Spatial soil information is fundamental for environmental modelling and land use management. Spatial representation (maps) of separate soil attributes (both laterally and vertically) and of soil-landscape processes are needed at a scale appropriate for environmental management. The challenge is to develop explicit, quantitative, and spatially realistic models of the soil-landscape continuum to be used as input in environmental models, such as hydrological, climate or vegetation productivity (crop models) while addressing the uncertainty in the soil layers and its impact in the environmental modelling. Modern advances in soil sensing, geospatial technologies, and spatial statistics are enabling exciting opportunities to efficiently create soil maps that are more consistent, detailed, and accurate than previous maps while providing information about the related uncertainty. The production of high-quality soil maps is a key issue because it enables stakeholders (e.g. farmers, planners, other scientists) to understand the variation of soils at the landscape, field, and sub-field scales. Digital soil mapping (DSM) is examining the soil landscape in dimensions and resolutions that were not previously possible, pushing our understanding of the soil system. The methodology of digital soil mapping has made huge advances recently, moving from a research tool to a tool ready for application. Yet there are still key challenges in spatial relationships, scale, interacting factors of soil formation, and modelling and communication of the uncertainty. Recent advances in computer power and data availability impact DSM in resolution and extents that can be addressed.
However, the availability of the modern advances enabling DSM does not automatically create better soil maps. All presentations related to the tools of digital soil mapping, the philosophy and strategies of digital soil mapping at different scales and for different purposes are welcome. Examples of implementation and use of digital soil maps in different disciplines such as agricultural (e.g. crops, food production) and environmental (e.g. element cycles, water, climate) modelling are also welcomed.