition of silty clay loam, the error of the TDR sensor increased first, then decreased and then increased with the increase of fertilization amount. A multivariate prediction model was established for the output value of TDR soil moisture sensor under experimental conditions, which was then calibrated by logistic model. The average absolute errors after calibration were -0.18%, 1.46%, and 0.01%, respectively. This indicates that considering changes in fertilization and moisture content is conducive to improving the measurement accuracy of TDR sensors.
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