Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance
Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predict...
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doaj-5ba605b2e2824eb49f36b4aec5be9bc02020-11-25T00:35:41ZengMDPI AGSustainability2071-10502020-02-01124147610.3390/su12041476su12041476Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral ReflectanceLei Han0Rui Chen1Huili Zhu2Yonghua Zhao3Zhao Liu4Hong Huo5School of Land Engineering, Chang’an University, Xi’an 710054, ChinaShaanxi Key Laboratory of Land consolidation, Xi’an 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Land Engineering, Chang’an University, Xi’an 710054, ChinaSchool of Land Engineering, Chang’an University, Xi’an 710054, ChinaShaanxi Key Laboratory of Land consolidation, Xi’an 710054, ChinaSoil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>R</mi> <mi>v</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring.https://www.mdpi.com/2071-1050/12/4/1476estimation mechanismsoil as contentshyperspectralback propagation neutral network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Han Rui Chen Huili Zhu Yonghua Zhao Zhao Liu Hong Huo |
spellingShingle |
Lei Han Rui Chen Huili Zhu Yonghua Zhao Zhao Liu Hong Huo Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance Sustainability estimation mechanism soil as contents hyperspectral back propagation neutral network |
author_facet |
Lei Han Rui Chen Huili Zhu Yonghua Zhao Zhao Liu Hong Huo |
author_sort |
Lei Han |
title |
Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance |
title_short |
Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance |
title_full |
Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance |
title_fullStr |
Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance |
title_full_unstemmed |
Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance |
title_sort |
estimating soil arsenic content with visible and near-infrared hyperspectral reflectance |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2020-02-01 |
description |
Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>R</mi> <mi>v</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring. |
topic |
estimation mechanism soil as contents hyperspectral back propagation neutral network |
url |
https://www.mdpi.com/2071-1050/12/4/1476 |
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