Spectral features of Fe and organic carbon in estimating low and moderate concentration of heavy metals in mangrove sediments across different regions and habitat types-Reference-Cited by-同舟云学术

Spectral features of Fe and organic carbon in estimating low and moderate concentration of heavy metals in mangrove sediments across different regions and habitat types

Published:2022-11 Issue: Volume:426 Page:116093
ISSN:0016-7061
Container-title:Geoderma
language:en
Short-container-title:Geoderma

Author:

Zhao Demei,Wang Jingzhe,Miao Jing,Zhen Jianing,Wang Junjie,Gao Changjun,Jiang Jincheng,Wu Guofeng

Publisher

Elsevier BV

Subject

Soil Science

Reference114 articles.

1. Lithologic characterization of a reservoir using continuous-wavelet transforms;Alvarez;IEEE. T. Geosci. Remote.,2003

2. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis;Araujo;Chemometr. Intell. Lab.,2001

3. A review on spectral processing methods for geological remote sensing;Asadzadeh;Int. J. Appl. Earth Obs.,2016

4. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy;Bellon-Maurel;Trac-trend. Anal. Chem.,2010

5. Bird diversity and dissimilarity show contrasting patterns along heavy metal pollution gradients in the Urals, Russia;Belskii;Environ. Sci. Pollut. R.,2018

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hierarchical residual correction-based hyperspectral inversion method for soil heavy metals considering spatial heterogeneity;Journal of Hazardous Materials;2024-11

2. Improving Soil Quality Index Prediction by Fusion of Vis-NIR and pXRF spectral data;Geoderma;2024-07

3. Monitoring of soil heavy metals based on hyperspectral remote sensing: A review;Earth-Science Reviews;2024-07

4. Quantitative prediction of soil chromium content using laboratory-based visible and near-infrared spectroscopy with different ensemble learning models;Advances in Space Research;2024-07

5. Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints;Science of The Total Environment;2024-05