Special Issue on RS entitled Global Gridded Soil Information Based on Machine Learning

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.

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Author:

He is Full Professor of Water Management, Hydrology and Hydraulic Constructions at the University of Naples Federico II. His research activities focus on distributed modeling, flood risk, stochastic processes in hydrology and UAS-based monitoring.