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| Prediction Soil Heavy Metal Zinc Based on Spectral Reflectance in Karst Area |
| WANG Jin-feng1, 2, 5, WANG Shi-jie2, 3, BAI Xiao-yong2, 3*, LIU Fang1, LU Qian1, 2, TIAN Shi-qi2, 4, WANG Ming-ming2 |
1. College of Resource and Environment, Guizhou University, Guiyang 550001, China
2. State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550001, China
3. Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, China
4. School of Geographyical and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
5. School of Tourism and Histrical Culture, Liupanshui Normal University, Liupanshui 553004, China |
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Abstract In order to solve the problem of inefficiency in measuring heavy metal zinc contentand soil samples collection difficultly with traditional way in karst area, it is greatly essential to get zinc content in soil by effective measures. The institutional area is a typical Karst region, soil zinc content as well as reflectance spectral of soil data were collected by inductively coupled plasma mass and Spectrophotometer. The reflectance spectra of measurement were handed by these steps. Firstly, 7 kinds of mathematical transformations were used including continuum removed, first differential, second differential, reciprocal transformation, absorbance transformation, first differential of absorbance, and second differential of absorbance. Secondly, spectral characteristic variables were determined based on the characteristic absorption band of spectral absorption of heavy metals. And then, further spectral characteristic variables were selected by correlation analysis. Finally, stepwise regression was used to determine the effective modeling spectral bands. Mapping relationships between Spectral bands and heavy metal zinc content were revealed by linear and nonlinear estimation algorithm, and the results aim to measure the heavy metal zinc in soil. It shows that the characteristic bands of zinc are associated with iron oxide, organic matter and clay mineral absorption band. It’s focused on 580，810，1 410，1 910，2 160，2 260，2 270，2 350，2 430 nm, and these results reveal that the absorption characteristics of heavy metal zinc possible were captured in karst area. The models were funded by Random Forests, Support Vector Machines, Partial Least Squares Regression to precision evaluation by coefficient of determination and the root mean square error of prediction. The best estimation model was obtained from spectrum transformation and model performance. The algorithm of Random forests for second differential transformation has the highest accuracy and is chosen as the best model. The content of heavy metal zinc was estimated by spectral reflectance. It is a rapid, efficient method for indirect evaluation of zinc. It provides a technical support for the dynamic monitoring of heavy metal content in karst areas.
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Received: 2018-10-24
Accepted: 2019-02-22
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Corresponding Authors:
BAI Xiao-yong
E-mail: baixiaoyong@126.com
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