Volume 8, Issue 5, October 2020, Page: 170-180
Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data
Sonoyo Mukai, School of Applied Information Technology, The Kyoto College of Graduate Studies for Informatics, Kyoto, Japan
Makiko Nakata, Faculty of Applied Sociology, Kindai University, Higashi-Osaka, Japan
Toshiyuki Fujito, School of Applied Information Technology, The Kyoto College of Graduate Studies for Informatics, Kyoto, Japan
Itaru Sano, Faculty of Science and Engineering, Kindai University, Higashi-Osaka, Japan
Received: Oct. 4, 2020;       Accepted: Oct. 19, 2020;       Published: Oct. 26, 2020
DOI: 10.11648/j.ijema.20200805.16      View  183      Downloads  62
This work aimed at detection of aerosols above clouds (AAC). It has been known that AAC has significant potential to change the global radiation budget, namely plays an important role in elucidating climate change. First we examined the advantages of multichannel data from near-UV to thermal infrared (IR) including polarization channels at red and near-IR collected using the GCOM-C/SGLI. The near-UV data at 0.38μm and 0.41μm not only detected absorbing aerosols such as biomass burning aerosols (BBA) or mineral dust (DUST), but were also used to distinguish between BBA and DUST with short wavelength IR measurements at 1.63μm. Because understanding aerosol types facilitates subsequent aerosol characterization, classification algorithms for aerosol types have been dealt with since the previous work. Discriminant verification was performed using ground measurements from NASA/AERONET and practically examined in a case of large forest fire. Then the detection of optically thick clouds was challenged in a similar way to aerosol classification in order to lead such a final goal of this work as detection of aerosols above clouds. Subsequently some scenes concerned with DUST type aerosols or BBA ones above water clouds were detected using GCOM-C/SGLI radiance or polarization measurements, respectively, and validated with Terra/MODIS products.
BBA (Biomass Burning Aerosol), DUST, Multichannel Satellite Data, Near-UV, Color Ratio, Polarization
To cite this article
Sonoyo Mukai, Makiko Nakata, Toshiyuki Fujito, Itaru Sano, Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data, International Journal of Environmental Monitoring and Analysis. Vol. 8, No. 5, 2020, pp. 170-180. doi: 10.11648/j.ijema.20200805.16
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