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Remote Sens. 2017, 9(6), 632; doi:10.3390/rs9060632

Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements

1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China
2
University of Chinese Academy of Sciences, Yuquan Street, Shijingshan District, Beijing 100049, China;
3
School of Geoscience and Info-Physics, Central South University, Changsha, Hunan 410083, China
*
Author to whom correspondence should be addressed.
Received: 18 April 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 20 June 2017
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Abstract

Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil science, due to its rapidity and convenience. With different spectrally active soil characteristics, soil reflectance spectra exhibit distinctive curve forms, which may limit the application of VNIRS in estimating contaminant elements in soil. Consequently, spectral clustering was applied to explore the potential of classification in estimating soil contaminant elements. Spectral clustering based on different distance measure methods and elements with different contamination levels were exploited. In this study, soil samples were collected from Hunan Province, China and 74 reflectance spectra of air-dried soil samples over 350–2500 nm were used to predict nickel (Ni) and zinc (Zn) concentrations. Spectral clustering was achieved by K-means clustering based on squared Euclidean distance and Cosine of spectral angle, respectively. The prediction model was calibrated with the combination of Genetic algorithm and partial least squares regression (GA-PLSR). The prediction accuracy shows that the prediction of Ni and Zn concentrations in soil was improved to different extents by the two clustering methods and the clustering based on squared Euclidean distance had better performance over the clustering relied on Cosine of the spectral angle. The result reveals the potential of spectral classification in predicting soil Ni and Zn concentrations. A selected subset of the 74 soil spectra was used to further explore the potential of spectral classification in estimating Zn concentrations. The prediction was dramatically improved by clustering based on squared Euclidean distance. Additionally, analysis on distance measure methods indicates that Euclidean distance is more suitable to describe the difference between the collected soil reflectance spectra, which brought the better performance of the clustering based on squared Euclidean distance. View Full-Text
Keywords: soil contaminant elements; visible and near-infrared spectroscopy; spectral classification; genetic algorithm; partial least squares regression soil contaminant elements; visible and near-infrared spectroscopy; spectral classification; genetic algorithm; partial least squares regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sun, W.; Zhang, X.; Zou, B.; Wu, T. Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements. Remote Sens. 2017, 9, 632.

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