Abstract:
Analysing spatial patterns of soil properties in a landscape requires a sampling strategy that adequately covers
soil toposequences. In this context, we developed a hybrid methodology that couples global weighted principal
component analysis (GWPCA) and cost-constrained conditioned Latin hypercube algorithm (cLHC). This
methodology produce an optimized sampling stratification by analysing the local variability of the soil property,
and the influence of environmental factors. The methodology captures the maximum local variances in the global
auxiliary dataset with the GWPCA, and optimizes the selection of representative sampling locations for sampling
with the cLHC. The methodology also suppresses the subsampling of auxiliary datasets from areas that are less
representative of the soil property of interest. Consequently, the method stratifies the geographical space of
interest in order to adequately represent the soil property. We present results on the tested method (R2 = 0.90 and
RMSE = 0.18 m) from the Guinea savannah zone of Ghana.
