Hyperspectral analysis of clay minerals

10 citations as recorded by crossref.

1. Mapping several soil types using hyperspectral datasets and advanced machine learning methods A. Vibhute & K. Kale https://doi.org/10.1016/j.rio.2023.100503
2. Synthesis of a copper(II) oxide–montmorillonite composite for lead removal D. Egirani et al. https://doi.org/10.1007/s12613-019-1788-7
3. Machine learning-enabled soil classification for precision agriculture: a study on spectral analysis and soil property determination A. Vibhute et al. https://doi.org/10.1007/s12518-023-00546-3
4. Synthesis and characterization of goethite coated with copper oxide and its effect on the removal of aqueous mercury(II) ions D. Egirani et al. https://doi.org/10.1080/16583655.2018.1493275
5. Synthesis of zinc oxide–montmorillonite composite and its effect on the removal of aqueous lead ions D. Egirani et al. https://doi.org/10.1007/s11631-018-0285-4
6. Unsupervised learning of satellite images enhances discovery of late Miocene fossil sites in the Urema Rift, Gorongosa, Mozambique J. d’Oliveira Coelho et al. https://doi.org/10.7717/peerj.11573
7. Early warning study on ecological risk of heavy metals in soil based on XGBoost-IER coupled model - A case study of black soil area in Jilin province J. Tan et al. https://doi.org/10.1016/j.ecolind.2025.114554
8. Synthesis and characterization of kaolinite coated with copper oxide and its effect on the removal of aqueous Lead(II) ions D. Egirani et al. https://doi.org/10.1007/s13201-019-0989-6
9. Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data B. Bhadra et al. https://doi.org/10.1080/01431161.2020.1783710
10. Application of near-infrared reflectance for quantitative assessment of soil properties E. Mohamed et al. https://doi.org/10.1016/j.ejrs.2017.02.001