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Mogens Humlekrog Greve

Combining X-ray computed tomography and visible near-infrared spectroscopy for prediction of soil structural properties

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Combining X-ray computed tomography and visible near-infrared spectroscopy for prediction of soil structural properties. / Katuwal, Sheela; Hermansen, Cecilie; Knadel, Maria; Møldrup, Per; Greve, Mogens Humlekrog; de Jonge, Lis Wollesen.

I: Vadose Zone Journal, Bind 17, Nr. 1, 2018, s. 1-13.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{2a6f2fea004045a7932a7203ad26fc59,
title = "Combining X-ray computed tomography and visible near-infrared spectroscopy for prediction of soil structural properties",
abstract = "Soil structure is a key soil property affecting a soil’s flow and transportbehavior. X-ray computed tomography (CT) is increasingly used to quantifysoil structure. However, the availability, cost, time, and skills required forprocessing are still limiting the number of soils studied. Visible near-infrared(vis-NIR) spectroscopy is a rapid analytical technique used successfully topredict various soil properties. In this study, the potential of using vis-NIRspectroscopy to predict X-ray CT derived soil structural properties wasinvestigated. In this study, 127 soil samples from six agricultural fields withinDenmark with a wide range of textural properties and organic C (OC) contentswere studied. Macroporosity (>1.2 mm in diameter) and CTmatrix (thedensity of the field-moist soil matrix devoid of large macropores and stones)were determined from X-ray CT scans of undisturbed soil cores (19 by 20 cm).Both macroporosity and CTmatix are soil structural properties that affect thedegree of preferential transport. Bulk soils from the 127 sampling locationswere scanned with a vis-NIR spectrometer (400–2500 nm). Macroporosityand CTmatrix were statistically predicted with partial least squares regression(PLSR) using the vis-NIR data (vis-NIR-PLSR) and multiple linear regression(MLR) based on soil texture and OC. The statistical prediction of macroporositywas poor, with both vis-NIR-PLSR and MLR (R2 < 0.45, ratio of performanceto deviation [RPD] < 1.4, and ratio of performance to interquartile distance[RPIQ] < 1.8). The CTmatrix was predicted better (R2 > 0.65, RPD > 1.5, and RPIQ> 2.0) combining the methods. The results illustrate the potential applicabilityof vis-NIR spectroscopy for rapid assessment/prediction of CTmatrix.",
author = "Sheela Katuwal and Cecilie Hermansen and Maria Knadel and Per M{\o}ldrup and Greve, {Mogens Humlekrog} and {de Jonge}, {Lis Wollesen}",
year = "2018",
doi = "10.2136/vzj2016.06.0054",
language = "English",
volume = "17",
pages = "1--13",
journal = "Vadose Zone Journal",
issn = "1539-1663",
publisher = "GeoScienceWorld",
number = "1",

}

RIS

TY - JOUR

T1 - Combining X-ray computed tomography and visible near-infrared spectroscopy for prediction of soil structural properties

AU - Katuwal, Sheela

AU - Hermansen, Cecilie

AU - Knadel, Maria

AU - Møldrup, Per

AU - Greve, Mogens Humlekrog

AU - de Jonge, Lis Wollesen

PY - 2018

Y1 - 2018

N2 - Soil structure is a key soil property affecting a soil’s flow and transportbehavior. X-ray computed tomography (CT) is increasingly used to quantifysoil structure. However, the availability, cost, time, and skills required forprocessing are still limiting the number of soils studied. Visible near-infrared(vis-NIR) spectroscopy is a rapid analytical technique used successfully topredict various soil properties. In this study, the potential of using vis-NIRspectroscopy to predict X-ray CT derived soil structural properties wasinvestigated. In this study, 127 soil samples from six agricultural fields withinDenmark with a wide range of textural properties and organic C (OC) contentswere studied. Macroporosity (>1.2 mm in diameter) and CTmatrix (thedensity of the field-moist soil matrix devoid of large macropores and stones)were determined from X-ray CT scans of undisturbed soil cores (19 by 20 cm).Both macroporosity and CTmatix are soil structural properties that affect thedegree of preferential transport. Bulk soils from the 127 sampling locationswere scanned with a vis-NIR spectrometer (400–2500 nm). Macroporosityand CTmatrix were statistically predicted with partial least squares regression(PLSR) using the vis-NIR data (vis-NIR-PLSR) and multiple linear regression(MLR) based on soil texture and OC. The statistical prediction of macroporositywas poor, with both vis-NIR-PLSR and MLR (R2 < 0.45, ratio of performanceto deviation [RPD] < 1.4, and ratio of performance to interquartile distance[RPIQ] < 1.8). The CTmatrix was predicted better (R2 > 0.65, RPD > 1.5, and RPIQ> 2.0) combining the methods. The results illustrate the potential applicabilityof vis-NIR spectroscopy for rapid assessment/prediction of CTmatrix.

AB - Soil structure is a key soil property affecting a soil’s flow and transportbehavior. X-ray computed tomography (CT) is increasingly used to quantifysoil structure. However, the availability, cost, time, and skills required forprocessing are still limiting the number of soils studied. Visible near-infrared(vis-NIR) spectroscopy is a rapid analytical technique used successfully topredict various soil properties. In this study, the potential of using vis-NIRspectroscopy to predict X-ray CT derived soil structural properties wasinvestigated. In this study, 127 soil samples from six agricultural fields withinDenmark with a wide range of textural properties and organic C (OC) contentswere studied. Macroporosity (>1.2 mm in diameter) and CTmatrix (thedensity of the field-moist soil matrix devoid of large macropores and stones)were determined from X-ray CT scans of undisturbed soil cores (19 by 20 cm).Both macroporosity and CTmatix are soil structural properties that affect thedegree of preferential transport. Bulk soils from the 127 sampling locationswere scanned with a vis-NIR spectrometer (400–2500 nm). Macroporosityand CTmatrix were statistically predicted with partial least squares regression(PLSR) using the vis-NIR data (vis-NIR-PLSR) and multiple linear regression(MLR) based on soil texture and OC. The statistical prediction of macroporositywas poor, with both vis-NIR-PLSR and MLR (R2 < 0.45, ratio of performanceto deviation [RPD] < 1.4, and ratio of performance to interquartile distance[RPIQ] < 1.8). The CTmatrix was predicted better (R2 > 0.65, RPD > 1.5, and RPIQ> 2.0) combining the methods. The results illustrate the potential applicabilityof vis-NIR spectroscopy for rapid assessment/prediction of CTmatrix.

U2 - 10.2136/vzj2016.06.0054

DO - 10.2136/vzj2016.06.0054

M3 - Journal article

VL - 17

SP - 1

EP - 13

JO - Vadose Zone Journal

JF - Vadose Zone Journal

SN - 1539-1663

IS - 1

ER -