The minimum level for soil allocation using topsoil reflectance spectra: Genus or species?

Abstract

Spectroscopy has been used for rapid determination of soil physicochemical parameters and soil classification or allocation, usually at the great group level of soil genetic classification. Soil genetic classification is still the main system of classification used in China, on which the Chinese Soil Classification System is based, and includes order, suborder, great group, subgroup, genus, species, and subspecies. In this paper, we introduce a soil allocation model that uses topsoil spectral characteristics and determined the minimum level for soil allocation. The topsoil spectra of four typical soils from the Songnen Plain in Northeast China were used to extract spectral feature parameters (SFPs) with clear physical meaning for soil allocation at the great group, genus, and species levels, and principal components of reflectance and first-derivative reflectance were used for allocation to determine the optimal input. Multinomial logistic regression MLR, multi-layer perception neural network, MLPNN, and decision trees (DT) were used to build allocation models. The results show the following: 1) the first and second absorption valleys of the topsoil spectral curves are substantially different between soils at the great group and genus levels but similar at the species level; 2) the topsoil samples can be allocated with SFPs, for which genus is the minimum level within the Chinese Soil Classification System for soil allocation with topsoil hyperspectral reflectance; and 3) the DT model with SFPs as input is the most accurate at the genus level; the accuracy of allocation is 83.3% and the Kappa coefficient, an evaluation index, is 0.8. Our results suggest that soil allocations using topsoil hyperspectral reflectance can be performed accurately at genus level of classification, which can be of considerable help in research on the effects of soil minerals on soil spectral characteristics and in detailed soil mapping.