Models of soil processes require information on key parameters and input variables. As we consider more complex models, applied to larger geographical regions, so the demand for information on these inputs becomes harder to meet. Digital soil mapping is concerned with the provision of spatial information on soil properties on the basis of ancilliary variables, such as proxy and remote sensor data, and limited direct measurements.
In this session we will address the current state of the art in digital soil mapping, addressing problems such as the development of spatial and non-spatial soil inference systems and the quantitative treatment of the inevitable uncertainty in our predictions. There is particular interest in the use of proximal and remote sensing technologies, such as geophysical measurements (conductivity measurements, GPR, passive gamma radiometry etc) as a basis for prediction of soil properites, and therefore an important problem is how to extract information at appropriate spatial scale from multiple sources of data. We will also aim to identify priorities for the future in what is an active area of research.