Review of dust storm detection algorithms for multispectral satellite sensors

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.