Land-Surface Segmentation as sampling framework for soil mapping

Abstract—Current sampling methods require a large number of samples to account for spatial variation of environmental covariates, which often conflicts the available financial resources. Thus, efficient sampling strategies are desirable. The aim of this study was to evaluate the potential of land-surface segmentation in stratifying a landscape into homogeneous areas, which can be used as support in optimizing soil sampling. The experiments were carried out in a study area where soil samples were available. Land-surface variables were derived from DEMs and segmented with a multiresolution segmentation (MRS) algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment, based on which predictions of the A-horizon thickness and soil types, were made. Predictions based on the land-surface segmentation sampling schemes outperformed predictions based on simple random sampling and conditioned Latin hypercube, respectively