Identification of soil profile classes using depth-weighted visible-near-infrared spectral reflectance

Vis-NIR spectral reflectance exhibited acceptable overall performance for identification of soil orders and suborders, with overall validation accuracies of 0.63 and 0.62, respectively, but low overall performance at group and subgroup levels, with overall validation accuracies of 0.40 and 0.28, respectively. The overall performance at different taxonomic levels was affected by the number of soil classes and the class distribution of the soil profiles. Soil classes with pedogenic processes associated closely with spectrally active soil properties or with characteristic profile patterns were most accurately identified, even if they were minority classes. The results show that the Vis-NIR spectral pattern of soil profile can be used to identify soil profile classes at higher taxonomic levels in the CST system. Combined with machine-learning techniques, the soil Vis-NIR spectral library will serve as an efficient tool for digital soil survey mapping and updating with the use of legacy soil samples and the reduction of conventional laboratory analyses.