Review of dust storm detection algorithms for multispectral satellite sensors

LI, Jing, Wong, Man Sing, Lee, Kwon Ho, Nichol, Janet and Chan, P W (2020) Review of dust storm detection algorithms for multispectral satellite sensors. Atmospheric Research. a105398. ISSN 0169-8095

[img] PDF
Restricted to SRO admin only
Available under License Creative Commons Attribution-NonCommercial No Derivatives.

Download (661kB)

Abstract

Satellite remote sensing has been extensively utilized for monitoring dust storms in space and time. Dust storm detection using satellite observations is important to analyze the dust storm trajectories and sources. This paper reviews the algorithms for dust storm detection used in multispectral satellite sensors, spanning visible to thermal wavelengths. Four categories of dust detection algorithms are summarized, namely, dust spectral index algorithms, temporal anomalous detection algorithms, spatial coherence tested algorithms (physical-based algorithms) and machine learning-based algorithms. Following discussions of dust storm detection algorithms, the dust presence validation methods are also reviewed. Future developments for dust storm detection are focused upon three aspects: detection of dust storms at nighttime; development of more efficient machine learning methods for retrieval; and integrating physical and machine learning methods for satellite images.

Item Type: Article
Schools and Departments: School of Global Studies > Geography
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 01 Dec 2020 08:02
Last Modified: 01 Dec 2020 08:02
URI: http://sro.sussex.ac.uk/id/eprint/95405

View download statistics for this item

📧 Request an update