In environmental research, there is an extensive knowledge of the underlying processes that
cause a distinct spatial landscape pattern of system properties. Statistical sampling theory …

The conditioned Latin hypercube sampling (cLHS) algorithm is popularly used for planning
field sampling surveys in order to understand the spatial behavior of natural phenomena …

In digital soil mapping (DSM), a fundamental assumption is that the spatial variability of the
target variable can be explained by the predictors or environmental covariates. Strategies to …

In balanced sampling a linear relation between the soil property of interest and one or more
covariates with known means is exploited in selecting the sampling locations. Recent …

This paper presents the conditioned Latin hypercube as a sampling strategy of an area with
prior information represented as exhaustive ancillary data. Latin hypercube sampling (LHS) …

Soil properties are important because they determine the soil's suitability for different types
of plant growth, ecosystems and biota functioning. Soil properties influence nutrient cycling …

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 …

Spatial soil sampling is an integral part of a soil survey aimed at describing spatial variability
in soil properties. We propose considering the soil sampling procedure as a task of optimal …

Legacy soil data form an important resource for digital soil mapping and are essential for
calibration of models for predicting soil properties from environmental variables. Such data …

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 …