The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has show...

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Autores principales:	Huihui Zhao, Peijia Liu, Baojin Qiao, Kening Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	anthrosols BPNN multi-spectral PLSR soil heavy metal spatial distribution Agriculture S
Acceso en línea:	https://doaj.org/article/bfe0e59e68d148629b46b853cd73f1df
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spelling	oai:doaj.org-article:bfe0e59e68d148629b46b853cd73f1df2021-11-25T18:09:49ZThe Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China10.3390/land101112272073-445Xhttps://doaj.org/article/bfe0e59e68d148629b46b853cd73f1df2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1227https://doaj.org/toc/2073-445XSoil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (<i>p</i> < 0.01), and of Hg was 0.318 (<i>p</i> < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.Huihui ZhaoPeijia LiuBaojin QiaoKening WuMDPI AGarticleanthrosolsBPNNmulti-spectralPLSRsoil heavy metalspatial distributionAgricultureSENLand, Vol 10, Iss 1227, p 1227 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anthrosols BPNN multi-spectral PLSR soil heavy metal spatial distribution Agriculture S
spellingShingle	anthrosols BPNN multi-spectral PLSR soil heavy metal spatial distribution Agriculture S Huihui Zhao Peijia Liu Baojin Qiao Kening Wu The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
description	Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (<i>p</i> < 0.01), and of Hg was 0.318 (<i>p</i> < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.
format	article
author	Huihui Zhao Peijia Liu Baojin Qiao Kening Wu
author_facet	Huihui Zhao Peijia Liu Baojin Qiao Kening Wu
author_sort	Huihui Zhao
title	The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
title_short	The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
title_full	The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
title_fullStr	The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
title_full_unstemmed	The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China
title_sort	spatial distribution and prediction of soil heavy metals based on measured samples and multi-spectral images in tai lake of china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bfe0e59e68d148629b46b853cd73f1df
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_version_	1718411582467211264

The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China

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