Near Real-Time Dust Aerosol Detection with Support Vector Machines for Regression

Abstract:

Remote sensing instruments operating in the near-infrared spectrum usually provide the necessary information for further dust aerosol spectral analysis using statistical or machine learning algorithms. Such algorithms have proven to be effective in analyzing very specific case studies or dust events. However, very few make the analysis open to the public on a regular basis, fewer are designed specifically to operate in near real-time to higher resolutions, and almost none give a global daily coverage. In this research we investigated a large-scale approach to a machine learning algorithm called “support vector regression”. The algorithm uses four near-infrared spectral bands from NASA MODIS instrument: B20 (3.66–3.84μm), B29 (8.40–8.70μm), B31 (10.78–11.28μm), and B32 (11.77–12.27μm). The algorithm is presented with ground truth from more than 30 distinct reported dust events, from different geographical regions, at different seasons, both over land and sea cover, in the presence of clouds and clear sky, and in the presence of fires. The purpose of our algorithm is to learn to distinguish the dust aerosols spectral signature from other spectral signatures, providing as output an estimate of the probability of a data point being consistent with dust aerosol signatures. During modeling with ground truth, our algorithm achieved more than 90% of accuracy, and the current live performance of the algorithm is remarkable. Moreover, our algorithm is currently operating in near real-time using NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) servers, providing a high resolution global overview including 64, 32, 16, 8, 4, 2, and 1km. The near real-time analysis of our algorithm is now available to the general public at http://dust.reev.us and archives of the results starting from 2012 are available upon request.