干旱区生态环境知识资源中心(Arid): 基于电磁感应数据的电导率反演模型研究

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	基于电磁感应数据的电导率反演模型研究
其他题名	Calculating Electrical Conductivity of Soil Using Electromagnetic Induction Data
	吴家林; 彭杰; 白建铎; 王佳文; 纪文君; 王楠
来源期刊	灌溉排水学报
ISSN	1672-3317
出版年	2021
卷号	40 期号:4 页码:1672-3317
中文摘要	【目的】土壤盐渍化是限制新疆南部棉花高产的主要因子,准确获取区域尺度土壤剖面盐分信息。【方法】以南疆阿拉尔垦区为研究区,以田间尺度采集的30个不同盐渍化程度棉田的540个样点的0~0.375、0~0.750、0~1.000 m的土壤剖面电导率数据和对应的电磁感应数据为数据源,采用线性模型和非线性模型分别构建了田间尺度和区域尺度的土壤剖面电导率的电磁感应反演模型,并采用缩减建模样本量方法进一步检验了区域尺度模型的可靠性和稳定性。【结果】多元线性回归(MLR)、偏最小二乘回归(PLSR)和主成分回归(PCR)建模方法的田间尺度模型R~2在0.88~0.95,而对应的区域尺度模型R~2在0.34~0.53。基于随机森林(RF)、神经网络(NN)和支持向量机(SVM)非线性建模方法构建的土壤剖面电导率的区域尺度电磁感应反演模型R~2在0.60~0.85,其中RF模型的精度最高。 0~0.375、0~0.750、0~1.000 m土壤剖面电导率的RF反演模型R~2分别为0.80、0.85和0.84,相较于线性建模方法的区域尺度模型精度有明显的提高。RF区域尺度模型的样本数量由540个缩减到240个,模型精度没有明显变化,表明采用区域尺度模型,可大幅度降低土壤剖面样本采集数量,从而可显著提高采样效率和降低采样成本。【结论】区域尺度下构建土壤剖面电导率反演模型时,随机森林建模方法效果较优,模型预测能力具有较高的可靠性。
英文摘要	【Background】Along with drought,soil salinization is one of the most important abiotic stresses facing agricultural production in arid and semi-arid regions like Xinjiang in northwest China.Saline soil is estimated to have reached 11 million hm~2 in Xinjiang,and is a major limiting factor in cotton industry in this region.Accurately measuring soil salinity is hence important to safeguard cotton production in Xinjiang【.Objective】The purpose of this paper is to present a new method to estimate salinity distribution at field and regional scales to help improve irrigation and cultivation management.【Method】The experiments were conducted at Alar reclamation area in southern Xinjiang.Electrical conductivity of 540 soil samples taken from 30 cotton fields with different salinization levels were measured in soil profile at 0~0.375,0~0.750,and 0~1.000 m depth.Using the electromagnetic induction data,linear and nonlinear models were constructed to inversely calculate the electrical conductivity of soil at field and regional scales respectively.Stability and reliability of the models was verified against ground-true data using the sample-size-reduction method.【Result】At field scale,the coefficient of determination R~2 associated with the multiple linear regression model (MLR),the partial least square regression (PLSR) model and the principal component regression model (PCR) varied from 0.88 to 0.95,while their associated R~2 for regional scale was from 0.34 to 0.53.The R~2 of the nonlinear model built on the random forest (RF),neural network (NN) and support vector machine (SVM) varied between 0.60 and 0.85,with the RF most accurate.The R~2 of the RF model for calculating the electrical conductivity of the soil profile at 0~0.375,0~0.750,0~1.000 m was 0.80,0.85 and 0.84,respectively.Compared with the linear model,the model for regional scale significantly improved the accuracy.The sample size in the RF model for regional scale was reduced from 540 to 240,while the accuracy remained almost unchanged,indicating the regional-scale model can reduce the number of soil profile without compromising modelling accuracy.【Conclusion】For constructing the inversion model for estimating soil-profile electrical conductivity at regional scale,the random forest method is most accurate.
中文关键词	电磁感应 ; 区域尺度 ; 土壤电导率 ; 反演模型
英文关键词	electromagnetic induction regional scale soil electrical conductivity inversion model
类型	Article
语种	中文
国家	中国
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:6954221
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/354320
作者单位	吴家林, 塔里木大学植物科学学院, 阿拉尔, 新疆 843300, 中国.; 彭杰, 塔里木大学植物科学学院, 阿拉尔, 新疆 843300, 中国.; 白建铎, 塔里木大学植物科学学院, 阿拉尔, 新疆 843300, 中国.; 王佳文, 塔里木大学植物科学学院, 阿拉尔, 新疆 843300, 中国.; 纪文君, 中国农业大学土地科学与技术学院, 北京 100083, 中国.; 王楠, 浙江大学环境与资源学院, 杭州, 浙江 310058, 中国.
推荐引用方式 GB/T 7714	吴家林,彭杰,白建铎,等. 基于电磁感应数据的电导率反演模型研究[J],2021,40(4):1672-3317.
APA	吴家林,彭杰,白建铎,王佳文,纪文君,&王楠.(2021).基于电磁感应数据的电导率反演模型研究.灌溉排水学报,40(4),1672-3317.
MLA	吴家林,et al."基于电磁感应数据的电导率反演模型研究".灌溉排水学报 40.4(2021):1672-3317.