Aerosol types were characterized and classified using multispectral satellite data. The role of near-UV data in the detection of absorbing aerosols, such as biomass burning aerosols (BBA) or mineral dust particles (DUST), was examined on a global scale. An absorbing aerosol index (AAI) was proposed and defined as the ratio of the satellite-observed radiance (R) at a wavelength of 0.412 μm [R (0.412)] to that at 0.380 μm [R (0.380)] that can also detect nonabsorbing-type aerosols. Initially, the numerical AAI values were estimated for the BBAs and DUST from measurements collected by the Advanced Earth Observing Satellite-2/Global Imager (ADEOS-2/GLI). The Japanese short mission ADEOS-2 carried the GLI instrument with observation channels in the near-UV region. Not only the AAI index but also the short-wavelength infrared measurements were utilized to determine the dust detection index (DDI) defined as the ratio of R (2.210) to R (0.380) in order to discriminate BBAs from DUST. In addition, the AAI and DDI values were evaluated for the detection of clouds. The results allowed the classification criteria for DUST, BBA, other types of aerosols and clouds to be obtained. The Second-Generation Global Imager (SGLI) sensor is onboard the Japanese Global Change Observation Mission-Climate (GCOM-C) (SHIKISAI in Japanese) satellite launched on December 23, 2017. The SGLI has multiple channels (19) including near-UV and polarization sensors in the red and near-IR wavelengths. We also demonstrated the advantages of the SGLI for near-UV and polarization data for aerosol remote sensing. An understanding of aerosol types facilitated subsequent aerosol retrieval. Then, retrieval for classified aerosols was made based on the radiation simulations with multispectral radiance by GLI and polarization measurements by Polarization and Directionality of the Earth's Reflectances (POLDER)-2, respectively, mounted on the ADEOS-2 satellite. The proposed algorithms are expected to be available not only for the analysis of the SGLI data but also for other future missions. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.