A variety of statistical and geostatistical methods exist to estimate environmental data on e.g., a fine grid using sparsely sampled data (Isaaks and Srivastava, 1989 and Webster and Oliver, 2001). Statistical approaches to interpolate SOC and net soil redistribution, such as a (multiple) linear regression, perform well under some circumstances. Geostatistical approaches account for the spatial dependence (autocorrelation) of the property of interest. This spatial autocorrelation can be quantified using an empirical semi-variogram (or variogram) of the sampled data, where the semi-variance is plotted as a function of the separation distance. For a dataset z(x1), z(x2), …, z(xn), with separation distance (lag h) the semi-variance: