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Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon

Abstract : Field measurement using NIR spectroscopy is becoming a popular method to provide in situ, rapid, and inexpensive estimation of soil organic carbon (SOC) content. However NIR reflectance is quite sensitive to external environmental conditions, such as temperature and soil moisture. In the field, the soil moisture content can be highly variable. It is a challenge to find a chemometric method that allows for prediction of soil organic carbon from spectra obtained under field conditions that is insensitive to variable moisture content. This paper utilises an external parameter orthogonalisation (EPO) algorithm to remove the effect of soil moisture from NIR spectra for the calibration of SOC content. The algorithm projects all the soil spectra orthogonal to the space of unwanted variation, and thus the variations of soil moisture can be effectively removed. We designed a protocol with 3 independent datasets to be used for calibration of NIR spectra: (1) the calibration dataset, which contains soil samples with measured spectra and SOC content under standard (or laboratory) condition (air-dried), (2) the EPO development dataset contains spectra under laboratory condition (air-dried samples) and spectra collected under field conditions (varying soil moisture content), and (3) the validation dataset contains spectra collected under field condition and measured SOC content. We conducted experiments using soils at different moisture contents in laboratory conditions. Using the EPO algorithm, we are able to remove the effect of soil moisture from the spectra, which resulted in improved calibration and prediction of SOC content.
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Submitted on : Monday, December 5, 2011 - 2:13:27 PM
Last modification on : Thursday, March 10, 2022 - 5:02:07 PM
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B. Minasny, A.B. Mcbratney, Véronique Bellon Maurel, J.M. Roger, Alexia Gobrecht, et al.. Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma, Elsevier, 2011, 167 - 168, p. 118 - p. 124. ⟨10.1016/j.geoderma.2011.09.008⟩. ⟨hal-00648248⟩

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