Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5459–5480, 2020
https://doi.org/10.5194/amt-13-5459-2020
Atmos. Meas. Tech., 13, 5459–5480, 2020
https://doi.org/10.5194/amt-13-5459-2020

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.

Model code and software

TensorFlow-Slim image classification model library Google https://github.com/tensorflow/models/tree/master/research/slim

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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.