Articles | Volume 12, issue 11
https://doi.org/10.5194/amt-12-6017-2019
https://doi.org/10.5194/amt-12-6017-2019
Research article
 | 
20 Nov 2019
Research article |  | 20 Nov 2019

Neural network for aerosol retrieval from hyperspectral imagery

Steffen Mauceri, Bruce Kindel, Steven Massie, and Peter Pilewskie

Viewed

Total article views: 2,976 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,961 954 61 2,976 87 70
  • HTML: 1,961
  • PDF: 954
  • XML: 61
  • Total: 2,976
  • BibTeX: 87
  • EndNote: 70
Views and downloads (calculated since 11 Jun 2019)
Cumulative views and downloads (calculated since 11 Jun 2019)

Viewed (geographical distribution)

Total article views: 2,976 (including HTML, PDF, and XML) Thereof 2,682 with geography defined and 294 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Dec 2024
Download
Short summary
Aerosols are fine particles that are suspended in Earth’s atmosphere. A better understanding of aerosols is important to lower uncertainties in climate predictions. We propose measuring aerosols from satellites and airplanes equipped with hyperspectral cameras using an artificial neural network, a form of machine learning. We applied our neural network to hyperspectral observations from a recent airplane flight over India and find general agreement with independent aerosol measurements.