Selection of terrain attributes and its scale dependency on soil organic carbon prediction

Terrain attributes are commonly used as predictors of soil organic carbon (SOC) in digital soil mapping. However, there are no fixed rules in the selection of suitable grid size and models with different attribute combinations. Past studies have used a few empirical, as well as pedological guidelines to determine scale dependency of terrain attributes on SOC prediction. The aim of this paper was to evaluate the scale dependency of terrain attributes using varying grid sizes and select the most important attributes and optimum grid size for SOC prediction. A 7500 km(2) area located in Denmark was selected; a total of 2,514,820 prediction models were generated in Cubist data-mining tool in which 8570 SOC observations and 22 terrain attributes at 71 different grid sizes ranging from 12.8 m to 2304 m were used as inputs. Terrain attributes were derived from the Light Detection And Ranging (LiDAR) based digital elevation model (DEM) (1.6 m x 1.6 m grid size) and was subsequently resampled to different resolutions by simple mean aggregation. Relative importance and usage of each terrain attribute in each prediction model were computed and only the top 5 attributes were reported for different attribute combinations. The results showed that the relative contribution of terrain attributes to predict SOC distribution varied by grid sizes, and by grid size and attribute combinations. Overall, Relative Slope Position (rsp), Channel Altitude (chnl_alti), and Standard Height (standh) were the three most important terrain attributes in the five-attribute-model at all grid resolutions and the remaining two attributes being Normalized Height (normalh) and Valley Depth (vall_depth) at resolutions finer than 30 m, and elevation and Channel Base (chnl_base) at resolutions coarser than 30 m. The models at 88 m and 92.8 m grid size (nearest to the 90 m SRTM data resolutions) and 30.4 m (nearest to the 30 m TM satellite image resolution) were validated. We observed that the model performance was dependent on grid size, and by attribute combinations. For example, for a 4-attribute model that used rsp, chnl_alti, elevation and vall_depth, the best performance was for 30.4 m compared to 88 m and 92.8 m grid sizes. We found that for modeling SOC distribution, the three terrain attributes rsp, chnl_alti, and standh were found to be the most important at all resolutions and should be considered as important variables in future SOC modeling studies in young moraine landscapes.