黄土高原土壤铁元素含量遥感反演方法
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摘要
利用光谱技术可快速而高效地获取土壤组分及其空间分布信息,为准确估算黄土高原土壤Fe元素含量与分布特征,通过对榆林东部地区的典型黄土进行野外采集、室内理化分析、光谱测定及其预处理,分析土壤铁含量与反射光谱的相关性,筛选敏感波段,利用偏最小二乘法建模,确定最佳估算模型。光谱反射率及其不同变换后筛选出的敏感波段,主要分布在500、870、1 700、2 200 nm附近;原始反射率(Ref)建模结果相对较稳定且预测效果最佳,预测集相关系数R~2达0.73,均方根误差RMSEP最小;经导数变换(FDR和SDR)及连续统去除CR变换的模型次之,预测集R~2分别达到0.61和0.64;采用波段内插法将土壤Fe含量最优估算模型Ref应用Sentinel-2多光谱遥感影像上,得到土壤Fe含量遥感反演图,结果表明研究区土壤Fe含量的分布特征与地层密切相关;该研究结果可为土壤Fe元素含量遥感分析提供支撑,实现黄土高原土壤铁的光谱快速制图。
The information of soil composition and its spatial distribution could be obtained quickly and efficiently by using spectral technology.In order to accurately estimate the content and distribution characteristics of soil Fe elements in the loess plateau,the typical loess in the eastern part of Yulin was collected in the field.Laboratory physical and chemical analysis,spectral determination and pretreatment,analysis of the correlation between soil iron content and reflection spectrum,screening sensitive bands,using partial least squares modeling to determine the best estimation model.The spectral reflectivity and the selected sensitive bands are mainly distributed at 500 nm,870 nm,1 700 nm and 2 200 nm.The original reflectivity(Ref) modeling results are relatively stable and the prediction effect is the best.The prediction set correlation coefficient R~2 is up to 0.73 and the Root Mean Square Error(RMSEP) is the smallest.After derivative transform(FDR and SDR) and continuum removal of CR transform,the prediction set R~2 is 0.61 and 0.64,respectively.The optimal estimation model of soil Fe content(Ref) was applied to the Sentinel-2 multi-spectral remote sensing image to obtain the remote sensing inversion map of soil Fe content by band interpolation.It was found that the distribution characteristics of soil Fe content in the studied area were closely related to the strata.The results of this study can provide support for remote sensing analysis of soil Fe element content and realize rapid spectral mapping of soil iron in the Loess Plateau.
The information of soil composition and its spatial distribution could be obtained quickly and efficiently by using spectral technology.In order to accurately estimate the content and distribution characteristics of soil Fe elements in the loess plateau,the typical loess in the eastern part of Yulin was collected in the field.Laboratory physical and chemical analysis,spectral determination and pretreatment,analysis of the correlation between soil iron content and reflection spectrum,screening sensitive bands,using partial least squares modeling to determine the best estimation model.The spectral reflectivity and the selected sensitive bands are mainly distributed at 500 nm,870 nm,1 700 nm and 2 200 nm.The original reflectivity(Ref) modeling results are relatively stable and the prediction effect is the best.The prediction set correlation coefficient R~2 is up to 0.73 and the Root Mean Square Error(RMSEP) is the smallest.After derivative transform(FDR and SDR) and continuum removal of CR transform,the prediction set R~2 is 0.61 and 0.64,respectively.The optimal estimation model of soil Fe content(Ref) was applied to the Sentinel-2 multi-spectral remote sensing image to obtain the remote sensing inversion map of soil Fe content by band interpolation.It was found that the distribution characteristics of soil Fe content in the studied area were closely related to the strata.The results of this study can provide support for remote sensing analysis of soil Fe element content and realize rapid spectral mapping of soil iron in the Loess Plateau.
引文
[1] Viscarra Rossel R A,Bui E N,De Caritat P,et al.Mapping Iron Oxides and the Color of Australian Soil Using Visible-near-infrared Reflectance Spectra[J].Journal of Geophysical Research Earth Surface,2010,115:F04031.doi:10.1029/2009JF001645.
[2] Claudio C,Iorio E D,Liu Q,et al.Iron Oxide Nanoparticles in Soils:Environmental and Agronomic Importance[J].Journal of Nanoscience & Nanotechnology,2018,17(7):4449-4460.
[3] Zeng Zhaohua.Relationship between Agricultural Ecology and Element Fe in Soil[J].The Administration and Technique of Environmental Monitoring,2001,13(4):18-20.[曾昭华.农业生态与土壤环境中铁元素的关系[J] 环境监测管理与技术,2001,13(4):18-20.]
[4] Yu Cunzu,Peng Lin,Peng Xianglin,et al.Soil Iron Content and Its Effectiveness in Loess Area[J].Shaanxi Journal of Agricultural Sciences,1982(6):26-28.[余存祖,彭琳,彭祥林,等.黄土区土壤铁的含量及其有效性[J].陕西农业科学,1982(6):26-28.]
[5] Sun W,Xia Z.Estimating Soil Zinc Concentrations Using Reflectance Spectroscopy[J].International Journal of Applied Earth Observation & Geoinformation,2017,58:126-133.
[6] Ren H Y,Zhuang D F,Singh A N,et al.Estimation of as and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study[J].Pedosphere,2009,19(6):719-726.
[7] Stoner E R,Baumgardner M F,Biehl L L,et al.Atlas of Soil Reflectance Properties[R].Purdue University,Indiana,1980.
[8] Zhang Guanghui,Zhou Shenglu,Wu Shaohua.Prediction of as in Soil with Reflectance Spectroscocy[J].Spectroscopy and Spectral Analysis,2011,31(1):173-176.[郑光辉,周生路,吴绍华.土壤砷含量高光谱估算模型研究[J].光谱学与光谱分析,2011,31(1):173-176.]
[9] Wu Mingzhu,Li Xiaomei,Sha Jinming.Spectral Inversion Models for Prediction of Total Chromium Content in Subtropical Soil[J].Spectroscopy and Spectral Analysis,2014,34(6):1660-1666.[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,2014,34(6):1660-1666.]
[10] Wu Y,Chen J,Ji J,et al.A Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils[J].Soil Science Society of America Journal,2007,71(3):918-926.
[11] Wang Lu,Lin Qizhong,Jia Dong,et al.Study on the Prediction of Soil Heavy Metal Elements Content based on Reflectance Spectra[J].Journal of Remote Sensing,2007,11(6):906-913.[王璐,蔺启忠,贾东,等.基于反射光谱预测土壤重金属元素含量的研究[J].遥感学报,2007,11(6):906-913.]
[12] Yuan Zhongqiang,Cao Chunxiang,Bao Daming,et al.Inversion on Contents of Heavy Metals in Soils of Wetlands in Zoigê Plateau based on Remote Sensing Data[J].Wetland Science,2016,14(1):113-116.[袁中强,曹春香,鲍达明,等.若尔盖湿地土壤重金属元素含量的遥感反演[J].湿地科学,2016,14(1):113-116.]
[13] Xu Y,Smith S E,Grunwald S,et al.Estimating Soil Total Nitrogen in Smallholder Farm Settings Using Remote Sensing Spectral Indices and Regression Kriging[J].Catena,2018,163:111-122.
[14] Lu Wanzhen.Modern Near Infrared Spectroscopy Analy Technology[M].Beijing:China Petrochemical Press,2007.[陆婉珍.现代近红外光谱分析技术[M].北京:中国石化出版社,2000.]
[15] Xu Hanqiu.A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index(MNDWI)[J].Journal of Remote Sensing,2005,9(5):589-595.[徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.]
[16] Yan Yanlu.The Basis and Application of Near Infrared Spectroscopy[M].Beijing:China Light Industry Press,2005.[严衍禄.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005.]
[17] He T,Wang J,Lin Z,et al.Spectral Features of Soil Organic Matter[J].Geo-spatial Information Science,2009,12(1):33-40.
[18] Saeys W,Mouazen A M,Ramon H.Potential for Onsite and Online Analysis of Pig Manure Using Visible and Near Infrared Reflectance Spectroscopy[J].Biosystems Engineering,2005,91(4):393-402.
[2] Claudio C,Iorio E D,Liu Q,et al.Iron Oxide Nanoparticles in Soils:Environmental and Agronomic Importance[J].Journal of Nanoscience & Nanotechnology,2018,17(7):4449-4460.
[3] Zeng Zhaohua.Relationship between Agricultural Ecology and Element Fe in Soil[J].The Administration and Technique of Environmental Monitoring,2001,13(4):18-20.[曾昭华.农业生态与土壤环境中铁元素的关系[J] 环境监测管理与技术,2001,13(4):18-20.]
[4] Yu Cunzu,Peng Lin,Peng Xianglin,et al.Soil Iron Content and Its Effectiveness in Loess Area[J].Shaanxi Journal of Agricultural Sciences,1982(6):26-28.[余存祖,彭琳,彭祥林,等.黄土区土壤铁的含量及其有效性[J].陕西农业科学,1982(6):26-28.]
[5] Sun W,Xia Z.Estimating Soil Zinc Concentrations Using Reflectance Spectroscopy[J].International Journal of Applied Earth Observation & Geoinformation,2017,58:126-133.
[6] Ren H Y,Zhuang D F,Singh A N,et al.Estimation of as and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study[J].Pedosphere,2009,19(6):719-726.
[7] Stoner E R,Baumgardner M F,Biehl L L,et al.Atlas of Soil Reflectance Properties[R].Purdue University,Indiana,1980.
[8] Zhang Guanghui,Zhou Shenglu,Wu Shaohua.Prediction of as in Soil with Reflectance Spectroscocy[J].Spectroscopy and Spectral Analysis,2011,31(1):173-176.[郑光辉,周生路,吴绍华.土壤砷含量高光谱估算模型研究[J].光谱学与光谱分析,2011,31(1):173-176.]
[9] Wu Mingzhu,Li Xiaomei,Sha Jinming.Spectral Inversion Models for Prediction of Total Chromium Content in Subtropical Soil[J].Spectroscopy and Spectral Analysis,2014,34(6):1660-1666.[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,2014,34(6):1660-1666.]
[10] Wu Y,Chen J,Ji J,et al.A Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils[J].Soil Science Society of America Journal,2007,71(3):918-926.
[11] Wang Lu,Lin Qizhong,Jia Dong,et al.Study on the Prediction of Soil Heavy Metal Elements Content based on Reflectance Spectra[J].Journal of Remote Sensing,2007,11(6):906-913.[王璐,蔺启忠,贾东,等.基于反射光谱预测土壤重金属元素含量的研究[J].遥感学报,2007,11(6):906-913.]
[12] Yuan Zhongqiang,Cao Chunxiang,Bao Daming,et al.Inversion on Contents of Heavy Metals in Soils of Wetlands in Zoigê Plateau based on Remote Sensing Data[J].Wetland Science,2016,14(1):113-116.[袁中强,曹春香,鲍达明,等.若尔盖湿地土壤重金属元素含量的遥感反演[J].湿地科学,2016,14(1):113-116.]
[13] Xu Y,Smith S E,Grunwald S,et al.Estimating Soil Total Nitrogen in Smallholder Farm Settings Using Remote Sensing Spectral Indices and Regression Kriging[J].Catena,2018,163:111-122.
[14] Lu Wanzhen.Modern Near Infrared Spectroscopy Analy Technology[M].Beijing:China Petrochemical Press,2007.[陆婉珍.现代近红外光谱分析技术[M].北京:中国石化出版社,2000.]
[15] Xu Hanqiu.A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index(MNDWI)[J].Journal of Remote Sensing,2005,9(5):589-595.[徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.]
[16] Yan Yanlu.The Basis and Application of Near Infrared Spectroscopy[M].Beijing:China Light Industry Press,2005.[严衍禄.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005.]
[17] He T,Wang J,Lin Z,et al.Spectral Features of Soil Organic Matter[J].Geo-spatial Information Science,2009,12(1):33-40.
[18] Saeys W,Mouazen A M,Ramon H.Potential for Onsite and Online Analysis of Pig Manure Using Visible and Near Infrared Reflectance Spectroscopy[J].Biosystems Engineering,2005,91(4):393-402.