Abstract

The aim of this work is to present a method for “intelligent”, field-scale digital soil mapping based on visible-near infrared (vis-NIR) reflectance spectroscopy, in combination with statistical analysis (Principal Component Analysis, PCA and geostatistics). The study was carried out in a site of southern Italy. With reference to a 50 × 50 cell size grid, 240 soil samples were collected to a depth of 20–30 cm. The soil was analyzed by vis-NIR reflectance spectroscopy and the data were decomposed by PCA. The first three components (PC1, PC2, PC3) explained 98% of the total variance of the initial data set and therefore they were selected for the assessment of soil spatial variability by variography and kriging (geostatistics). The resulting PC1, PC2 and PC3 kriging maps were interpreted in the light of the information contents on reflectance spectra and compared with the results of a previous, conventional soil survey. The presented strategy seems to be efficient and reliable to use, when mapping soil spatial variability. * Corresponding author

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