Soil sampling strategies for spatial prediction by correlation with auxiliary maps

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Optimization of sample patterns for universal kriging of environmental variables (2007, methodology): “To strike a balance between spreading in feature and geographic space, Hengl et al. (2003) proposed an equal range design for mapping by regression-kriging.”
Predicting into unknown space? Estimating the area of applicability of spatial prediction models (2020, background): “2012) and is 320 unlikely to cover the entire environmental conditions of heterogeneous environments even when a sampling design 321 is planned with the aim to cover the range of the predictor space (Hengl et al. 2003).”
A conditioned Latin hypercube method for sampling in the presence of ancillary information (2006, background): “Hengl et al. (2003) proposed sampling along the principal components of the environmental variables; the number of samples taken from each of the components is related to the proportion of the total variance described by each of the principal component.”
Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping (2016, unknown): “Moreover, the selection followed the criterion of a low correlation ( r collinearity < 0.4) within the subset of covariates to avoid collinearity ( Gessler et al., 2000; Hengl et al., 2003; Mulder et al., 2013).”
On digital soil mapping (2003, background): “Heuvelink and Webster (2001) have discussed the merger of discrete and continuous models of spatial variation.”