DSM for global and continental applications: model applicability, spatial uncertainty and maps assessment

Digital Soil mapping (DSM) at continental and global scale provides standardised global information layers. It is also an important tool to create soil information layers for areas for which local soil survey information is lacking. The recent availability of global and continental remote sensing derived products coupled with the ease-of-access to computational resources has made the production of such layers easier across the globe. Therefore, it is ever more important to assess the quality of DSM-derived products, in particular the type of information they can actually provide to users (i.e., fitness for intended use).  

DSM studies commonly assess prediction uncertainty using various approaches, including multiple simulations or quantile random forests. However, this does not encompass all the potential elements that could be used to characterise the uncertainty of a DSM product. In this study we are going to assess maps based also on area of applicability (i.e., the area in covariate space where the model learns about relationships based on the training data) and the landscape heterogeneity both in the landscape itself and in covariate space. 

We present examples of continental and global mapping products, highlighting main uncertainty-related issues and how these influence suitability for intended use by stakeholders, decision makers and users in general at the given resolution. The examples come from a range of projects with different aims and goals. The results permit some practical reflections on how to integrate all the above elements to identify regions where the confidence in the predictions is highest and the associated uncertainty  lowest. We will integrate the practical reflections with information collected from a user survey on requirements and usability of continental and global DSM products.