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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2022
Research article |
| 21 Feb 2022
| 21 Feb 2022
Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Jaakko Reinvall
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Arttu Väisänen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Henri Taskinen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Timo Lähivaara
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Larisa Sogacheva
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Pekka Kolmonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Kari Lehtinen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Ville Kolehmainen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Viewed
Total article views: 4,811 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Sep 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,446 | 1,261 | 104 | 4,811 | 136 | 150 |
- HTML: 3,446
- PDF: 1,261
- XML: 104
- Total: 4,811
- BibTeX: 136
- EndNote: 150
Total article views: 3,298 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Feb 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,351 | 862 | 85 | 3,298 | 122 | 133 |
- HTML: 2,351
- PDF: 862
- XML: 85
- Total: 3,298
- BibTeX: 122
- EndNote: 133
Total article views: 1,513 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Sep 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,095 | 399 | 19 | 1,513 | 14 | 17 |
- HTML: 1,095
- PDF: 399
- XML: 19
- Total: 1,513
- BibTeX: 14
- EndNote: 17
Viewed (geographical distribution)
Total article views: 4,811 (including HTML, PDF, and XML)
Thereof 4,622 with geography defined
and 189 with unknown origin.
Total article views: 3,298 (including HTML, PDF, and XML)
Thereof 3,173 with geography defined
and 125 with unknown origin.
Total article views: 1,513 (including HTML, PDF, and XML)
Thereof 1,449 with geography defined
and 64 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
14 citations as recorded by crossref.
- Deep Learning Model for Global Spatio-Temporal Image Prediction D. Nikezić et al.
- Retrieval of aerosol optical depth from INSAT-3DR for accurate geostationary monitoring of regional and temporal aerosol dynamics C. Tandule et al.
- A Neural Network–Enhanced Born Approximation for Inverse Scattering A. Desai et al.
- First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al.
- Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al.
- Post-process correction improves the accuracy of satellite PM2.5 retrievals A. Porcheddu et al.
- A Machine Learning Approach to Derive Aerosol Properties from All-Sky Camera Imagery F. Scarlatti et al.
- A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al.
- Opinion: Aerosol remote sensing over the next 20 years L. Remer et al.
- Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval P. Pelucchi et al.
- High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al.
- Real-Time Production of High-Resolution, Gap-Free, 3-Hourly AOD over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, and Air Quality Data S. Kim et al.
- Machine learning data fusion for high spatio-temporal resolution PM2.5 A. Porcheddu et al.
- Physical-Guided Transfer Deep Neural Network for High-Resolution AOD Retrieval D. Chen et al.
14 citations as recorded by crossref.
- Deep Learning Model for Global Spatio-Temporal Image Prediction D. Nikezić et al.
- Retrieval of aerosol optical depth from INSAT-3DR for accurate geostationary monitoring of regional and temporal aerosol dynamics C. Tandule et al.
- A Neural Network–Enhanced Born Approximation for Inverse Scattering A. Desai et al.
- First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al.
- Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al.
- Post-process correction improves the accuracy of satellite PM2.5 retrievals A. Porcheddu et al.
- A Machine Learning Approach to Derive Aerosol Properties from All-Sky Camera Imagery F. Scarlatti et al.
- A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al.
- Opinion: Aerosol remote sensing over the next 20 years L. Remer et al.
- Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval P. Pelucchi et al.
- High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al.
- Real-Time Production of High-Resolution, Gap-Free, 3-Hourly AOD over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, and Air Quality Data S. Kim et al.
- Machine learning data fusion for high spatio-temporal resolution PM2.5 A. Porcheddu et al.
- Physical-Guided Transfer Deep Neural Network for High-Resolution AOD Retrieval D. Chen et al.
Saved (final revised paper)
Latest update: 11 May 2026
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.
We have developed a machine-learning-based model that can be used to correct the Sentinel-3...
