Estimation of Soil Organic Carbon (SOC) Stock in South Korea Using Digital Soil Mapping Technique

Abstract

ORCID Seong-Jin Park https://orcid.org/0000-0001-5102-3657 Seong-Heon Kim https://orcid.org/0000-0001-6047-9103 Taek-Keun Oh https://orcid.org/0000-0003-0215-0427 Estimation of soil organic carbon (SOC) stocks is highly relevant considering that SOCs is the central driver in soil fertility and climate change mitigation. This study aims to (i) evaluate the SOC stock in the first 0 30 cm and 0 100 cm soil layer on a national scale from spatially explicit explanatory environmental variables and a legacy soil database and (ii) the spatial distribution of SOCs at national scale through digital mapping technique. A spatial model was established using Cubist, a decision tree algorithm and based on soil data (s factor), climatic (c factor), topographic (r factor). Results showed that soil texture, soil parent, mean annual precipitatio and elevation were the most important predictors of SOCs. The Cubist prediction model had a Root Mean Squared Error (RMSE) equal to 19.5 at 0 30 cm, 68.7 at 0 100 cm. The predicted mean SOC stock from fitted models was 35 ton C ha-1 for 0 30 cm depth, 87 ton C ha-1 for 0 100 cm soil depth. In total, soil stored approximately 330 Mt C for 0 30 cm depth and 842 Mt C for 0 100 cm depth.