In this paper, we develop an algorithm based on combining spectral, spatial, and temporal thresholds from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) daytime measurements to identify and track different aerosol types, primarily volcanic ash. Contemporary methods typically do not use temporal information to identify ash. We focus not only on the identification and tracking of volcanic ash during the Eyjafjallaj?kull volcanic eruption period beginning in 14 April and ending 17 May 2010 but also on a pixel-level classification method for separating various classes in the SEVIRI images. Three case studies on 13, 16, and 17 May are analyzed in extensive detail with other satellite data including from the Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), and Facility for Airborne Atmospheric Measurements (FAAM) BAe146 aircraft data to verify the aerosol spatial distribution maps generated by the SEVIRI algorithm. Our results indicate that the SEVIRI algorithm is able to track volcanic ash when the solar zenith angle is lower than about 65°. Furthermore, the BAe146 aircraft data show that the SEVIRI algorithm detects nearly all ash regions when AOD 0.2. However, the algorithm has higher uncertainties when AOD is
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