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This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin Hypercube Sampling (cLHS) to assess variability in soil properties at regional scale. The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables. The optimization also accounts for specific constraints and costs expressing the field sampling effort. The approach is demonstrated using a case study in Morocco, where a small but representative sample record had to be collected over a 15,000km² area within 2 weeks. The covariate space of the Latin Hypercube consisted of the first three principal components of ASTER imagery as well as elevation. Comparison of soil properties taken from the topsoil with the existing soil map, a geological map and lithological data showed that the sampling …