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    Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil

    Access Status
    Fulltext not available
    Authors
    Vasques, G.
    Demattê, J.
    Viscarra Rossel, Raphael
    Ramírez López, L.
    Terra, F.
    Rizzo, R.
    De Souza Filho, C.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Vasques, G. and Demattê, J. and Viscarra Rossel, R. and Ramírez López, L. and Terra, F. and Rizzo, R. and De Souza Filho, C. 2015. Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil. European Journal of Soil Science. 66 (4): pp. 767-779.
    Source Title
    European Journal of Soil Science
    DOI
    10.1111/ejss.12255
    ISSN
    1351-0754
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/73760
    Collection
    • Curtin Research Publications
    Abstract

    Soil mapping across large areas can be enhanced by integrating different methods and data sources. This study merges laboratory, field and remote sensing data to create digital maps of soil suborders based on the Brazilian Soil Classification System, with and without additional textural classification, in an area of 13000ha in the state of São Paulo, southeastern Brazil. Data from 289 visited soil profiles were used in multinomial logistic regression to predict soil suborders from geospatial data (geology, topography, emissivity and vegetation index) and visible-near infrared (400-2500nm) reflectance of soil samples collected at three depths (0-20, 40-60 and 80-100cm). The derived maps were validated with 47 external observations, and compared with two conventional soil maps at scales of 1:100000 and 1:20000. Soil suborders with and without textural classification were predicted correctly for 44 and 52% of the soil profiles, respectively. The derived suborder maps agreed with the 1:100000 and 1:20000 conventional maps in 20 and 23% (with textural classification) and 41 and 46% (without textural classification) of the area, respectively. Soils that were well defined along relief gradients (Latosols and Argisols) were predicted with up to 91% agreement, whereas soils in complex areas (Cambisols and Neosols) were poorly predicted. Adding textural classification to suborders considerably degraded classification accuracy; thus modelling at the suborder level alone is recommended. Stream density and laboratory soil reflectance improved all classification models, showing their potential to aid digital soil mapping in complex tropical environments.

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