Saharan dust detection using multi-sensor satellite measurements
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Updated: Mar 7, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
Sriharsha Madhavan1, John J Qu2, X Hao2
1Environmental Science and Technology Center, Department of Geography and Geoinformation Sciences, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA; NOAA National Environmental Satellite, Data, and Information Service Center for Satellite Applications and Research, E/RA3, 5830 University Research Ct., College Park, MD 20740, USA; Science Systems and Applications Inc., 10210 Greenbelt Rd., Lanham, MD 20706, USA.
Abstract:
Contemporary scientists have vested interest in trying to understand the climatology of the North Atlantic Basin since this region is considered as the genesis for hurricane formation that eventually get shipped to the tropical Atlantic region and the Caribbean. The effects of atmospheric water cycle and the climate of West Africa and the Atlantic basin are hugely impacted by the radiative forcing of Saharan dust. The focus area in this paper would be to improve the dust detection schemes by employing the use of multi sensor measurements in the thermal emissive wavelengths using legacy sensors such as Terra (T) and Aqua (A) MODerate-resolution Imaging Spectroradiometer (MODIS), fusing with Ozone Monitoring Instrument (OMI). Previous work by Hao and Qu (2007) had considered a limited number of thermal infrared channels which led to a correlation coefficient R2 value of 0.765 between the Aerosol Optical Thickness (AOT) at 550 nm and the modeled dust index. In this work, we extend the thermal infrared based dust detection by employing additional channels: the 8.55 μm which has shown high sensitivity to the Saharan dust, along with water vapor channel of 7.1 μm and cloud top channel of 13.1 μm. Also, the dust pixels were clearly identified using the OMI based aerosol types. The dust pixels were cleanly segregated from the other aerosol types such as sulfates, biomass, and other carbonaceous aerosols. These improvements led to a much higher correlation coefficient R2 value of 0.85 between the modified dust index and the AOT in comparison to the previous work. The key limitations from the current AOT products based on MODIS and were put to test by validating the improved dust detection algorithm. Two improvements were noted. First, the dust measurement radiometry using MODIS is significantly improved by at least an order of 2. Second the spatial measurements are enhanced by a factor of at least 10.
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