Estimating chromium concentration in arable soil based on the optimal principal components by hyperspectral data

Estimating chromium concentration in arable soil based on the optimal principal components by hyperspectral data

The heavy metal pollution in arable soil poses a significant threat to human health. Thus, it is of great significance to investigate the contamination of heavy metal elements in the soil. As the soil polluted by heavy metal is sensitive to spectral reflectance, thus the hyperspectral remote sensing...

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Bibliographic Details
Main Authors: Guo, F. (Author), Li, K. (Author), Liu, F. (Author), Liu, X. (Author), Ma, H. (Author), Peng, M. (Author), Tang, S. (Author), Xu, Z. (Author), Yang, Z. (Author), Zhang, L. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
agricultural soil
Agriculture
arable land
Chromium
Chromium concentration
concentration (composition)
data set
Decision trees
estimation method
Gradient boosting
Gradient boosting decision tree
Gradient boosting decision tree (GBDT)
Health risks
heavy metal
Heavy metals
Hyperspectral models
Hyperspectral remote sensing technology
Least squares approximations
Linear regression
Nonlinear systems
Optimal principal component
Optimal principal components
Partial least square regression
Partial least squares regression (PLSR)
Principal component analysis
Principal Components
remote sensing
Remote sensing
Soil chromium
soil pollution
Soil pollution
Soils
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