城市交通绿地土壤重金属含量的高光谱反演
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摘要
以交通绿地区土壤Cr、Pb、Zn为研究对象,分析重金属元素与土壤光谱在一阶、二阶等微分变换下的相关性,突出特征波段,通过多元线性逐步回归(MLR)和偏最小二乘回归(PLSR)方法,建立光谱与重金属元素含量的最佳模型,并进行精度检验与模型预测。结果表明:光谱微分变换可以有效地提高土壤重金属含量与土壤光谱反射率间的相关性,土壤Cr、Pb、Zn的最大特征波段分别在1 289.41、1 408.35、1 411.9 nm;从模型稳定性和精确性来看,土壤Cr、Pb、Zn在PLSR模型中的R~2分别为0.986、0.809、0.629,依次是MLR模型的1.32、1.14、1.12倍,土壤Cr、Pb、Zn在PLSR模型中的建模RMSE均低于MLR模型,PLSR模型效果较优;与土壤Cr相比,土壤Pb、Zn的MLR和PLSR模型效果较差,这可能与人为因素影响有关。
The soil Cr, Pb and Zn were the research object in the traffic green area. The correlation between heavy metal elements and soil spectra under different mathematical processing was analyzed by using hyperspectral remote sensing in order to prominent feature band. The model between reflection spectrum and heavy metal elements was established by MLR and PLSR methods. Meanwhile, accuracy test and model prediction were carried out. The results showed that the correlation between soil heavy metal content and soil spectral reflectivity can be improved effectively by spectral differential transformation.The characteristic bands corresponding to the maximum correlation coefficients of soil Cr, Pb, Zn are 1 289.41, 1 408.35 and 1 411.9 nm respectively. From the stability and accuracy of the model, the best model R~2 of soil Cr, Pb and Zn in the PLSR model are 0.986, 0.809 and 0.629 respectively, which are 1.32, 1.14 and 1.12 times of the MLR model.The model RMSE of soil Cr, Pb and Zn in the PLSR model is lower than the MLR model. Overall, the PLSR model is better than MLR model. Compared with soil Cr, the MLR and PLSR models of soil Pb and Zn are less effective, which may be related to the influence of human factors.
The soil Cr, Pb and Zn were the research object in the traffic green area. The correlation between heavy metal elements and soil spectra under different mathematical processing was analyzed by using hyperspectral remote sensing in order to prominent feature band. The model between reflection spectrum and heavy metal elements was established by MLR and PLSR methods. Meanwhile, accuracy test and model prediction were carried out. The results showed that the correlation between soil heavy metal content and soil spectral reflectivity can be improved effectively by spectral differential transformation.The characteristic bands corresponding to the maximum correlation coefficients of soil Cr, Pb, Zn are 1 289.41, 1 408.35 and 1 411.9 nm respectively. From the stability and accuracy of the model, the best model R~2 of soil Cr, Pb and Zn in the PLSR model are 0.986, 0.809 and 0.629 respectively, which are 1.32, 1.14 and 1.12 times of the MLR model.The model RMSE of soil Cr, Pb and Zn in the PLSR model is lower than the MLR model. Overall, the PLSR model is better than MLR model. Compared with soil Cr, the MLR and PLSR models of soil Pb and Zn are less effective, which may be related to the influence of human factors.
引文
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[13]张秋霞,张合兵,张会娟,等.粮食主产区耕地土壤重金属高光谱综合反演模型[J].农业机械学报,2017(3):148-155.Zhang Qiuxia,Zhang Hebing,Zhang Huijuan,et al.Hybrid inversion model of heavy metals with hyperspectral reflectance in cultivated soils of main grain producing areas[J].Transactions of the Chinese Society of Agriculural Machinery,2017(3):148-155.
[14]Kemper T,Sommer S.Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy[J].Environmental Science and Technology,2002(36):2742-2747.
[15]吴明珠,李小梅,沙晋明.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,2014(6):1660-1666.Wu Mingzhu,Li Xiaomei,Sha Jinming.Spectral inversion models for prediction of total chromium content in subtropical soil[J].Spectroscopy and Spectral Analysis,2014(6):1660-1666.
[16]邵田田,宋开山,杜嘉,等.基于偏最小二乘法的玉米FPAR高光谱反演模型研究[J].地理与地理信息科学,2012(3):27-31.Shao Tiantian,Song Kaishan,Du Jia,et al.Hyperspectral remote sensing modeling of FPAR for corn based on partial least squares(PLS)regression analysis[J].Geography and Geo-information Science,2012(3):27-31.
[17]李向,管涛,徐清.基于BP神经网络的土壤重金属污染评价方法-以包头土壤环境质量评价为例[J].中国农学通报,2012,28(2):250-256.Li Xiang,Guan Tao,Xu Qing.The evaluation of soil heavy metal pollution based on the BP neural network-taking soil environmental quality assessment in Baotou as an example[J].Chinese Agricultural Science Bulletin,2012,28(2):250-256.
[18]王天巍,蔡崇法,李朝霞,等.道路边际土壤重金属分布格局的神经网络模拟-以现代黄河三角洲为例[J].生态学报,2009,29(6):3154-3162.Wang Tianwei,Cai Chongfa,Li Zhaoxia,et al.Simulation of distribution pattern of heavy metals in road verge soils using neural network:a case study of modern Yellow River Delta[J].Acta Ecologica Sinica,2009,29(6):3154-3162.
[19]庄腾飞.上海市城市边缘区土壤重金属多尺度空间分布特征及污染评价[D].上海:上海师范大学,2012.Zhuang Tengfei.Multi-scale Spatial Distribution Characteristics and Pollution Assessment of Heavy Metals in Urban Marginal Areas of Shanghai[D].Shanghai:Shanghai Normal University,2012.
[20]Xu Binbin.Study on reflectance spectra of soil profiles[J].Soils,2000,32(6):281-287.
[21]王维,沈润平,吉曹翔.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,2011,26(3):348-354.Wang Wei,Shen Ruiping,Ji Caoxiang.Study on heavy metal Cu based on hyperspectral remote sensing[J].Remote Sensing Technology and Application,2011,26(3):348-354.
[22]Sutlohmann M,Raab T.Feasibility of Diffuse Reflectance Infrared Fourier Spectroscopy(DRIFTS)to Quantify Ironcyanide(Fe-CN)Complexes in Soil[C].EGU General Assembly Conference Abstracts,2017.
[23]栾福明,熊黑钢,王芳,等.荒漠-绿洲交错带土壤N、P、K含量的高光谱反演模型[J].中国沙漠,2014,34(5):1320-1328.Luan Fuming,Xiong Heigang,Wang Fang,et al.Hyperspectral reflectance inversion models on content of N,P,Kof soil in oasis-desert ecotone[J].Journal of Desert Research,2014,34(5):1320-1328.
[24]向颖.成都平原水稻土重金属铜和铅含量的高光谱反演研究[D].成都:四川农业大学,2015.Xiang Ying.Hyperspectral Inversion of Heavy Metal Copper and Lead in Paddy Soils in Chengdu Plain[D].Chengdu:Sichuan Agricultural University,2015.
[25]钟燕.基于HSI高光谱数据的耕地土壤重金属镉、铅含量遥感反演[D].成都:四川农业大学,2016.Zhong Yan.Remote Sensing Inversion of Heavy Metal Cadmium and Lead in Cultivated Soil Based on HSI Hyperspectral Data[D].Chengdu:Sichuan Agricultural University,2016.
[2]刘庆,孙景宽,陈印平,等.不同采样尺度下土壤重金属的空间变异特征[J].土壤通报,2009,40(6):1406-1410.Liu Qing,Sun Jingkuan,Chen Yinping,et al.Spatial variability of the soil heavy metal with different sampling scales[J].Chinese Journal of Soil Science,2009,40(6):1406-1410.
[3]季辉,赵健,冯金飞,等.高速公路沿线农田土壤重金属总量和有效态含量的空间分布特征及其影响因素分析[J].土壤通报,2013,44(2):477-483.Ji Hui,Zhao Jian,Feng Jinfei,et al.Spatial distribution characteristics of total and available heavy metal contents and their influencing factors in farmland soils along expressway[J].Chinese Journal of Soil Science,2013,44(2):477-483.
[4]张慧峰,钱枫,宋洋,等.城市交通对道路周边土壤重金属污染影响的研究[J].河北科技大学学报,2010,31(1):57-61.Zhang Huifeng,Qian Feng,Song Yang,et al.Study on heavy metal pollutions in roadside soil[J].Journal of Hebei University of Science and Technology,2010,31(1):57-61.
[5]Bo?ko Milo?,Aleksandra Bensa.Estimation of SOC content in anthropogenic soils from flysch deposits using Vis-NIRspectroscopy[J].Agriculturae Conspectus Scintificus,2018,2:149-153.
[6]彭杰,向红英,周清,等.土壤氧化铁的高光谱响应研究[J].光谱学与光谱分析,2013,33(2):502-506.Peng Jie,Xiang Hongying,Zhou Qing,et al.Influence of soil iron oxide on VNIR diffuse reflectance spectroscopy[J].Spectroscopy and Spectral Analysis,2013,33(2):502-506.
[7]Golayeh Y,Mehdi H,Akbar N A.Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy[J].Environmental Monitoring and Assessment,2018,190(9):513.
[8]宋迪思,盛浩,周清,等.不同母质发育土壤的中红外吸收光谱特[J].土壤通报,2016,47(1):1-7.Song Disi,Sheng Hao,Zhou Qing,et al.Characteristics of middle-infrared absorption spectrum of soils derived from different parent materials[J].Chinese Journal of Soil Science,2016,47(1):1-7.
[9]Mohamed E S,Saleh A M,Belal A B,et al.Application of near-infrared reflectance for quantitative assessment of soil properties[J].Egyptian Journal of Remote Sensing&Space Science,2018,21(1):1-14.
[10]程先锋,宋婷婷,陈玉,等.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,2017(1):60-69.Cheng Xianfeng,Song Tingting,Chen Yu,et al.Retrieval and analysis of heavy metal content in soil based on measured spectra in Lanping Zn-Pb mining area,western Yunnan Province[J].Acta Petrologica et Mineralogica,2017(1):60-69.
[11]郭熙,谢碧裕,叶英聪,等.基于一阶微分变换方法的南方丘陵稻田土壤电阻率高光谱特性研究[J].江西农业大学学报,2015,37(1):190-198.Guo Xi,Xie Biyu,Ye Yingcong,et al.The spectral characteristics of high soil resistivity in paddy fields in southern China hilly areas[J].Acta Agriculturae Universitatis Jiangxiensis,2015,37(1):190-198.
[12]李淑敏,李红,孙丹峰,等.利用光谱技术分析北京地区农业土壤重金属光谱特征[J].土壤通报,2011,42(3):730-735.Li Shumin,Li Hong,Sun Danfeng,et al.Characteristic and diagnostic bands of heavy metals in Beijing agricultural soils based on spectroscopy[J].Chinese Journal of Soil Science,2011,42(3):730-735.
[13]张秋霞,张合兵,张会娟,等.粮食主产区耕地土壤重金属高光谱综合反演模型[J].农业机械学报,2017(3):148-155.Zhang Qiuxia,Zhang Hebing,Zhang Huijuan,et al.Hybrid inversion model of heavy metals with hyperspectral reflectance in cultivated soils of main grain producing areas[J].Transactions of the Chinese Society of Agriculural Machinery,2017(3):148-155.
[14]Kemper T,Sommer S.Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy[J].Environmental Science and Technology,2002(36):2742-2747.
[15]吴明珠,李小梅,沙晋明.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,2014(6):1660-1666.Wu Mingzhu,Li Xiaomei,Sha Jinming.Spectral inversion models for prediction of total chromium content in subtropical soil[J].Spectroscopy and Spectral Analysis,2014(6):1660-1666.
[16]邵田田,宋开山,杜嘉,等.基于偏最小二乘法的玉米FPAR高光谱反演模型研究[J].地理与地理信息科学,2012(3):27-31.Shao Tiantian,Song Kaishan,Du Jia,et al.Hyperspectral remote sensing modeling of FPAR for corn based on partial least squares(PLS)regression analysis[J].Geography and Geo-information Science,2012(3):27-31.
[17]李向,管涛,徐清.基于BP神经网络的土壤重金属污染评价方法-以包头土壤环境质量评价为例[J].中国农学通报,2012,28(2):250-256.Li Xiang,Guan Tao,Xu Qing.The evaluation of soil heavy metal pollution based on the BP neural network-taking soil environmental quality assessment in Baotou as an example[J].Chinese Agricultural Science Bulletin,2012,28(2):250-256.
[18]王天巍,蔡崇法,李朝霞,等.道路边际土壤重金属分布格局的神经网络模拟-以现代黄河三角洲为例[J].生态学报,2009,29(6):3154-3162.Wang Tianwei,Cai Chongfa,Li Zhaoxia,et al.Simulation of distribution pattern of heavy metals in road verge soils using neural network:a case study of modern Yellow River Delta[J].Acta Ecologica Sinica,2009,29(6):3154-3162.
[19]庄腾飞.上海市城市边缘区土壤重金属多尺度空间分布特征及污染评价[D].上海:上海师范大学,2012.Zhuang Tengfei.Multi-scale Spatial Distribution Characteristics and Pollution Assessment of Heavy Metals in Urban Marginal Areas of Shanghai[D].Shanghai:Shanghai Normal University,2012.
[20]Xu Binbin.Study on reflectance spectra of soil profiles[J].Soils,2000,32(6):281-287.
[21]王维,沈润平,吉曹翔.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,2011,26(3):348-354.Wang Wei,Shen Ruiping,Ji Caoxiang.Study on heavy metal Cu based on hyperspectral remote sensing[J].Remote Sensing Technology and Application,2011,26(3):348-354.
[22]Sutlohmann M,Raab T.Feasibility of Diffuse Reflectance Infrared Fourier Spectroscopy(DRIFTS)to Quantify Ironcyanide(Fe-CN)Complexes in Soil[C].EGU General Assembly Conference Abstracts,2017.
[23]栾福明,熊黑钢,王芳,等.荒漠-绿洲交错带土壤N、P、K含量的高光谱反演模型[J].中国沙漠,2014,34(5):1320-1328.Luan Fuming,Xiong Heigang,Wang Fang,et al.Hyperspectral reflectance inversion models on content of N,P,Kof soil in oasis-desert ecotone[J].Journal of Desert Research,2014,34(5):1320-1328.
[24]向颖.成都平原水稻土重金属铜和铅含量的高光谱反演研究[D].成都:四川农业大学,2015.Xiang Ying.Hyperspectral Inversion of Heavy Metal Copper and Lead in Paddy Soils in Chengdu Plain[D].Chengdu:Sichuan Agricultural University,2015.
[25]钟燕.基于HSI高光谱数据的耕地土壤重金属镉、铅含量遥感反演[D].成都:四川农业大学,2016.Zhong Yan.Remote Sensing Inversion of Heavy Metal Cadmium and Lead in Cultivated Soil Based on HSI Hyperspectral Data[D].Chengdu:Sichuan Agricultural University,2016.