摘要：Core Ideas Vis‐NIR can be used for estimation of soil physical and structural properties. Structural parameters are better predicted using vis‐NIR than pedotransfer functions. Vis‐NIR can be a fast and reliable method for predicting soils' transport behavior. Soil structure is a key soil property affecting a soil's flow and transport behavior. X‐ray computed tomography (CT) is increasingly used to quantify soil structure. However, the availability, cost, time, and skills required for processing are still limiting the number of soils studied. Visible near‐infrared (vis‐NIR) spectroscopy is a rapid analytical technique used successfully to predict various soil properties. In this study, the potential of using vis‐NIR spectroscopy to predict X‐ray CT derived soil structural properties was investigated. In this study, 127 soil samples from six agricultural fields within Denmark with a wide range of textural properties and organic C (OC) contents were studied. Macroporosity (>1.2 mm in diameter) and CT matrix (the density 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 CT matix are soil structural properties that affect the degree of preferential transport. Bulk soils from the 127 sampling locations were scanned with a vis‐NIR spectrometer (400–2500 nm). Macroporosity and CT matrix 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 macroporosity was poor, with both vis‐NIR‐PLSR and MLR ( R 2 0.65, RPD > 1.5, and RPIQ > 2.0) combining the methods. The results illustrate the potential applicability of vis‐NIR spectroscopy for rapid assessment/prediction of CT matrix .
关键词：CT; computed tomography; CTmatrix; density of field-moist soil matrix devoid of large macropores and stones; HU; Hounsfield units; MLR; multiple linear regression; OC; organic carbon; PCA; principal component analysis; PLSR; partial least squares regression; RPD; ratio of performance to deviation; RPIQ; ratio of performance to interquartile distance; vis-NIR; visible near-infrared.
