土壤重金属含量高光谱反演

doi:10.16258/j.cnki.1674-5906.2023.01.019

生态环境学报 ›› 2023, Vol. 32 ›› Issue (1): 175-182.DOI: 10.16258/j.cnki.1674-5906.2023.01.019

土壤重金属含量高光谱反演

肖洁芸¹(), 周伟¹^,^*(), 石佩琪²^,³

1.西南大学地理科学学院/重庆金佛山喀斯特生态系统国家野外科学观测研究站，重庆 400715
2.上海师范大学环境与地理科学学院，上海 200234
3.重庆交通大学建筑与城市规划学院，重庆 400074

收稿日期:2022-10-21 出版日期:2023-01-18 发布日期:2023-04-06
通讯作者: *周伟，副教授，主要从事生态环境遥感监测和3S技术集成研究。E-mail: zw20201109@swu.edu.cn
作者简介:肖洁芸（2000年生），女，硕士研究生，主要从事土壤污染评价。E-mail: xjy513930@email.swu.edu.cn
基金资助:
重庆市自然科学基金面上项目(cstc2021jcyj-msxmX0384);中央高校基本科研业务费专项资金资助项目(SWU020015);国家自然科学基金项目(41501575);国家自然科学基金项目(41977337)

Hyperspectral Inversion of Soil Heavy Metals

XIAO Jieyun¹(), ZHOU Wei¹^,^*(), SHI Peiqi²^,³

1. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station/School of Geographical Sciences, Southwest University, Chongqing 400715, P. R. China
2. School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, P. R. China
3. College of Architecture and Urban Planning, Chongqing Jiaotong University, Chongqing 400074, P. R. China

Received:2022-10-21 Online:2023-01-18 Published:2023-04-06

摘要/Abstract

摘要：

土壤重金属含量估算对于区域土壤质量评估和土壤污染修复具有重要的科学意义，也是保障粮食安全的重要支撑。为估算重庆市耕地中土壤重金属铜（Cu）、锌（Zn）、铬（Cr）、镍（Ni）、铅（Pb）的含量，探讨利用土壤光谱进行重金属含量反演的可行性，基于土壤样品的室内测试高光谱数据和重金属含量，首先进行土壤重金属统计特征和赋存关系分析，然后对原始光谱数据分别进行一阶微分（FD）、二阶微分（SD）、倒数对数（LR）及去包络线（CR）变换，分析土壤重金属含量与光谱变量之间的相关性，从而确定土壤光谱特征波段；利用偏最小二乘回归（Partial Least Square Regression，PLSR）和支持向量机（Support Vector Machine，SVM）进行重金属含量的反演建模，对建模集和验证集进行模型精度和稳定性分析；根据模型精度对比分析，确定不同重金属反演的最佳光谱变换和模型组合。结果表明，土壤重金属元素与Fe、Mn元素、土壤有机质之间存在显著正相关关系（P<0.05）；土壤光谱与重金属元素间存在显著相关的波长主要包括445、530、1002、1414、1913、2218、2320 nm；PLSR建模与LR变量组合模拟结果的精度较高，但是总体上该模型对5种重金属含量的建模精度都较低，尤其对于含量比较低的Cr、Ni和Pb元素，不具备估算能力；而SVM模拟精度整体优于PLSR，且基于CR光谱变换的SVM模型对5种重金属元素的反演精度最高（r_M²介于0.68—0.86之间），SVM模型更加具有稳定性。土壤光谱可为本研究区土壤重金属含量估算提供重要手段，也可为西南地区土壤重金属含量监测及土壤环境评价提供参考。

关键词: 土壤重金属, 高光谱, 反演, 支持向量机, 偏最小二乘

Abstract:

Estimation of heavy metal content in soil has important scientific significance for regional soil quality assessment and soil pollution remediation, as well as providing important support for ensuring food security. In order to estimate the level of Copper (Cu), zinc (Zn), nickel (Ni) and lead (Pb) in the sloping farmland of Chongqing, and to explore the feasibility of retrieving heavy metal content using soil spectra, this study firstly analyzed the statistical characteristics and occurrence relationship of soil heavy metals based on the hyperspectral data of soil samples and the laboratory test results. The original spectral variables were transformed into first-order differential (FD), second-order differential (SD), logarithmic reciprocal (LR) and continuum removal (CR) respectively to analyze the correlation between soil heavy metal content and spectral variables, thus the characteristic band of the soil spectrum could be determined. Partial least square regression (PLSR) and support vector machine (SVM) were used to invert the heavy metal content, and the accuracy and stability of the modeling set and verification set were analyzed. Finally, the optimal spectral transformation and model combination of each heavy metal inversion were determined through the comparative analysis of model accuracy. The results showed that there were significant positive correlations (P<0.05) between soil heavy metals, Fe, Mn and soil organic matter. There were significant correlations between wavelength (around 445, 530, 1002, 1414, 1913, 2218 and 2320 nm) and heavy metals. The accuracy of PLSR modeling and LR variable combination simulation results was higher, but in general, the model did not have the ability to estimate elements with relatively low content especially for Cr, Ni and Pb. The overall accuracy of SVM simulation was better and more stable than that of PLSR. The SVM model based on CR had the highest inversion accuracy for the five heavy metal elements (r_M² is between 0.68 and 0.86), and the content of heavy metals could be effectively retrieved by soil spectrum. Soil spectroscopy can provide an important means for estimating soil heavy metal content in this study area, and can also provide a reference for soil heavy metal content monitoring and soil environmental assessment in Southwest China.

Key words: soil heavy metals, hyperspectral, inversion, support vector machine, partial least squares

中图分类号:

X833

肖洁芸, 周伟, 石佩琪. 土壤重金属含量高光谱反演[J]. 生态环境学报, 2023, 32(1): 175-182.

XIAO Jieyun, ZHOU Wei, SHI Peiqi. Hyperspectral Inversion of Soil Heavy Metals[J]. Ecology and Environment, 2023, 32(1): 175-182.

图/表 8

表1 研究区土壤重金属质量分数统计分析

Table 1 Statistics analysis of soil heavy metal mass fraction in study area mg·kg-1

土壤重金属	最小值	最大值	平均值	标准差	变异系数	偏度	峰度
Cu	15.950	103.860	32.149	19.164	0.596	2.853	7.913
Zn	52.860	216.270	104.046	30.115	0.289	1.663	3.234
Cr	26.930	102.580	64.391	14.368	0.223	0.433	1.293
Ni	8.578	41.444	28.082	7.396	0.263	-0.275	0.147
Pb	17.020	79.414	32.560	13.612	0.418	2.024	3.953
Fe	13320	40386.140	32282.602	4803.021	0.149	-1.343	3.857
Mn	281.25	1152.970	656.201	165.696	0.253	1.036	2.235

表2 重庆市土壤有机质和重金属含量相关系数矩阵图

Table 2 Correlation coefficients for organic matters and heavy metals in soil of Chongqing

土壤有机质与重金属	SOM	Fe	Mn	Cu	Zn	Pb	Cr	Ni
SOM	1
Fe	0.480	1
Mn	-0.131	0.338**	1
Cu	0.340**	0.495**	0.260*	1
Zn	0.439**	0.537**	0.202	0.596**	1
Pb	0.591**	0.282*	0.278*	0.391**	0.719**	1
Cr	-0.049	0.824**	0.388**	0.569**	0.350**	0.144	1
Ni	-0.108*	0.723**	0.335**	0.575**	0.351**	0.124	0.827**	1

图1 土壤样品原始光谱曲线

Figure 1 The original spectral curve of the soil sample

图2 Cu、Zn、Cr、Ni和Pb元素含量与5种光谱变换的相关系数

Figure 2 Correlation between the contents of Cu, Zn, Cr, Ni and Pb and five spectral transformations

图3 基于偏最小二乘法的5种重金属含量模型精度散点图

Figure 3 Scatter plot of prediction accuracy of five heavy metal content models based on partial least squares

表3 基于偏最小二乘法的土壤重金属含量模型预测精度

Table 3 Scatter plot of prediction accuracy of four heavy metal content models based on partial least squares

土壤重金属	光谱指标	建模集		验证集
土壤重金属	光谱指标	r_M²	σ_RMSEM	r_V²	σ_RMSEV
Cu	倒数对数	0.402	7.831	0.293	6.495
Zn	倒数对数	0.397	19.280	0.315	14.100
Cr	倒数对数	0.208	12.980	0.177	12.036
Ni	二阶微分	0.430	5.380	0.209	5.650
Pb	倒数对数	0.271	10.306	0.018	8.726

图4 基于支持向量机的5种重金属含量模型预测精度散点图

Figure 4 Scatter plot of prediction accuracy of five heavy metal content models based on support vector machine

表4 基于支持向量机的土壤重金属含量模型预测精度

Table 4 Prediction accuracy of soil heavy metal content model based on support vector machine

土壤重金属	光谱指标	建模集		验证集
土壤重金属	光谱指标	r_M²	σ_RMSEM	r_V²	σ_RMSEV
Cu	去包络线	0.711	5.503	0.680	5.216
Zn	去包络线	0.686	15.526	0.434	12.643
Cr	去包络线	0.859	5.927	0.447	8.712
Ni	去包络线	0.820	3.213	0.417	4.243
Pb	去包络线	0.811	4.985	0.598	5.773

参考文献 47

[1]	CHENG N C, ZHENG Y J, HE X F, et al., 2017. Analysis of the Report on the national general survey of soil contamination[J]. Journal of Agro-Environment Science, 36(9): 1689-1692.
[2]	COLLOBERT R, BENGIO S, 2001. SVMTorch: Support vector machines for large-scale regression problems[J]. Journal of Machine Learning Research, 1(2): 143-160.
[3]	DIAN Y Y, LE Y, FANG S H, et al., 2016. Influence of spectral bandwidth and position on chlorophyll content retrieval at leaf and canopy levels[J]. Journal of the Indian Society of Remote Sensing, 44(4): 583-593. DOI URL
[4]	GUO D D, HUANG S M, ZHANG S Q, et al., 2014. Comparative analysis of various hyperspectral prediction models of fluvo-aquic soil organic matter[J]. Nongye Gongcheng Xuebao (Beijing), 30(21): 192-200.
[5]	HE J L, CUI J L, ZHANG S Y, et al., 2019. Hyperspectral estimation of heavy metal Cu content in soil based on partial least square method[J]. Remote Sensing Technology and Application, 34(5): 998-1004.
[6]	HOU L, LI X J, LI F, 2019. Hyperspectral-based inversion of heavy metal content in the soil of coal mining areas[J]. Journal of Environmental Quality, 48(1): 57-63. DOI PMID
[7]	LIU M L, LIU X N, WU L, et al., 2011. Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance[J]. Computers & Geosciences. 37(9): 1254-1263. DOI URL
[8]	LUO Y, XU L, RYSZ M, et al., 2011. Occurrence and transport of tetracycline, sulfonamide, quinolone, and macrolide antibiotics in the Haihe River Basin, China[J]. Environmental Science and Technology, 45(5): 1827-1833. DOI PMID
[9]	MA W B, TAN K, LI H D, et al., 2016. Hyperspectral inversion of heavy metals in soil of a mining area using extreme learning machine[J]. Journal of Ecology and Rural Environment, 32(2): 213-218.
[10]	MEVIK B H, WEHRENS R, 2007. The PLS package: Principal component and partial least squares regression in R[J]. Journal of Statistical Software, 18(2): 1-23.
[11]	MOROS J, VALLEJUELO S F D, GREDILLA A, et al., 2009. Use of reflectance infrared spectroscopy for monitoring the metal content of the estuarine sediments of the Nerbioi-Ibaizabal river (Metropolitan bilbao, bay of bis-cay, basque country)[J]. Environmental Science & Technology, 43(24): 9314-9320. DOI URL
[12]	PAN L X, LIU Y, LIN M L, 2017. Pollution evaluation of soil heavy metal in coal mining area[J]. Coal Technology, 36(7): 301-303.
[13]	PENG X H, ZHU Q H, ZHANG Z B, et al., 2017. Combined turnover of carbon and soil aggregates using rare earth oxides and isotopically labelled carbon as tracers[J]. Soil Biology and Biochemistry, 109: 81-94. DOI URL
[14]	SUMMERS D, LEWIS M, OSTENDORF B, et al., 2011. Visible near infrared reflrctance spectroscopy as a predictive indicator of soil properties[J]. Ecological Indicators, 11(1): 123-131. DOI URL
[15]	TU Y L, ZOU B, JIANG X L, et al., 2018. Hyperspectral remote sensing based modeling of Cu content in mining soil[J]. Spectroscopy and Spectral Analysis, 38(2): 575-581.
[16]	VEGA F A, COVELO E F, ANDRADE M L, et al., 2004. Relationships between heavy metals content and soil properties in minesoils[J]. Analytica Chimica Acta, 524(1-2): 141-150. DOI URL
[17]	VISCARRA ROSSEL R A, BUI E N, CARITAT P D, et al., 2010. Mapping iron oxides and the color of Australian soil using visible-near-infrared reflectance spectra[J]. Journal of Geophysical Rearch, 115(F04031): 001645.
[18]	WU Y Z, CHEN J, WU X M, et al., 2005. Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils[J]. Applied Geochemistry, 20(6): 1051-1059. DOI URL
[19]	XU M X, WU S H, ZHOU S L, 2011. Hyperspectral reflectance models for retrieving heavy metal content: Application in the archaeological soil[J]. Journal of Infrared, Millimeter, and Terahertz Waves, 30(2): 109-114. DOI URL
[20]	ZHOU W, LI H R, WEN S Y, et al., 2022. Simulation of soil organic carbon content based on laboratory spectrum in the Three-Rivers Source Region of China[J]. Remote Sensing, 14: 1521. DOI URL
[21]	ZHOU W, YANG H, XIE L J, et al., 2021. Hyperspectral inverion of soil heavy metals in Three-Rivers Source Region based on random forest model[J]. Catena, 202: 105222. DOI URL
[22]	柴磊, 王新, 马良, 等, 2020. 基于PMF模型的兰州耕地土壤重金属来源解析[J]. 中国环境科学, 40(9): 3919-3929.
	CHAI L, WANG X, MA L, et al., 2020. Sources appointment of heavy metals in cultivated soils of Lanzhou based on PMF models[J]. China Environmental Science, 40(9): 3919-3929.
[23]	陈凤娇, 2020. 农用地土壤重金属Cu、Zn含量的高光谱反演研究[D]. 成都: 成都理工大学.
	CHEN F J, 2020. Studies on Cu and Zn content of agricultural land soil based on the hyperspectrum estimation model[D]. Chengdu: Chengdu University of Technology.
[24]	程先锋, 宋婷婷, 陈玉, 等, 2017. 滇西兰坪铅锌矿区土壤重金属含量的高光谱反演分析[J]. 岩石矿物学杂志, 36(1): 60-69.
	CHENG X F, SONG T T, CHEN Y, et al., 2017. Retrival and analysis of heavy metal content in soil base on measured spectra in the Lanping Zn-Pb mining area, western Yunnan Province[J]. Acta Petrologica Et Mineralogica, 36(1): 60-69.
[25]	龚绍琦, 王鑫, 沈润平, 等, 2010. 滨海盐土重金属含量高光谱遥感研究[J]. 遥感技术与应用, 25(2): 169-177.
	GONG S Q, WANG X, SHEN R P, et al., 2010. Study on heavy metal element content in the coastal saline soil by hyperspectral remote sensing[J]. Remote Sensing Technology and Application, 25(2): 169-177.
[26]	古丽扎提·艾买提, 阿不都拉·阿不力孜, 茹克亚·沙吾提, 等, 2018. 准东煤田土壤铅含量高光谱估算[J]. 土壤通报, 49(5): 1233-1239.
	GULZAT A, ABDULLA A, RUKIYA S, et al., 2018. Estimating soil lead content in the Eastern Junggar coalfield using hyperspectral data[J]. Chinese Journal of Soil Science, 49(5): 1233-1239.
[27]	贺军亮, 张淑媛, 查勇, 等, 2015. 高光谱遥感反演土壤重金属含量研究进展[J]. 遥感技术与应用, 30(3): 407-412.
	HE J L, ZHANG S Y, ZHA Y, et al., 2015. Review of retrieving soil heavy metal content by hyperspectral remote sensing[J]. Remote Sensing Technology and Application, 30(3): 407-412.
[28]	刘晖, 刘杰, 张玉玲, 等, 2021. 外源水稻根系和茎叶碳氮在稻田土壤中释放的特征[J]. 土壤学报, 58(4): 989-997.
	LIU H, LIU J, ZHANG Y L, et al., 2021. Release of exogenous carbon and nitrogen in rice root, stem and leaf in paddy soil[J]. Acta Pedologica Sinica, 58(4): 989-997.
[29]	鲁如坤, 2000. 土壤农业化学分析方法[M]. 北京: 中国农业科技出版社:474.
	LU R K, 2000. Methods for chemical analysis of soil agriculture[M]. Beijing: China Agricultural Science and Technology Press:474.
[30]	刘彦平, 罗晴, 程和发, 2020. 高光谱遥感技术在土壤重金属含量测定领域的应用和发展[J]. 农业环境科学学报, 39(12): 2699-2709.
	LIU Y P, LUO Q, CHENG H F, 2020. Application and development of hyperspectral remote sensing technology to determine the heavy metal content in soil[J]. Journal of Agro-Environment Science, 39(12): 2699-2709.
[31]	马磊, 颜安, 2019. 基于地物光谱和Landsat8遥感影像的土壤铅含量反演研究[J]. 山东农业科学, 51(12): 120-126.
	MA L, YAN A, 2019. Study on soil lead content inversion based on hyperspectral and Landsat8 remote sensing images[J]. Shandong Agricultural Sciences, 51(12): 120-126.
[32]	施建飞, 靳正忠, 周智彬, 等, 2022. 额尔齐斯河流域典型尾矿库区周边土壤重金属污染评价[J]. 生态环境学报, 31(5): 1015-1023. DOI URL
	SHI J F, JIN Z Z, ZHOU Z B, et al., 2022. Evaluation of heavy metal pollution in the soil around a typical tailing reservoir in Irtysh River Basin[J]. Ecology and Environmental Sciences, 31(5): 1015-1023.
[33]	石文静, 周翰鹏, 孙涛, 等, 2022. 矿区周边土壤重金属污染优先控制因子及健康风险评价研究[J]. 生态环境学报, 31(8): 1616-1628. DOI URL
	SHI W J, ZHOU H P, SUN T, et al., 2022. Research on priority control factors and health risk assessment of heavy metal pollution in soil around mining areas[J]. Ecology and Environmental Sciences, 31(8): 1616-1628.
[34]	石雨佳, 方林发, 方标, 等, 2022. 三峡库区 (重庆段) 菜地土壤重金属污染特征、潜在风险评估及源解析[EB/OL]. 环境科学, https://doi.org/10.13227/j.hjkx.202202204.
	SHI Y J, FANG L F, FANG B, et al., 2022. Pollution characteristics and source apportionment of heavy metals in vegetable field in the Three Gorges Reservoir area (Chongqing section)[EB/OL]. Environmental Science, https://doi.org/10.13227/j.hjkx.202202204.
[35]	滕靖, 何政伟, 倪忠云, 等, 2016. 西范坪矿区土壤铜元素的高光谱响应与反演模型研究[J]. 光谱学与光谱分析, 36(11): 3637-3642.
	TENG J, HE Z W, NI Z Y, et al., 2016. Spectral response and inversion models for prediction of total copper content in soil of Xifangping Ming area[J]. Spectroscopy and Spectral Analysis, 36(11): 3637-3642.
[36]	王维, 沈润平, 吉曹翔, 2011. 基于高光谱的土壤重金属铜的反演研究[J]. 遥感技术与应用, 26(3): 348-354.
	WANG W, SHEN R P, JI C X, 2011. Study on heavy metal Cu based on hyperspectral remote sensing[J]. Remote Sensing Technology and Application, 26(3): 348-354.
[37]	王雪梅, 玉米提·买明, 毛东雷, 等, 2021. 干旱区绿洲耕层土壤重金属铬含量的高光谱估测[J]. 生态环境学报, 30(10): 2076-2084. DOI URL
	WANG X M, YUMITI M, MAO D L, et al., 2021. Hyperspectral estimation of heavy metal chromium content in arable soil of arid area oasis[J]. Ecology and Environmental Sciences, 30(10): 2076-2084.
[38]	吴明珠, 李小梅, 沙晋明, 2014. 亚热带土壤铬元素的高光谱响应和反演模型[J]. 光谱学与光谱分析, 34(6): 1660-1666.
	WU M Z, LI X M, SHA J M, 2014. Spectral inversiong models for prediction of total chromium content in subtropical soil[J]. Spectroscopy and Spectral Analysis, 34(6): 1660-1666.
[39]	肖捷颖, 王燕, 张倩, 等, 2013. 土壤重金属含量的高光谱遥感反演方法综述[J]. 湖北农业科学, 52(6): 1248-1253.
	XIAO J Y, WANG Y, ZHANG Q, et al., 2013. Review on methods of monitoring soil heavy metal based on hyperspectral remote sensing data[J]. Hubei Agricultural Sciences, 52(6): 1248-1253.
[40]	解宪丽, 孙波, 郝红涛, 2007. 土壤可见光-近红外反射光谱与重金属含量之间的相关性[J]. 土壤学报, 44(6): 982-993.
	XIE X L, SUN B, HAO H T, 2007. Relationship between visible near infrared reflectance spectroscopy and heavy metal of soil concentration[J]. Acta Pedologica Sinica, 44(6): 982-993.
[41]	杨安, 王艺涵, 胡建, 等, 2020. 青藏高原表土重金属污染评价与来源解析[J]. 环境科学, 41(2): 886-894.
	YANG A, WANG Y H, HU J, et al., 2020. Evaluation and sorce of heavymetal pollution in surface soil of Qinghai-Tibet Plateau[J]. Environmental Science, 41(2): 886-894.
[42]	杨晗, 2020. 三江源区土壤重金属含量高光谱反演研究[D]. 重庆: 重庆交通大学.
	YANG H, 2020. Hyperspectral inversion of soil heavy metal content in the Three-River Source region[D]. Chongqing: Chongqing Jiaotong University.
[43]	杨杉, 汪军, 李洪刚, 等, 2018. 重庆市绿地土壤重金属污染特征及健康风险评价[J]. 土壤通报, 49(4): 966-972.
	YANG S, WANG J, LI H G, et al., 2018. Pollution characteristics and health risk assessment of heavy metals in green space of Chongqing City[J]. Chinese Journal of Soil Science, 49(4): 966-972.
[44]	张威, 高小红, 杨扬, 等, 2014. 基于光谱分析的土壤重金属含量估算研究——以三江源区玉树县和玛多县为例[J]. 土壤, 46(6): 1052-1060.
	ZHANG W, GAO X H, YANG Y, et al., 2014. Estimating heavy metal contents for topsoil based on spectral analysis: A case study of Yushu and Maduo counties in the Three-River Source Region[J]. Soils, 46(6): 1052-1060.
[45]	周伟, 李丽丽, 周旭, 等, 2021a. 基于地理探测器的土壤重金属影响因子分析及其污染风险评价[J]. 生态环境学报, 30(1): 173-180.
	ZHOU W, LI L L, ZHOU X, et al., 2021. Influence factor analysis of soil heavy metal based on geographic detector and its pollution risk assessment[J]. Ecology and Environmental Sciences, 30(1): 173-180.
[46]	周伟, 谢丽娟, 杨晗, 等, 2021b. 基于高光谱的三江源区土壤有机质含量反演[J]. 土壤通报, 52(3): 564-574.
	ZHOU W, XIE L J, YANG H, et al., 2021. Hyperspectral inversion of soil organic matter content in the Three-Rivers Source Region[J]. Chinese Journal of Soil Science, 52(3): 564-574.
[47]	卓荦, 2010. 基于高光谱遥感的土壤重金属空间分布研究[D]. 武汉: 武汉大学.
	ZHUO L, 2010. The research of estimating heavy metal spatial distribution of soil using hyperspectral data[D]. Wuhan: Wuhan University.

土壤重金属含量高光谱反演

Hyperspectral Inversion of Soil Heavy Metals

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