| 基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析 |
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| 引用本文： | 刘亚秋,陈红艳,王瑞燕,常春艳,陈哲.基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析[J].中国农业科学,2016,49(10):1925-1935. |
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| 作者姓名： | 刘亚秋 陈红艳 王瑞燕 常春艳 陈哲 |
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| 基金项目： | 国家自然科学基金（41401239）、中国博士后科学基金（2014M561953） |
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| 摘 要： | 【目的】定量、准确地监测盐渍土,对其防治和农业可持续发展至关重要,论文旨在明确黄河口区土壤盐分及其主要离子的特征光谱,建立适用于该区域的土壤盐渍化定量分析模型,提高其定量分析的精度和稳定性。【方法】首先以山东省垦利县为研究区,于2014年10月5—9日野外采集代表性土样96个,对土样风干后,采用土壤化学分析方法室内分析盐分及其主要离子(Cl~-、Na~+、Ca~(2+))含量,并采用美国ASD Fieldspec 3光谱仪测定土样可见/近红外高光谱数据,对光谱反射率进行去噪、一阶导数变换等预处理;然后基于盐分及其主要离子不同含量的样本光谱分析盐分及其主要离子的光谱响应,在此基础上,对样本的土壤盐分及其主要离子含量与反射率的一阶导数光谱进行逐波段的相关分析,按照相关系数高且显著的原则,选取各自的敏感波段,再根据敏感波段的交叉情况选取集中波段为特征波段,进而选取特征波段中具有极值相关系数的波段作为显著特征波段,综合确定表征土壤盐分及其主要离子(Cl~-、Na~+、Ca~(2+))的特征光谱;最后分别采用多元线性回归(multiple linear regression,MLR)、支持向量机(support vector machine,SVM)和随机森林(random forest,RF)方法构建土壤盐分及其主要离子的定量高光谱分析模型。【结果】研究区土壤盐分及其主要离子(Cl~-、Na~+、Ca~(2+))含量的光谱曲线形状和走势整体一致;土壤盐分及其主要离子(Cl~-、Na~+、Ca~(2+))的光谱响应谱区为1 320—1 495、1 790—1 920、2 120—2 290 nm;基于相关分析的土壤盐分及其主要离子的敏感谱区为1 490—1 520、1 890—1 930nm;最后综合光谱响应及相关分析确定土壤盐分及其主要离子的特征波段为1 493、1 801、1 911和2 289 nm,显著特征波段为1 493和1 911 nm。模型结果显示基于2个显著特征波段反射率一阶导数的模型精度均与4个特征波段的模型精度相当,表明显著特征光谱作为盐分及其主要离子的特征光谱进行其定量分析的有效性。比较3种建模方法,RF模型的预测效果最好,SVM模型次之,而MLR模型精度最低;对于盐分、Cl~-和Na~+,3种方法构建的模型均可有效地用于其定量分析,精度较高且稳定,然而Ca~(2+)预测精度还有待提高。通过综合比较分析,基于显著特征波段(1 493和1 911 nm)反射率一阶导数构建的随机森林(RF)模型对盐分、Cl~-和Na~+均具有较好的估测精度和稳定性,也可用于Ca~(2+)的定量估测。【结论】基于光谱响应及相关分析综合确定盐分及其主要离子的显著特征光谱(1 493和1 911 nm反射率一阶导数),进而采用随机森林方法构建盐分及其主要离子的定量估测模型,适用于黄河口区土壤盐渍化信息的有效提取。
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| 关 键 词： | 土壤盐渍化 可见/近红外光谱 黄河口区 随机森林 支持向量机 |
| 收稿时间： | 2015-09-30 |
Quantitative Analysis of Soil Salt and Its Main Ions Based on Visible/Near Infrared Spectroscopy in Estuary Area of Yellow River |
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| LIU Ya-qiu,CHEN Hong-yan,WANG Rui-yan,CHANG Chun-yan,CHEN Zhe.Quantitative Analysis of Soil Salt and Its Main Ions Based on Visible/Near Infrared Spectroscopy in Estuary Area of Yellow River[J].Scientia Agricultura Sinica,2016,49(10):1925-1935. |
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| Authors: | LIU Ya-qiu CHEN Hong-yan WANG Rui-yan CHANG Chun-yan CHEN Zhe |
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| Affiliation: | 1.College of Resources and Environment, Shandong Agricultural University/National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Taian 271018, Shandong;2.Yantai Land Assets Management Center, Yantai 264003, Shandong |
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| Abstract: | 【Objective】It is crucial to quantitatively and accurately detect saline soil for the prevention of soil salinization and agricultural sustainable development. The objective of this study is to find the characteristic spectra of soil salt and its main ions, and build a quantitative analysis model of soil salinization which is suitable for the estuary area of Yellow River to improve the accuracy and stability of the quantitative analysis.【Method】 Kenli County in Shandong Province was selected as the experimental area. Firstly, 96 representative soil samples were collected on October 5th-9th, 2014. After soil sample were dried, the contents of soil salt and ions were analyzed. Then the visible and near infrared reflectance hyperspectra of the soil samples were measured in the laboratory by ASD Fieldspec 3 spectrometer, smoothed and transformed to the first deviation. Secondly, the response spectra of salt and its main ions (Cl-, Na+, Ca2+）were analyzed, first soil salinity and main ions content and the first derivative spectra of reflectance by band correlation analysis, according to the principle of correlation coefficient and significant, their sensitive bands were selected as the characteristic bands, feature bands with maximum correlation coefficient were chosen as the significant feature bands. Then the characteristicspectra which can represent soil salt and mainions (Cl-, Na+, Ca2+) were analyzed synthetically using correlation analysis and identified. Finally, the methods of multiple linear regression (MLR), support vector machine (SVM) and random forest (RF) were used respectively to build quantitative analysismodels ofsoil salinityandions contents. 【Result】 The overall shape and trend of the spectra curves of soil salinity and major ions (Cl-, Na+, Ca2+) content in study area were very similar. Soil salinity and major ions (Cl-, Na+, Ca2+) spectra response regions were determined to be 1 320-1 495, 1 790-1 920, 2 120-2 290 nm. On account of the correlation between the first derivative of the reflectance and the soil salinity and its main ions content, the sensitive spectral regions were 1 490-1 520, 1 890-1 930 nm, final integrated spectra analysis and correlation analysis the characteristic bands were1 493,1 801,1 911 and 2 289 nm and the significantcharacteristic bands were 1 493 and 1 911 nm. The models’ accuracy based on the first deviation of reflectance on thesignificantcharacteristic bands matched the models’ accuracy based on the four characteristic bands, which indicates that the significant characteristic spectra were effective for quantitative analysis of soil salt and its main ions. Compared the three modeling methods, the prediction ability of the RF was thebest, followed by the SVM, the MLRmodels’ precision was the lowest. The models using the above-mentioned three methods could be used for quantitative analysis of salt, Cl- and Na+, and had good stability and high precision, however the prediction accuracy of Ca2+ contents was still to be improved. In comprehensive comparison and analysis, among the built models, the RF models based on the first deviation of reflectance on thesignificantcharacteristic bands (1 493 and 1 911 nm) had higher accuracy and stability for the quantitative analysis of soil salt, Cl- and Na+, and could be applied to the quantitative estimation of Ca2+.【Conclusion】Thesignificant characteristicspectra(the first deviation of reflectance on 1 493 and 1 911 nm) of soil salt and its main ions were selected synthetically using correlation analysis based on the spectral response, then the quantitative estimation models of salt and its main ions were built using the RF regression method, which is suitable for the effective extraction of soil salinization information in the estuary area of Yellow River. |
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| Keywords: | soil salinization visible/near infrared spectroscopy estuary area of Yellow River random forest support vector machine |
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