Spatial disaggregation of a legacy soil map to support digital soil and land evaluation assessments in Scotland

In recent years, the importance of soils and soil functions has been recognised for supporting the delivery of ecosystem services and for the realisation of international initiatives, such as the UN Sustainable Development Goals. At the same time, Digital Soil Mapping (DSM) has emerged as a modelling technique that can satisfy increased end-user needs for new soil datasets by producing fine resolution soils and soil property maps to support complex digital soil and land evaluation assessments. Spatial disaggregation is a popular DSM technique that is used to transform legacy soil maps to more spatially-explicit soils datasets, which can also be used in conjunction with soil databases to generate digital soil property maps. In this study, we performed spatial disaggregation of the National Soil Map of Scotland (originally published at 1:250,000 scale) at the taxonomic level of Soil Series, with the specific objective to facilitate the production of harmonised digital soil property maps to support soil and land evaluation assessments in Scotland through linking to the Scottish Soil Database. We divided Scotland into Landscape Units of similar soil and landform characteristics and trained probability random forest models within each Landscape Unit using area-proportion random sampling of both single- and multiple- (complex) Soil Series map units and selected environmental covariates to produce Soil Series probability layers at 50 m grid resolution. The performance of the disaggregated Soil Series maps was evaluated using prediction uncertainties of individual soil types and independent soil profile classifications. Evaluation results indicated that the random forest algorithm was successful in promoting effective spatial disaggregation of both single soil and complex soil polygons and provided good prediction accuracies for most soil types with the exception of some of the least extensive soil types typically found within complex map units. This was attributed mainly to algorithm's tendency to favour dominant, more extensive classes, along with its difficulty to distinguish between similar soils within spatially diverse areas. However, training Soil Series models at a Landscape Unit level instead of nationally helped to limit both the underestimation of these minority soil types and the overestimation of the dominant ones. In addition, map evaluation results showed the usefulness of using the generated conditional Soil Series probabilities for exploring soil spatial variability, especially within complex areas such as river floodplains covered by multiple alluvial and non-alluvial soils. Overall, this study demonstrates the potential of using spatial disaggregation to extract pedological knowledge embedded in legacy soil maps and use it to generate new dynamic and harmonised soil and soil property maps by effectively using readily-available and easily-updated soils information from existing databases.