Seeing variability before sampling: soil stratification from fields to landscapes

Obtaining representative soil samples is fundamental for robust soil research, agronomic decision-making, and evidence-based policy planning. Because soil sampling and laboratory analyses are resource-intensive, sampling campaigns typically rely on selecting a limited number of representative sites using methodologies that depend on the spatial scale of interest and the availability of prior information. A common approach is stratified sampling, in which sampling locations are allocated based on known drivers of soil variability. However, the choice of stratification variables and their effectiveness across scales remain open questions.

In this study, we compare three soil sampling strategies: simple random sampling, land-use-based stratification, and stratification based remote sensing products, across multiple field-scale and small-landscape-scale case studies. The performance of each strategy is evaluated in terms of its ability to capture spatial variability in key soil properties while minimizing sampling effort using design efficiency as the evaluation criterion. Our results provide insights into the relative efficiency and robustness of remote sensing-based stratification compared to more commonly applied approaches, and highlight the conditions under which each sampling strategy is most appropriate.