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http://dx.doi.org/10.7744/kjoas.20200092

Digital mapping of soil carbon stock in Jeolla province using cubist model  

Park, Seong-Jin (Division of soil and fertilizer, National Institute of Agricultural Science, RDA)
Lee, Chul-Woo (Division of soil and fertilizer, National Institute of Agricultural Science, RDA)
Kim, Seong-Heon (Division of soil and fertilizer, National Institute of Agricultural Science, RDA)
Oh, Taek-Keun (Department of Bio-Environmental Chemistry, College of Agriculture and Life Science, Chungnam National University)
Publication Information
Korean Journal of Agricultural Science / v.47, no.4, 2020 , pp. 1097-1107 More about this Journal
Abstract
Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.
Keywords
carbon stock; digital soil mapping; Jeolla province; soil organic carbon;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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