A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data

TL;DR

This review paper discusses how machine learning techniques applied to satellite data have advanced dust aerosol detection, highlighting the effectiveness of multi-spectral approaches and the potential for improved performance in modeling dust storms.

Contribution

It provides a comprehensive overview of machine learning applications in dust aerosol detection using satellite sensors, emphasizing recent advancements and challenges.

Findings

Multi-spectral methods are effective for visualization and analysis.

Machine learning improves dust aerosol detection performance.

Opportunities exist for solving unique dust modeling problems.

Abstract

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.