Spatial extrapolation of topographic models for mapping soil organic carbon using local samples

Accurate prediction of soil organic carbon (SOC) at fine scales is frequently limited by data availability but fine-scale maps of soil properties are often of great importance for local agricultural land management and watershed processes assessment. Terrain attributes derived from elevation have been commonly used predictor variables in SOC mapping. The aim of this study is to map SOC density (0–30 cm) using hybrid methods that combine regional topographic models and a residual kriging procedure using local samples from an area of interest and evaluate the effects of different sampling designs for residual kriging on final map quality. The regional topographic model was developed based on light detection and ranging (lidar)-derived topographic covariates using random forest to map SOC density on a cropland site intensively sampled for SOC (n = 224). To test ability to generate a local calibration of this regional model, a second intensively sampled cropland site (n = 225) was used to generate a reference SOC density map where residual kriging was implemented involving different numbers of local samples (n = 10–255) and different sample selection schemes: stratified random sampling (SRS) and spatial coverage sampling with close-pairs inclusions (SCS+). To generate a population of local sample sets, 500 repeats were conducted for each scheme from the reference map. By analyzing the distribution of relative mean error (RME) of the overall map for the second site with repeated selection of local samples, we found a smaller variation in RME for SCS+ than that for SRS for a given sample size. However, the E(RME) estimates for SCS+ slightly varied with number of local samples used while the E(RME) for SRS was stabilized at zero over that range in local sampling intensity. There was negligible difference between the distributions of RME for the two sampling schemes at individual points, except a finding of somewhat lower RME for SCS+ based point estimates. These results indicate that SCS+ can generate smaller variation in overall map quality while SRS can provide unbiased mean estimate for an area of interest. This study demonstrates the utility of extrapolating a regional topographic model by use of a limited number of new samples for local calibration and provides insight for practitioners who are interested in mapping soil carbon distribution in sparsely sampled areas of interest.