  This paper reviews the most important information fusion data-driven
algorithms based on Machine Learning (ML) techniques for problems in Earth
observation. Nowadays we observe and model the Earth with a wealth of
observations, from a plethora of different sensors, measuring states, fluxes,
processes and variables, at unprecedented spatial and temporal resolutions.
Earth observation is well equipped with remote sensing systems, mounted on
satellites and airborne platforms, but it also involves in-situ observations,
numerical models and social media data streams, among other data sources.
Data-driven approaches, and ML techniques in particular, are the natural choice
to extract significant information from this data deluge. This paper produces a
thorough review of the latest work on information fusion for Earth observation,
with a practical intention, not only focusing on describing the most relevant
previous works in the field, but also the most important Earth observation
applications where ML information fusion has obtained significant results. We
also review some of the most currently used data sets, models and sources for
Earth observation problems, describing their importance and how to obtain the
data when needed. Finally, we illustrate the application of ML data fusion with
a representative set of case studies, as well as we discuss and outlook the
near future of the field.
