Soil Property, Carbon Stock and Peat Extent Mapping at 10 m Resolution in Scotland Using Digital Soil Mapping Techniques

The estimation of soil carbon stocks is an important component in environmental planning, policy and land management, particularly in the context of climate change mitigation. The following work presents national-scale soil property mapping at 10 m resolution, a level of detail not previously attempted in Scotland. The ultimate aim of this work is to facilitate carbon stock estimation from the soil properties to help inform planning and policy. Scottish organisations possess extensive field-based soil property records obtained through various initiatives, but coverage is inevitably constrained by cost and labour. A modelling-based approach informed by both field measurement, remote sensing imagery and other spatial covariates has the potential to fill the gaps in field-based accounts, providing contiguous estimates of soil carbon content, bulk density, profile depth, organic layer thickness and peat extent. Carbon content, bulk density and profile depth were predicted using machine learning techniques, yielding $r^2$ scores of 0.78, 0.65 and 0.7, respectively. Presence of peat was determined by the thickness of the organic layer for every grid cell profile (30% carbon content) that was greater or equal to 50 cm. National carbon stock was calculated by integrating the predicted soil properties down the full profile depth. This work demonstrates that soil properties can be mapped effectively using digital soil mapping techniques at high resolution, on a national scale, providing estimates of carbon stock and peat extent to aid policy makers in decision making.