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基于高光谱技术的黑土地微量金属元素探测方法及地学意义

王建华 左玲 李志忠 穆华一 周萍 杨佳佳 赵英俊 秦凯

王建华, 左玲, 李志忠, 等, 2021. 基于高光谱技术的黑土地微量金属元素探测方法及地学意义. 地质力学学报, 27 (3): 418-429. DOI: 10.12090/j.issn.1006-6616.2021.27.03.038
引用本文: 王建华, 左玲, 李志忠, 等, 2021. 基于高光谱技术的黑土地微量金属元素探测方法及地学意义. 地质力学学报, 27 (3): 418-429. DOI: 10.12090/j.issn.1006-6616.2021.27.03.038
WANG Jianhua, ZUO Ling, LI Zhizhong, et al., 2021. A detection method of trace metal elements in black soil based on hyperspectral technology: Geological implications. Journal of Geomechanics, 27 (3): 418-429. DOI: 10.12090/j.issn.1006-6616.2021.27.03.038
Citation: WANG Jianhua, ZUO Ling, LI Zhizhong, et al., 2021. A detection method of trace metal elements in black soil based on hyperspectral technology: Geological implications. Journal of Geomechanics, 27 (3): 418-429. DOI: 10.12090/j.issn.1006-6616.2021.27.03.038

基于高光谱技术的黑土地微量金属元素探测方法及地学意义

doi: 10.12090/j.issn.1006-6616.2021.27.03.038
基金项目: 

中国地质调查局地质调查项目 DD20190316

国际地学计划项目 IGCP-665

详细信息
    作者简介:

    王建华(1979-), 男, 助理研究员, 研究方向为遥感在土壤信息提取、评价中的应用。E-mail: wangjh@aircas.ac.cn

    通讯作者:

    穆华一(1974-), 女, 讲师, 长期从事地质科技管理和地质杂志英文编辑。E-mail: 1517993978@qq.com

  • 中图分类号: P237;X87

A detection method of trace metal elements in black soil based on hyperspectral technology: Geological implications

Funds: 

the Geological Survey Projects of China Geological Survey DD20190316

the International Geoscience Program IGCP-665

  • 摘要: 在土壤中重金属含量较低的情况下,重金属的高光谱特征响应非常微弱,不易构建精确的高光谱直接反演模型。为了解决上述问题,依据土壤化学变量间的理化性质,将重金属富集特征转移到与之相关的化学主量元素上,使重金属微弱的信息得以间接定量反演。文中以海伦市黑土土壤为研究对象,通过主成分分析、聚类分析确定了主量元素氧化铁(Fe2O3)与微量重金属As、Zn、Cd之间存在明显吸附赋存关系。选用偏最小二乘法构建了研究区氧化铁含量的最佳反演模型(决定系数为0.704,均方根误差为0.148,F检验为12.732),并利用氧化铁与As、Zn、Cd之间的赋存关系,通过神经网络构建了氧化铁预测值与重金属真实值间的非线性拟合模型,得出As含量的拟合程度最高,Zn的拟合程度较好,Cd的拟合效果较理想,总体相关性分别为0.796、0.732、0.530。研究结果表明,基于氧化铁含量的间接预测模型能对微量重金属As、Zn、Cd进行较好的定量预测,为微量重金属含量的定量分析提供了新的方法参考,为高光谱遥感技术预测土壤重金属含量提供了依据,增强了土壤微量重金属反演可行性,对细化自然资源质量监测、深化开展地学系统综合分析与评价有重要意义。

     

  • 图  1  研究方法流程图

    Figure  1.  Flow chart of the research method

    图  2  土壤化学变量聚类分析树状图

    Figure  2.  Cluster analysis tree diagram of the selected chemical variables in soil

    图  3  土壤原始反射波谱与粘土矿物波谱特征对比图

    Figure  3.  Comparison of original reflection spectrum of soil and spectrum of clay minerals

    图  4  基于PLSR模型的氧化铁预测值与实测值散点图

    Figure  4.  Scatter plot of predicted and measured values of iron oxide based on PLSR model

    图  5  重金属As、Zn、Cd含量拟合曲线模型及预测结果精度评价

    Figure  5.  Fitting curve model for heavy metal contents of As, Zn and Cd and accuracy evaluation of the predicted results

    表  1  土壤重金属含量的统计特征

    Table  1.   Statistical characteristics of heavy metal contents in soil

    重金属
    Heavy Metal
    最小值
    Min/(mg/kg)
    最大值
    Max/(mg/kg)
    均值
    Mean/(mg/kg)
    标准差
    Standard Deviation(SD)/(mg/kg)
    变异系数
    Coefficient of Variation(CV)/%
    单项污染指数
    Average of Pollution index (Pi)
    背景值
    (张慧等,2018)
    Background Values (BV)/(mg/kg)
    GB 15618-2018
    风险筛选值/(mg/kg)
    Cd镉 0.065 0.123 0.089 0.007 0.079 0.297 0.078 0.3
    As砷 5.136 10.703 8.164 0.764 0.094 0.204 9.282 40
    Hg汞 0.020 0.089 0.029 0.010 0.345 0.016 0.016 1.8
    Cr铬 52.549 124.378 72.930 13.025 0.179 0.486 50.583 150
    Cu铜 13.006 71.859 17.794 6.083 0.342 0.356 18.683 50
    Pb铅 16.100 68.741 20.225 8.513 0.421 0.225 22.652 90
    Ni镍 20.171 89.038 29.238 9.781 0.335 0.418 24.037 70
    Zn锌 46.469 80.848 58.668 6.579 0.112 0.293 57.112 200
    注:1.样本数目Number=111;2.CV=SD/Mean;3.平均污染指数Pi=Mean/评价标准值,“评价标准值”(GB15618-1995);4.风险筛选值选取(pH:5.5<pH≤7.5)为评价标准值
    下载: 导出CSV

    表  2  土壤化学变量的Pearson相关系数矩阵

    Table  2.   Pearson correlation coefficient matrix for the selected chemical variables in soil

    土壤化学变量
    (Soilchemicalvariable)
    Cd As Hg Cr Cu Pb Ni Zn pH SOM Fe2O3
    Cd 1
    As 0.398** 1
    Hg 0.046 0.043 1
    Cr 0.210* 0.302** 0.427** 1
    Cu 0.029 0.203* 0.153 0.336** 1
    Pb -0.014 -0.006 0.201* 0.355** 0.450** 1
    Ni 0.085 0.134 0.583** 0.737** 0.253* 0.178 1
    Zn 0.263** 0.473** 0.128 0.351** 0.337** 0.064 0.245** 1
    pH 0.183 -0.012 -0.060 -0.061 -0.068 -0.037 -0.087 0.083 1
    SOM 0.071 0.019 0.070 -0.015 0.103 0.014 0.087 0.298** 0.296** 1
    Fe2O3 0.417** 0.762** 0.158 0.376** 0.345** 0.112 0.195* 0.647** 0.004 0.157 1
    注:**—在0.01水平上显著相关;*—在0.05水平上显著相关
    下载: 导出CSV

    表  3  土壤化学变量的主成分分析结果

    Table  3.   Principal component analysis of the selected chemical variables in soil

    土壤化学变量
    Soil chemical variable
    旋转前矩阵Component matrix 旋转后矩阵Rotated component matrix
    PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4
    Cd 0.386 0.480 0.068 -0.276 0.079 0.628 -0.199 0.141
    As 0.589 0.555 -0.297 -0.208 0.060 0.875 0.029 -0.126
    Hg 0.743 -0.555 0.217 -0.251 0.979 0.067 0.080 0.011
    Cr 0.814 -0.335 0.007 -0.092 0.792 0.285 0.269 -0.057
    Cu 0.537 -0.059 -0.234 0.614 0.151 0.223 0.807 0.015
    Pb 0.343 -0.277 -0.206 0.680 0.136 -0.076 0.822 -0.017
    Ni 0.743 -0.555 0.217 -0.251 0.979 0.067 0.080 0.011
    Zn 0.661 0.420 0.062 0.095 0.184 0.684 0.236 0.261
    Fe2O3 0.712 0.548 -0.214 -0.019 0.108 0.891 0.215 0.017
    pH -0.018 0.326 0.702 0.165 -0.085 0.039 -0.123 0.777
    SOM 0.205 0.244 0.685 0.328 0.061 0.090 0.118 0.807
    特征值 3.655 1.988 1.296 1.238 2.652 2.579 1.583 1.363
    方差贡献率/% 33.226 18.072 11.783 11.254 24.109 23.442 14.391 12.392
    累计方法贡献率/% 33.226 51.298 63.081 74.334 24.109 47.551 61.942 74.334
    下载: 导出CSV

    表  4  土壤化学变量相关性分类表

    Table  4.   Correlation table of chemical variables in soil

    组别
    Group
    土壤化学变量
    Soil chemical variable
    个数
    Number
    第一类 Cd、As、Zn、氧化铁 4
    第二类 Hg、Cr、Ni 4
    第三类 Cu、Pb 2
    第四类 pH、SOM 2
    下载: 导出CSV

    表  5  土壤氧化铁含量高光谱反演模型对比分析

    Table  5.   Comparative analysis of hyperspectral inversion models for iron oxide content in soil

    模型
    Model
    波谱变换
    Spectral index
    决定系数
    R2
    均方根误差
    RMSE
    MLSR CR 0.443 3.311
    FDR 0.569 1.980
    BP CR 0.314 1.769
    FDR 0.632 2.750
    PLSR CR 0.704 0.148
    FDR 0.223 0.240
    下载: 导出CSV

    表  6  基于偏最小二乘法(PLSR)的土壤化学变量高光谱反演模型精度评价表

    Table  6.   Accuracy evaluation table of the hyperspectral inversion model of chemical variables in soil based on PLSR

    土壤化学变量
    Soil chemical variable
    光谱变换
    Spectral index
    自变量所在波段
    Independent variable band
    预测样本集Training:89 验证样本集Validation:22
    RMSE R2 F RMSE R2 RPD
    Cd CR 2246、1678、356、1680 0.008 0.184 5.680 0.007 0.012 0.939
    FDR 2204、1438、2490、1767、366、356、1376、2495 0.006 0.421 6.390 0.009 0.001 1.317
    As CR 2484、371、1679 0.709 0.237 8.811 0.543 0.110 1.152
    FDR 407、1642、382、393 0.672 0.316 9.695 0.629 0.031 1.216
    Zn CR 2331、2356 6.622 0.112 5.394 5.245 0.140 1.070
    FDR 377、363、366 7.075 0.073 4.702 5.285 0.140 0.999
    Fe2O3 CR 371、2475、2331、2362、2398、2438、2447、2417、2414、2382、2452、2207、2192、2418 0.148 0.704 12.723 0.110 0.657 1.700
    FDR 377、1642、1087、447 0.240 0.223 6.014 0.343 0.016 1.141
    下载: 导出CSV
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  • 收稿日期:  2021-02-09
  • 修回日期:  2021-05-10
  • 刊出日期:  2021-06-28

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