Abstract

Texture is one of the main properties affecting the accuracy of visible (vis) and near infrared (NIR) spectroscopy during on-the-go measurement of soil properties. Classification of soil spectra into predefined texture classes is expected to increase the accuracy of measurement of other soil properties using separate groups of calibration models, each developed for one texture class. A mobile, fibre-type, vis-NIR spectrophotometer (Zeiss Corona 1.7 vis-NIR fibre), with a light reflectance measurement range of 306.5–1710.9 nm was used to measure the light reflectance from fresh soil samples collected from many fields in Belgium and northern France. A total of 365 soil samples were classified into four different texture classes, namely, coarse sandy, fine sandy, loamy and clayey soils. The factorial discriminant analysis (FDA) was applied on the first five principal components obtained from the principal component analysis performed on the vis-NIR spectra in order to classify soils into the four assigned groups. Correct classification (CC) of 85.7% and 81.8% was observed for the calibration and validation data sets, respectively. However, validation of the vis-NIR-FDA technique on the validation set showed poor discrimination between the coarse sandy and fine sandy soil groups, with a great deal of overlapping. Therefore, the soil groups were reduced to three groups by combining the coarse sandy and fine sandy soil groups into one group and FDA was applied again. A better classification was obtained with CC of 89.9 and 85.1% for the calibration and validation data sets, respectively. However, the CC for the sand group in the validation set was rather small (46.7%), which was attributed to the small sample number and poor correlation between sand fraction and vis-NIR spectroscopy. It was concluded that vis-NIR-FDA is an efficient technique to classify soil into three main groups of sandy (light soils), loamy (medium soils) and clayey (heavy soils). Additional samples from the sandy and clayey groups should be included to improve the accuracy of the vis-NIR-FDA classification models to be used for an on-the-go vis-NIR measurement system of soil properties.

© 2005 NIR Publications

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