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    Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library

    Access Status
    Fulltext not available
    Authors
    Shi, Z.
    Ji, W.
    Viscarra Rossel, Raphael
    Chen, S.
    Zhou, Y.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Shi, Z. and Ji, W. and Viscarra Rossel, R. and Chen, S. and Zhou, Y. 2015. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library. European Journal of Soil Science. 66 (4): pp. 679-687.
    Source Title
    European Journal of Soil Science
    DOI
    10.1111/ejss.12272
    ISSN
    1351-0754
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/73883
    Collection
    • Curtin Research Publications
    Abstract

    We need to determine the best use of soil vis-NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis-NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub-setting with a local partial least squares regression (local-PLSR) that uses a limited number of similar vis-NIR spectra (k-nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil-forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local-PLSR method (R<sup>2</sup>=0.74, RPIQ=2.6) was better than that from local-PLSR (R<sup>2</sup>=0.69, RPIQ=2.3) and PLSR alone (R<sup>2</sup>=0.50, RPIQ=1.5). The coupling of a local-PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis-NIR sensors for laboratory analysis or field estimation.

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