Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5459–5480, 2020
Atmos. Meas. Tech., 13, 5459–5480, 2020

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 et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Willem Marais on behalf of the Authors (14 Jul 2020)  Author's response    Manuscript
ED: Publish as is (27 Jul 2020) by Alexander Kokhanovsky
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.