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Wednesday, September 25 • 1:30pm - 2:00pm
A Hybrid Algorithm for Mineral Dust Detection Using Satellite Data

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Peichang Shi (University of Maryland), Qianqian Song (University of Maryland), Janita Patwardhan (University of Maryland), Zhibo Zhang (University of Maryland), Jianwu Wang (University of Maryland), and Aryya Gangopadhyay (University of Maryland)

Mineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these negative impacts. In this paper, using satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Observation (CALIPSO), we compared several machine learning algorithms to traditional physical models and evaluated their performance regarding mineral dust detection. Based on the comparison results, we proposed a hybrid algorithm to integrate physical model with the data mining model, which achieved the best accuracy result among all the methods. Further, we identified the ranking of different channels of MODIS data based on the importance of the band wavelengths in dust detection. Our model also showed the quantitative relationships between the dust and the different band wavelengths.


Jianwu Wang

University of Maryland

Wednesday September 25, 2019 1:30pm - 2:00pm PDT
Macaw Room