| 引用本文: | 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].灌溉排水学报,2021,(4):80-87. |
| WU Jialin,PENG Jie,BAI Jianduo,et al.基于电磁感应数据的电导率反演模型研究[J].灌溉排水学报,2021,(4):80-87. |
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| 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R2在0.88~0.95，而对应的区域尺度模型R2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R2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R2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。 |
| 关键词: 电磁感应；区域尺度；土壤电导率；反演模型 |
| DOI：10.13522/j.cnki.ggps.2020503 |
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| Calculating Electrical Conductivity of Soil Using Electromagnetic Induction Data |
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WU Jialin, PENG Jie, BAI Jianduo, WANG Jiawen, JI Wenjun, WANG Nan
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1.College of Plant Sciences, Tarim University, Alar 843300, China;2.College of Land Science and Technology, China Agricultural University, Beijing 100083, China;3.College of Environment and Resources, Zhejiang University, Hangzhou 310058, China
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| Abstract: |
| 【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 hm2 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 R2 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 R2 for regional scale was from 0.34 to 0.53. The R2 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 R2 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. |
| Key words: electromagnetic induction; regional scale; soil electrical conductivity; inversion model |
