This Special Issue is dedicated to machine learning-based methods in:
•proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);
•computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;
•preprocessing Earth observation data to feed into global soil mapping;
•data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;
•optimizing temporal resolution to globally track the changes of soil properties;
•uncertainty assessment of the derived gridded soil information;
•other related topics.
A 40% discount can be granted to papers received from this conference/project on the basis that the manuscript is accepted for publication following the peer review process.