Soil organic carbon prediction with terrain derivatives using geostatistics and sequential Gaussian simulation

This current study investigated the relationship between soil organic carbon (SOC) and terrain derivatives on soil developed on dissimilar lithology while comparing the best modelling approach. Sixty (n = 60) bulk soil samples were taken from the depth of 0–30 cm according to five identified basement complex materials and analyzed for SOC. The models considered are ordinary kriging (OK), principal components kriging (PCA_OK) and regression kriging (RK). For the prediction's accuracy, 2-fold, and leave one out (LOOCV) cross-validation was carried out. The study indicated SOC in soil developed on granite gneiss (2.12%) and Biottite hornblende gneiss (2.05%) to be significantly (p < 0.05) higher than those on other lithology materials. SOC was significantly and positively correlated with slope (r = 0.44), channel network (r = 0.46), elevation (r = 0.47) and relative slope length (r = 0.20). The RK model showed higher performances in estimating SOC considering 2-fold cross-validation (RMSE = 0.48, ME = − 0.02, MSE = 0.23 and MAE = 0.39). Relative difference (RD) was positive when RK was compared with OK and PCA_OK, further suggesting RK as a better performing model. RK improved the map's structure with terrain derivatives, but the area was generally low in SOC. Model limitations showed that OK requires larger samples to improve the quality of prediction. Also, RK significant dissatisfaction is that the regression model parameters and covariance function parameters need to be estimated simultaneously. Simultaneously, PCA_OK tries to reduce a large number of interdependent variables to smaller number variables. In comparison, the sequential Gaussian simulation (SGS) revealed that the possible simulation realizations discovered more detail information than the original interpolated maps except for RK. The study recommended that SGS and RK map be adopted when making a precise land management decision