Assessing digital elevation model resolution for soil organic carbon prediction

Abstract

Baseline estimations of soil organic carbon (SOC) have been made globally using SOC models, earth system models, and digital soil mapping techniques. Digital elevation models (DEMs) underpin these analyses to incorporate the effect of topography on SOC. Here, we test the effect of DEM resolution on the relationships between topography and SOC across catchment to hillslope scales. Samples were collected using a nested field sampling approach in a grazed catchment on Eastern Australia. Topographic attributes were derived from 5 m, 25 m, 30 m, and 90 m resolution DEMs and then used to predict SOC using a Random Forest model. This was trained on a catchment-wide dataset and tested using the repeat samples of this dataset and finer scale field data. SOC was able to be predicted using topography in the catchment-wide datasets (model R2 = 0.37–0.51), however not in the finer-scale data (model R2 = 0–0.21). Variable importance was calculated from the modelling process, with elevation (as a surrogate for climate) being the main driver at the catchment-scale. In the finer scale datasets, topographic variables linked to soil redistribution were more important. As a result, SOC estimation methods using coarse resolution DEM data and large-scale sampling may be limited in capturing the effect of topography, having implications for SOC management and modelling.