高标准基本农田建设区域土壤重金属含量的高光谱反演
为快速高效的获取高标准基本农田建设区域土壤重金属信息,以新郑市高标准基本农田建设区域为研究对象,共采集154个土壤样品,在室内利用ASD Field Spec3型地物光谱仪获得土壤高光谱数据,对土壤样品在400-2 400 nm的光谱反射率进行多元散射校正（multiplicative scatter correction,MSC）和Savitzky-Golay（SG）平滑后,进行一阶微分（first order differential reflectance,FDR）和二阶微分（second order differential reflectance,SDR）变换,并与Cr、Cd、Zn、...
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| Published in | 农业工程学报 Vol. 33; no. 12; pp. 230 - 239 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
河南理工大学测绘与国土信息工程学院,焦作,454000%河南理工大学测绘与国土信息工程学院,焦作 454000
2017
华北水利水电大学资源与环境学院,郑州 450046 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2017.12.030 |
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| Abstract | 为快速高效的获取高标准基本农田建设区域土壤重金属信息,以新郑市高标准基本农田建设区域为研究对象,共采集154个土壤样品,在室内利用ASD Field Spec3型地物光谱仪获得土壤高光谱数据,对土壤样品在400-2 400 nm的光谱反射率进行多元散射校正（multiplicative scatter correction,MSC）和Savitzky-Golay（SG）平滑后,进行一阶微分（first order differential reflectance,FDR）和二阶微分（second order differential reflectance,SDR）变换,并与Cr、Cd、Zn、Cu、Pb 5种重金属含量进行相关性分析,遴选出通过P=0.01显著性检验的高光谱特征波段作为反演模型的自变量,采用116个建模集样本构建偏最小二乘模型（partial least square regress,PLSR）,通过精度检验筛选每个土壤重金属的最佳反演模型,并采用最佳地统计插值方法对高标准基本农田建设区域土壤重金属进行空间插值。结果表明：Cr的SDR-PLSR模型为最佳反演模型（R2=0.88,RPD=1.68）,Cd的R-PLSR模型为最佳反演模型（R2=0.70,RPD=1.50）,Zn的R-PLSR模型为最佳反演模型（R2=0.88,RPD=2.05）,Cu的R-PLSR模型为最佳反演模型（R2=0.99,RPD=3.36）,Pb的SDR-PLSR模型为最佳反演模型（R2=0.93,RPD=3.16）;采用构建的土壤重金属的最佳模型,对土壤重金属含量进行空间插值,结合高标准农田建设标准可知Zn含量符合土壤环境质量Ⅱ类标准且均低于土壤背景值,Cr、Cd、Cu和Pb符合土壤环境质量Ⅱ类标准,但是部分区域超过了土壤背景值。该研究为高光谱反演模型用于高标准基本农田建设区域土壤基础信息的实时监测提供了参考。 |
|---|---|
| AbstractList | 为快速高效的获取高标准基本农田建设区域土壤重金属信息,以新郑市高标准基本农田建设区域为研究对象,共采集154个土壤样品,在室内利用ASD Field Spec3型地物光谱仪获得土壤高光谱数据,对土壤样品在400-2 400 nm的光谱反射率进行多元散射校正（multiplicative scatter correction,MSC）和Savitzky-Golay（SG）平滑后,进行一阶微分（first order differential reflectance,FDR）和二阶微分（second order differential reflectance,SDR）变换,并与Cr、Cd、Zn、Cu、Pb 5种重金属含量进行相关性分析,遴选出通过P=0.01显著性检验的高光谱特征波段作为反演模型的自变量,采用116个建模集样本构建偏最小二乘模型（partial least square regress,PLSR）,通过精度检验筛选每个土壤重金属的最佳反演模型,并采用最佳地统计插值方法对高标准基本农田建设区域土壤重金属进行空间插值。结果表明：Cr的SDR-PLSR模型为最佳反演模型（R2=0.88,RPD=1.68）,Cd的R-PLSR模型为最佳反演模型（R2=0.70,RPD=1.50）,Zn的R-PLSR模型为最佳反演模型（R2=0.88,RPD=2.05）,Cu的R-PLSR模型为最佳反演模型（R2=0.99,RPD=3.36）,Pb的SDR-PLSR模型为最佳反演模型（R2=0.93,RPD=3.16）;采用构建的土壤重金属的最佳模型,对土壤重金属含量进行空间插值,结合高标准农田建设标准可知Zn含量符合土壤环境质量Ⅱ类标准且均低于土壤背景值,Cr、Cd、Cu和Pb符合土壤环境质量Ⅱ类标准,但是部分区域超过了土壤背景值。该研究为高光谱反演模型用于高标准基本农田建设区域土壤基础信息的实时监测提供了参考。 S127; 为快速高效的获取高标准基本农田建设区域土壤重金属信息,以新郑市高标准基本农田建设区域为研究对象,共采集154个土壤样品,在室内利用ASD Field Spec3型地物光谱仪获得土壤高光谱数据,对土壤样品在400~2400 nm的光谱反射率进行多元散射校正(multiplicative scatter correction,MSC)和Savitzky-Golay(SG)平滑后,进行一阶微分(first order differential reflectance,FDR)和二阶微分(second order differential reflectance,SDR)变换,并与Cr、Cd、Zn、Cu、Pb 5种重金属含量进行相关性分析,遴选出通过P=0.01显著性检验的高光谱特征波段作为反演模型的自变量,采用116个建模集样本构建偏最小二乘模型(partial least square regress,PLSR),通过精度检验筛选每个土壤重金属的最佳反演模型,并采用最佳地统计插值方法对高标准基本农田建设区域土壤重金属进行空间插值.结果表明:Cr的SDR-PLSR模型为最佳反演模型(R 2=0.88,RPD=1.68),Cd的R-PLSR模型为最佳反演模型(R 2=0.70,RPD=1.50),Zn的R-PLSR模型为最佳反演模型(R 2=0.88,RPD=2.05),Cu的R-PLSR模型为最佳反演模型(R 2=0.99,RPD=3.36),Pb的SDR-PLSR模型为最佳反演模型(R 2=0.93,RPD=3.16);采用构建的土壤重金属的最佳模型,对土壤重金属含量进行空间插值,结合高标准农田建设标准可知Zn含量符合土壤环境质量Ⅱ类标准且均低于土壤背景值,Cr、Cd、Cu和Pb符合土壤环境质量Ⅱ类标准,但是部分区域超过了土壤背景值.该研究为高光谱反演模型用于高标准基本农田建设区域土壤基础信息的实时监测提供了参考. |
| Abstract_FL | Hyperspectral reflectance provides an alternative method to soil's physical and chemical analysis in laboratory for the estimation of soil properties in large range. In order to achieve rapid measurement of the soil heavy metal content in well-facilitied capital farmland construction areas, 154 soil samples at 0-30 cm depth were collected as research objects, which were from well-facilitied capital farmland construction areas in Xinzheng City, Henan Province. The raw hyperspectral reflectance of soil samples was measured by the standard procedure with a spectrometer of ASD Field Spec3 equipped with a high intensity contact probe under the laboratory conditions. Meanwhile, the contents of Cr, Cd, Zn, Cu, and Pb in these soil samples were analyzed. The 116 samples were used for building hyperspectral estimation models and the other 38 samples were used for model validation. In the next, the raw spectral reflectance of 400-2400 nm after multiplicative scatter correction and Savitzky-Golay was transformed to 2 spectral indices, i.e. first order differential reflectance(FDR) and second order differential reflectance(SDR). The correlation coefficient between the 3 kinds of spectral indices and Cr, Cd, Zn, Cu, Pb content was analyzed by Pearson correlation analysis. Then, the correlation coefficients (P<0.01) of the 3 spectral indices were got in significant test, which could be used to extract significant bands. At last, we used partial least squares regression (PLSR) method to build quantitative inversion models of soil heavy metal content based on significant bands for this study area, respectively. The prediction accuracies of these models were assessed by comparing determination coefficients (R2), root mean squared error (RMSE) and relative percent deviation (RPD) between the prediction and validation values. Based on these, the optimal models were selected. The spatial distribution map of Cr, Cd, Zn, Cu and Pb content was made by geographical interpolation. The results showed that, conducting the first order differential reflectance and second order differential reflectance transformation on raw soil spectral data, could highlight the hidden spectral reflectivity characteristics effectively. Among all of the 3 spectral indices based on PLSR model, the model of second order differential reflectance about Cr could obtain more robust prediction accuracies, its values ofR2 was 0.88,its values of RPD was 1.68; the model of the raw spectral reflectance (R) of 400-2400 nm after multiplicative scatter correction(MSC)and Savitzky-Golay(SG)about Cd、Zn and Cu could obtain more robust prediction accuracies, their values ofR2 were 0.70, 0.88 and 0.99, their values of RPD were 1.50, 2.05 and 3.36 respectively; Pb could obtain more robust prediction accuracies, their values ofR2was 0.93,its values of RPD was 3.16. The optimum model of soil heavy metal was used to interpolate the soil heavy metal content; the content of Zn was in accordance with the standard of soil environmental quality, and the contents of Cr, Cd, Cu and Pb met the soil environmental quality standardⅡ, but the contents in some well-facilitied capital farmland construction areas were more than the soil background value. This study provides a reference for the real-time monitoring of soil basic information in well-facilitied capital farmland construction areas by hyperspectral inversion model. |
| Author | 张秋霞 张合兵 刘文锴 赵素霞 |
| AuthorAffiliation | 河南理工大学测绘与国土信息工程学院,焦作454000 华北水利水电大学资源与环境学院,郑州450046 |
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| Author_FL | Zhang Qiuxia Zhang Hebing Zhao Suxia Liu Wenkai |
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| Author_xml | – sequence: 1 fullname: 张秋霞 张合兵 刘文锴 赵素霞 |
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| Keywords | heavy metals partial least square regression (PLSR) 高标准基本农田 hyperspectral 反演 土壤 重金属 高光谱 spectrum analysis inversion well-facilitied capital farmland 光谱分析 soils 偏最小二乘回归 |
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| Notes | soils; spectrum analysis; heavy metals; hyperspectral; inversion; partial least square regression （PLSR）; well-facilitied capital farmland 11-2047/S Hyperspectral reflectance provides an alternative method to soil＇s physical and chemical analysis in laboratory for the estimation of soil properties in large range. In order to achieve rapid measurement of the soil heavy metal content in well-facilitied capital farmland construction areas, 154 soil samples at 0-30 cm depth were collected as research objects, which were from well-facilitied capital farmland construction areas in Xinzheng City, Henan Province. The raw hyperspectral reflectance of soil samples was measured by the standard procedure with a spectrometer of ASD Field Spec3 equipped with a high intensity contact probe under the laboratory conditions. Meanwhile, the contents of Cr, Cd, Zn, Cu, and Pb in these soil samples were analyzed. The 116 samples were used for building hyperspectral estimation models and the other 38 samples were used for model vali |
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| Publisher | 河南理工大学测绘与国土信息工程学院,焦作,454000%河南理工大学测绘与国土信息工程学院,焦作 454000 华北水利水电大学资源与环境学院,郑州 450046 |
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| Snippet | 为快速高效的获取高标准基本农田建设区域土壤重金属信息,以新郑市高标准基本农田建设区域为研究对象,共采集154个土壤样品,在室内利用ASD Field Spec3型地物光谱仪获得土壤... S127; 为快速高效的获取高标准基本农田建设区域土壤重金属信息,以新郑市高标准基本农田建设区域为研究对象,共采集154个土壤样品,在室内利用ASD Field Spec3型地物光谱仪获... |
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| SubjectTerms | 偏最小二乘回归 光谱分析 反演 土壤 重金属 高光谱 高标准基本农田 |
| Title | 高标准基本农田建设区域土壤重金属含量的高光谱反演 |
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