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    Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths

    Access Status
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    Authors
    Vasques, G.
    Demattê, J.
    Viscarra Rossel, Raphael
    Ramírez-López, L.
    Terra, F.
    Date
    2014
    Type
    Journal Article
    
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    Citation
    Vasques, G. and Demattê, J. and Viscarra Rossel, R. and Ramírez-López, L. and Terra, F. 2014. Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths. Geoderma. 223-225 (1): pp. 73-78.
    Source Title
    Geoderma
    DOI
    10.1016/j.geoderma.2014.01.019
    ISSN
    0016-7061
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/74513
    Collection
    • Curtin Research Publications
    Abstract

    Visible/near-infrared diffuse reflectance spectroscopy (VNIRDRS) offers an alternative to conventional analytical methods to estimate various soil attributes. However, the use of VNIRDRS in soil survey and taxonomic classification is still underexplored. We investigated the potential use of VNIRDRS to classify soils in a region with variable soils, geology, and topography in southeastern Brazil. Soils were classified in the field according to the Brazilian Soil Classification System, and visible/near-infrared (400-2500. nm) spectra were collected from three depth intervals (0-20, 40-60 and 80-100. cm) and combined in sequence to compose a pseudo multi-depth spectral curve, which was used to derive the classification models. Principal component (PC) analysis and multinomial logistic regression were used to classify 291 soils (202 in calibration and 89 in validation mode) at the levels of order (highest), suborder (second highest) and suborder plus textural classification (STC). Based on the validation results, best classification was obtained at the order level (67% agreement rate), followed by suborder (48% agreement) and STC (24% agreement). The inherent complexity and variability within soil taxonomic groups and in contrast the strong similarity among different groups in terms of soil spectra and other attributes cause confusion in the classification model. This novel approach combining spectral data from different depths in multivariate classification can improve soil classification and survey in a cost-efficient manner, supporting sustainable use and management of tropical soils.

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