Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices

For agricultural production and food safety, it is important to accurately and extensively estimate the heavy metal(loid) pollution contents in farmland soil. Remote sensing technology provides a feasible method for the rapid determination of heavy metal(loid) contents. In this study, the contents o...

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Autores principales:	Cheng Han, Jilong Lu, Shengbo Chen, Xitong Xu, Zibo Wang, Zheng Pei, Yu Zhang, Fengxuan Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optimal spectral indices spectral transformation partial least-squares regression (PLSR) multiple linear regression (MLR) random forest regression (RFR) adaptive neuro-fuzzy inference system (ANFIS) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
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spelling	oai:doaj.org-article:26de608dff3143bdbb33603770d595d62021-11-11T19:43:47ZEstimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices10.3390/su1321120882071-1050https://doaj.org/article/26de608dff3143bdbb33603770d595d62021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12088https://doaj.org/toc/2071-1050For agricultural production and food safety, it is important to accurately and extensively estimate the heavy metal(loid) pollution contents in farmland soil. Remote sensing technology provides a feasible method for the rapid determination of heavy metal(loid) contents. In this study, the contents of Ni, Hg, Cr, Cu, and As in the agricultural soil of the Suzi River Basin in Liaoning Province were taken as an example. The spectral data, with Savitzky–Golay smoothing, were taken as the original spectra (OR), and the spectral transformation was achieved by continuum removal (CR), reciprocal (1/R), root means square (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msqrt><mi mathvariant="normal">R</mi></msqrt></mrow></semantics></math></inline-formula>), first-order differential (FDR), and second-order differential (SDR) methods. Then the spectral indices were calculated by the optimal band combination algorithm. The correlation between Ni, Hg, Cr, Cu, and As contents and spectral indices was analyzed, and the optimal spectral indices were selected. Then, multiple linear regression (MLR), partial least squares regression (PLSR), random forest regression (RFR), and adaptive neuro-fuzzy reasoning system (ANFIS) were used to establish the estimation model based on the combined optimal spectral indices method. The results show that the combined optimal spectral indices method improves the correlation between spectra and heavy metal(loid), the MLR model produces the best estimation effect for Ni and Cu (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup><mo>=</mo><mn>0.713</mn><mo> </mo><mi>and</mi><mo> </mo><mn>0.855</mn></mrow></semantics></math></inline-formula>, RMSE = 5.053 and 8.113, RPD = 1.908 and 2.688, respectively), and the PLSR model produces the best effect for Hg, Cr, and As (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup><mo>=</mo></mrow></semantics></math></inline-formula> 0.653, 0.603, and 0.775, RMSE = 0.074, 23.777, and 1.923, RPD = 1.733, 1.621, and 2.154, respectively). Therefore, the combined optimal spectral indices method is feasible for heavy metal(loid) estimation in soils and could provide technical support for large-scale soil heavy metal(loid) content estimation and pollution assessment.Cheng HanJilong LuShengbo ChenXitong XuZibo WangZheng PeiYu ZhangFengxuan LiMDPI AGarticleoptimal spectral indicesspectral transformationpartial least-squares regression (PLSR)multiple linear regression (MLR)random forest regression (RFR)adaptive neuro-fuzzy inference system (ANFIS)Environmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12088, p 12088 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optimal spectral indices spectral transformation partial least-squares regression (PLSR) multiple linear regression (MLR) random forest regression (RFR) adaptive neuro-fuzzy inference system (ANFIS) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	optimal spectral indices spectral transformation partial least-squares regression (PLSR) multiple linear regression (MLR) random forest regression (RFR) adaptive neuro-fuzzy inference system (ANFIS) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Cheng Han Jilong Lu Shengbo Chen Xitong Xu Zibo Wang Zheng Pei Yu Zhang Fengxuan Li Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices
description	For agricultural production and food safety, it is important to accurately and extensively estimate the heavy metal(loid) pollution contents in farmland soil. Remote sensing technology provides a feasible method for the rapid determination of heavy metal(loid) contents. In this study, the contents of Ni, Hg, Cr, Cu, and As in the agricultural soil of the Suzi River Basin in Liaoning Province were taken as an example. The spectral data, with Savitzky–Golay smoothing, were taken as the original spectra (OR), and the spectral transformation was achieved by continuum removal (CR), reciprocal (1/R), root means square (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msqrt><mi mathvariant="normal">R</mi></msqrt></mrow></semantics></math></inline-formula>), first-order differential (FDR), and second-order differential (SDR) methods. Then the spectral indices were calculated by the optimal band combination algorithm. The correlation between Ni, Hg, Cr, Cu, and As contents and spectral indices was analyzed, and the optimal spectral indices were selected. Then, multiple linear regression (MLR), partial least squares regression (PLSR), random forest regression (RFR), and adaptive neuro-fuzzy reasoning system (ANFIS) were used to establish the estimation model based on the combined optimal spectral indices method. The results show that the combined optimal spectral indices method improves the correlation between spectra and heavy metal(loid), the MLR model produces the best estimation effect for Ni and Cu (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup><mo>=</mo><mn>0.713</mn><mo> </mo><mi>and</mi><mo> </mo><mn>0.855</mn></mrow></semantics></math></inline-formula>, RMSE = 5.053 and 8.113, RPD = 1.908 and 2.688, respectively), and the PLSR model produces the best effect for Hg, Cr, and As (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup><mo>=</mo></mrow></semantics></math></inline-formula> 0.653, 0.603, and 0.775, RMSE = 0.074, 23.777, and 1.923, RPD = 1.733, 1.621, and 2.154, respectively). Therefore, the combined optimal spectral indices method is feasible for heavy metal(loid) estimation in soils and could provide technical support for large-scale soil heavy metal(loid) content estimation and pollution assessment.
format	article
author	Cheng Han Jilong Lu Shengbo Chen Xitong Xu Zibo Wang Zheng Pei Yu Zhang Fengxuan Li
author_facet	Cheng Han Jilong Lu Shengbo Chen Xitong Xu Zibo Wang Zheng Pei Yu Zhang Fengxuan Li
author_sort	Cheng Han
title	Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices
title_short	Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices
title_full	Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices
title_fullStr	Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices
title_full_unstemmed	Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices
title_sort	estimation of heavy metal(loid) contents in agricultural soil of the suzi river basin using optimal spectral indices
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/26de608dff3143bdbb33603770d595d6
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Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices

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