Spatial heterogeneity of heavy metals in contaminated soil using hyperspectral inversion models a case study of Dongting lake region south-central China
点击次数：
影响因子：
3.8
所属单位：
中南大学地球科学与信息物理学院
教研室：
GIS
发表刊物：
Scientific Reports
刊物所在地：
国外
关键字：
OHS, Soil heavy metals, Remote sensing, Inversion, Deep learning
摘要：
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 R2 of BPNN and CNN reaches 0.80. BPNN and CNN achieves the optimal inversion
of As, Cd and Pb. MLR and PLSR has the best accuracy in Cr and Cu, Hg, Ni and Zn. In addition,
the distribution trends of 8 heavy metals retrieved from a small number of samples were basically
consistent with the interpolation maps of 1589 samples, indicating that it is completely feasible to
use a small number of samples to retrieve the distribution trends of heavy metals in large areas. This
study provides important technical support for regional soil pollution prevention and control, and has
significant application value and promotion potential.
第一作者：
Gong Cheng
论文类型：
期刊论文
通讯作者：
Meiqing Ding, Xingwang Zhou
论文编号：
35256
学科门类：
工学
文献类型：
J
卷号：
15
是否译文：
否
收录刊物：
SCI
附件：
学位：博士学位
学科：地质资源与地质工程. 地质学. 测绘科学与技术
所在单位：地球科学与信息物理学院