A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data
It addresses dust storm detection for environmental and health monitoring, but is incremental as it synthesizes existing research.
This paper reviews machine learning algorithms for detecting dust aerosols from satellite data, finding that multi-spectral approaches are effective but machine learning improves performance and enables new solutions.
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.