Abstract:
With respect to sampling for regression-based digital soil mapping
(DSM), the above all aim is to ensure that the spatial variability
of the soil is well-captured without introducing any bias, while
the design remains feasible according to operational constraints
such as accessibility, man power and cost. Representativeness of
the sample concerning the population to be sampled needs to be
guaranteed in any regression-based modelling approach. Four selected
sampling designs were adapted to show that basically any
design may be optimised to represent the n-dimensional predictor
space of a particular area, while selecting points is only permitted
from a small accessible sub-area or from outside the area. Sampling
efficiency may be evaluated based on the representation of
the predictor space. However, not only each predictor’s probability
function but also the interaction between predictors may have to
be considered, to select a representative sample. Instead of sampling
a previously un-sampled area with limited accessibility, the
four sampling designs may also be used to subsample an existing
dataset and, thereby, optimise a suboptimal dataset based on the
predictor space of the area which shall be mapped by DSM.
