Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/amt-15-895-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-895-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
Research article
 | 
21 Feb 2022
Research article |  | 21 Feb 2022

Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product

Antti Lipponen, Jaakko Reinvall, Arttu Väisänen, Henri Taskinen, Timo Lähivaara, Larisa Sogacheva, Pekka Kolmonen, Kari Lehtinen, Antti Arola, and Ville Kolehmainen

Viewed

Total article views: 3,197 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,405 719 73 3,197 68 55
  • HTML: 2,405
  • PDF: 719
  • XML: 73
  • Total: 3,197
  • BibTeX: 68
  • EndNote: 55
Views and downloads (calculated since 27 Sep 2021)
Cumulative views and downloads (calculated since 27 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,197 (including HTML, PDF, and XML) Thereof 3,078 with geography defined and 119 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
We have developed a machine-learning-based model that can be used to correct the Sentinel-3 satellite-based aerosol parameter data of the Synergy data product. The strength of the model is that the original satellite data processing does not have to be carried out again but the correction can be carried out with the data already available. We show that the correction significantly improves the accuracy of the satellite aerosol parameters.
Read more