Spatial heterogeneity of heavy metals in contaminated soil using hyperspectral inversion models: a case study of Dongting lake region, south-central China.

Gong Cheng, Xingwang Zhou, Meiqing Ding, Buqing Wang, Lingyi Liao

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Gong ChengKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China. 127294@csu.edu.cn.

Xingwang ZhouKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China.

Meiqing DingSchool of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha, 410004, China. dkiddy@csust.edu.cn.

Buqing WangChangsha Natural Resources Comprehensive Investigation Center, China Geological Survey, Changsha, 410625, China.

Lingyi LiaoKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China.

Funding

Hunan Natural Resources Science and Technology Plan Project 2020-04Open Project of Hunan Provincial Key Laboratory for remote sensing monitoring of ecological environment in Dongting Lake area DTH Key Lab. 2022-12

6 · The paper itself

Abstract

Heavy metal pollution in soil seriously threatens ecosystem and human health. However, traditional monitoring methods usually rely on intensive sampling, which is costly and difficult to be extended to large regional scales. Based on Orbita Hyperspectral Satellites (OHS) imagery and 175 sample sets out of 1589 samples, Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVM), Back Propagation Neural Network (BPNN), and Convolutional Neural Network (CNN) models were constructed to predict eight elements (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn). To explore the feasibility of using a small number of samples to invert the distribution trend of heavy metals in a large area. The results show among the above eight elements, the retrieval of Pb is the best, with the R

Indexed as

Deep learningInversionOHSRemote sensingSoil heavy metals

Identifiers

PMID41068136

DOI10.1038/s41598-025-10313-6

PMCPMC12511314

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.