ALBERT

Description: Soil Carbon (C) is central to the functioning of ecosystems and climate change mitigation. It represents the largest terrestrial pool and much of it, is stored in forest soils. Soil Organic Carbon (SOC) in a forest varies not only laterally, but also vertically (i.e., with depth). However, the SOC content of forest soil horizons has not been investigated over large scales, despite its importance for framing our understanding of soil function. Visible–Near Infrared (vis–NIR) reflectance spectroscopy enables rapid and cost-effective examination of forest SOC distribution, both laterally and vertically. This study aims to evaluate the potential of vis–NIR spectroscopy for classifying and predicting the SOC concentration of organic and mineral horizons in forests of the Czech Republic. We investigated 1080 forest sites across the country, each with five soil horizons, representing the Litter (L), Fragmented (F), and Humus (H) organic horizons, as well as the A1 (depth of 2–10 cm) and A2 (depth of 10–40 cm) mineral horizons. We, then, used Support Vector Machines (SVMs) to classify the soil horizons based on their spectra and also to model the SOC concentration of (i) the profile (organic and mineral horizons together), (ii) only the organic horizons, (iii) only the mineral horizons, and (iv) each individual horizon separately. The models were validated using 10-repeated 10-fold cross-validation. Results show that the SVM with radial basis kernel could accurately classify the soil horizons (Correct Classification Rate (CCR) of 70% and Kappa coefficient of 0.63). The SOC model developed for the soil profile performed well (R2 = 0.76 and RMSE = 1.63%). The model of the combined organic horizons was considerably more accurate than that of the combined mineral horizons (R2 = 0.78 and R2 = 0.53, respectively). Estimates of SOC in the individual soil horizons had R2 values greater than 0.63 but those of the F and A1 models were better with R2 〉 0.70. The study indicates that vis–NIR spectroscopy can effectively characterize the SOC concentration of the highly variable forest soil horizons in the Czech Republic.