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Soil, land care and environmental research

Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy

Kamrunnahar Islam, Balwant Singh and Alex McBratney

Australian Journal of Soil Research 41(6) 1101 - 1114
Published: 17 October 2003


Fast and convenient soil analytical techniques are needed for soil quality assessment and precision soil management. Spectroscopy in the ultraviolet (UV, 250–400 nm), visible (VIS, 400–700 nm), and near-infrared (NIR, 700–2500 nm) ranges allows rapid acquisition of soil information at quantitative, and qualitative or indicator, levels for use in agriculture and environmental monitoring. The main objective of this study was to evaluate the ability of reflectance spectroscopy in the UV, VIS, and NIR ranges to predict several soil properties simultaneously. Soil samples (161 surface and subsurface) were used for simultaneous estimation of pH, electrical conductivity (EC), air-dry gravimetric water content, organic carbon (OC), free iron, clay, sand, and silt contents, cation exchange capacity (CEC), and exchangeable calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na). Principal component regression analyses (PCA) were used to develop calibration equations between the reflectance spectral data and measured values for the above soil properties obtained by traditional laboratory methods. By using randomly selected calibration and validation sets of samples, PCA models were able to successfully predict pH, OC, air-dry gravimetric water content, clay, CEC, exchangeable Ca, and exchangeable Mg of soil samples. The predictions, however, were poor for EC, free iron, sand, silt, exchangeable K, and exchangeable Na. The study shows that reflectance spectroscopy in the UV–VIS–NIR range has the potential for the rapid simultaneous prediction of several soil properties.

Keywords: organic carbon, cation exchnage capacity, free iron, exchangeable cations, air-dry gravimetric water content, particle-size analysis, principal component regression analysis.

© CSIRO 2003

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