  Dust storms are associated with certain respiratory illnesses across
different areas in the world. Researchers have devoted time and resources to
study the elements surrounding dust storm phenomena. This paper reviews the
efforts of those who have investigated dust aerosols using sensors onboard of
satellites using machine learning-based approaches. We have reviewed the most
common issues revolving dust aerosol modeling using different datasets and
different sensors from a historical perspective. Our findings suggest that
multi-spectral approaches based on linear and non-linear combinations of
spectral bands are some of the most successful for visualization and
quantitative analysis; however, when researchers have leveraged machine
learning, performance has been improved and new opportunities to solve unique
problems arise.
