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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Oct 2020
Research article |
| 14 Oct 2020
| 14 Oct 2020
Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations
Willem J. Marais
CORRESPONDING AUTHOR
Space Science Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Robert E. Holz
Space Science Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Jeffrey S. Reid
Marine Meteorology Division, Naval Research Laboratory, Monterey, California, USA
Rebecca M. Willett
Department of Statistics & Computer Science, University of Chicago, Chicago, Illinois, USA
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- Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation A. Shastry et al. 10.1016/j.rse.2023.113556
- Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters K. Butler et al. 10.1017/eds.2025.17
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16 citations as recorded by crossref.
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- AICCA: AI-Driven Cloud Classification Atlas T. Kurihana et al. 10.3390/rs14225690
- Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation A. Shastry et al. 10.1016/j.rse.2023.113556
- Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters K. Butler et al. 10.1017/eds.2025.17
- Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network M. Dabrowski et al. 10.5194/gmd-18-3707-2025
- Modern methods to explore the dynamics between aerosols and convective precipitation: A critical review S. Metangley et al. 10.1016/j.dynatmoce.2024.101465
- Machine learning-driven analysis of temporal pupil dynamics for interpretable ADHD diagnosis S. Sharma et al. 10.1016/j.compbiomed.2025.110878
- Application of Acoustic Sensing in Systemic to Pulmonary Shunts in Ductal Dependent Infants Using Deep Learning M. Nikbakht et al. 10.1109/JSEN.2024.3371354
- Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation A. Kaps et al. 10.1109/TGRS.2023.3237008
- Cloud detection in East Asian urban areas in VIIRS day/night band by texture analysis J. PARK et al. 10.4287/jsprs.61.317
- Data-Driven Cloud Clustering via a Rotationally Invariant Autoencoder T. Kurihana et al. 10.1109/TGRS.2021.3098008
- CNN Multibeam Seabed Sediment Classification Combined with a Novel Feature Optimization Method M. Anokye et al. 10.1007/s11004-023-10079-5
- Processing of VENµS Images of High Mountains: A Case Study for Cryospheric and Hydro-Climatic Applications in the Everest Region (Nepal) Z. Bessin et al. 10.3390/rs14051098
- Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks C. White et al. 10.5194/amt-14-3371-2021
- Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data D. Robbins et al. 10.5194/amt-15-3031-2022
- High-Resolution Detection of Stratospheric Aerosols in CALIPSO Atmospheric Lidar Data Facilitated by the CALIOP-Density-Dimension Algorithm U. Herzfeld et al. 10.1109/TGRS.2025.3569546
Latest update: 28 Aug 2025
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.
Space agencies use moderate-resolution satellite imagery to study how smoke, dust, pollution...
