Summary
Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the
Regional applicability
The review is global in scope and provides international benchmarking for soil mapping methodologies. United Kingdom-based DSM work would fall within this broader context; the findings on machine learning adoption, covariate selection, and predictive accuracy (e.g. soil pH R² = 0.60–0.63 at 0–30 cm) are applicable to UK soil survey modernisation efforts. The identified gaps in deep soil mapping and representation of certain regions may inform UK priorities for soil monitoring infrastructure.
Key measures
Number and trends in DSM publications; geographic distribution of DSM studies; soil properties mapped (organic matter/carbon, pH, texture); soil depths (0–30, 30–100, 100–200 cm); environmental covariates used; model types employed; predictive accuracy (R² values for soil pH, texture, SOC)
Outcomes reported
Systematic review of 244 digital soil mapping (DSM) articles (2003–2021) assessing progress in broad-scale mapping of 12 mandatory GlobalSoilMap soil properties. The review documents trends in DSM methodology, geographic coverage, target soil properties, soil depths studied, environmental covariates used, and predictive model performance across regions.
Topic tags
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