Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties
By A. Pirie, Balwant Singh, K. Islam. 2005
141 citations recorded by the source corpus.
Reflectance spectroscopy techniques in the ultraviolet, visible, near-infrared and mid-infrared regions are alternatives for many traditional laboratory methods for measuring soil properties. However, debate exists over whether the near-infrared (700–2500 nm) or the mid-infrared (MIR, 2500–25000 nm) region of the electromagnetic spectrum is more useful for predicting soil properties. Therefore, the aim of this study was to compare UV-VIS-NIR and MIR spectroscopic techniques to predict several soil properties. A total of 415 surface and subsurface soil samples were collected from widely spread locations within New South Wales and south-eastern Queensland of Australia to model the proposed hypothesis. Principal component regression analysis (PCR) was used to develop calibration and validation models from soil spectra and reference laboratory values. The models developed using MIR spectra achieved higher prediction accuracy (regression coefficient, r2 = 0.62–0.85) for pH, organic carbon, clay, sand, CEC, and exchangeable Ca and Mg than that obtained by UV-VIS-NIR spectra (r2 = 0.28–0.76). PCR models were also developed for the combined spectral regions (UV-VIS-NIR+MIR). The models developed using combined spectra were also found to predict pH, organic carbon, clay, sand, CEC, and exchangeable Ca and Mg with acceptable accuracy (r2 = 0.59–0.79). The results of this study indicate that MIR spectra are better than UV-VIS-NIR spectra for estimation of common soil properties.
Subfields: Analytical Chemistry, Artificial Intelligence, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/64afd1f49c2cef534abc309c1742f2b903e78866
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In Artificial Intelligence
Citations in the corpus
- Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach (2011, methodology): “Quality of the model was interpreted according to the three classes of RPD proposed by most authors (Chang et al., 2001; Pirie et al., 2005; Du et al., 2009; Du, and Zhou, 2009): RPDN2 corresponded to models that can accurately predict the tested property, RPD between 1.4 and 2 gathered models with…”
- Biological indicators of soil quality and soil organic matter characteristics in an agricultural management continuum (2013, background): “In this study, an RPD less than 1.6 was considered poor, 1.6–2.0 was acceptable, and greater than 2.0 was excellent (Dunn et al. 2002; Pirie et al. 2005).”