Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 10
Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 10
Research article
 | 
14 Oct 2020
Research article |  | 14 Oct 2020

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Willem J. Marais, Robert E. Holz, Jeffrey S. Reid, and Rebecca M. Willett

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Latest update: 20 Nov 2024
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Short summary
Space agencies use moderate-resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impact the Earth's climate; these space agencies include NASA, ESA and the China Meteorological Administration. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement on current aerosol and cloud detection algorithms.
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