Mapping peat in Scotland with remote sensing and site characteristics

Abstract

Mapping the spatial distribution of organic soil is important for decision making in land management. Organic soil types have a strong effect on carbon storage, water availability and quality, biodiversity and many other ecosystem services. A large proportion of Scotland's soil is organic, and is classed as peats or organomineral. Existing soil maps for Scotland include a 1:25 000 map of eastern and southern parts of the country that identifies the major soil subgroup at each location, or a 1:250 000 map covering the whole country that gives proportions of the major soil group within mapping units. A detailed map of the organic soils of Scotland that disaggregates these mapping units is therefore required. Existing data were used from several Scottish soil surveys and remote sensing data (Landsat 8) to develop a model for predicting whether a site has mineral, organomineral (mineral with peaty topsoil) or organic (peat) soil. The model was applied across Scotland at a grid resolution of 100 m. Predictive accuracies of 68.1% (mineral), 61.3% (organomineral) and 81.8% (organic) were obtained, with an overall prediction accuracy of 69.8%. Training the model with two classes only, non‐organic (including mineral and organomineral) and organic, gave accuracies of 83.9 and 93.6%, respectively, and an overall accuracy of 86.4%. A map of prediction confidence was also produced. The results show how the approach can be used to disaggregate the existing map units and predict mineral and organic soils at locations within a map unit. The map is being used to prioritize monitoring efforts in catchments across Scotland.Highlights Use of remote sensing and spatial covariates to map peat in Scotland. Resulting peat maps will be useful for decision making on land management and carbon storage in soil. Peat is mapped accurately in most areas, although uncertainty is greater in complex landscapes. Combination of remote sensing and spatial covariates improves accuracy of mapping.