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

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


  • 160054

    Forlagets udgivne version, 2,15 MB, PDF-dokument


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.
TidsskriftVadose Zone Journal
Sider (fra-til)1-13
Antal sider13
StatusUdgivet - 2018

Se relationer på Aarhus Universitet Citationsformater


Ingen data tilgængelig

ID: 103187163