Are topsoil spectra or soil-environmental factors better indicators for discrimination of soil classes?

Abstract

Visible and near-infrared (Vis-NIR) spectroscopy (350–2500 nm) is often used to discriminate different soil classes. Discrimination variables included generally reflectance, topographic factors, soil texture, and climate. In the present study, more soil properties, soil organic matter, soil pH, bulk density and thickness, were considered to build soil classes model, and the most important discrimination variables were identified. Reasons for misclassification and challenges in elaborating the soil class map were analyzed. The topsoil spectra of 1432 soil samples of nine soil classes in northeast China were measured and converted into continuum removal (CR) curves, and principal component analysis (PCA) was applied to extract spectral information for reducing data redundancy. Other discrimination variables, soil-environmental factors, were acquired, including topographic factors, soil texture, climate, and four soil properties of each soil sample. The importance of soil–environmental factors and CR spectra was calculated for building models. Random forest (RF) models were trained to discriminate nine soil classes based on five kinds of variables, and they were the principal components (PCs) of reflectance, PCs of CR spectra, soil–environmental factors, PCs of CR spectra + soil–environmental factors, and optimum combinations between CR spectra and soil–environmental factors according to the importance. Optimum discrimination variables of each soil class were selected to build random forest models, and then the sum accuracy was calculated. Our study showed that: (1) it was challenging to acquire a high discrimination accuracy using reflectance and CR spectra for large-scale soil data; (2) soil–environmental factors were more suitable to discriminate between soil classes than reflectance and CR spectra in northeast China; (3) the order of importance of discrimination variables was soil texture > climate > pH > thickness > other soil–environmental factors; and (4) the RF model with optimum combinations between CR spectra and soil–environmental factors led to an accuracy of 81.5 % and a kappa coefficient of 0.77. We validated soil-environmental factors were better than topsoil spectra for discrimination of soil classes, and we found soil constituents in soil-environmental factors had high importance, and we successfully discriminated nine soil classes with 1432 soil samples by optimum combinations between topsoil spectra and soil–environmental factors at a large scale.