Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1123-2022
© 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-15-1123-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Mar 2022
Research article |
| 04 Mar 2022
| 04 Mar 2022
Aerosol models from the AERONET database: application to surface reflectance validation
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Terrestrial Information Systems Laboratory (Branch Code 619), Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
Eric Vermote
Terrestrial Information Systems Laboratory (Branch Code 619), Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
Sergii Skakun
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Terrestrial Information Systems Laboratory (Branch Code 619), Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
Emilie Murphy
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Terrestrial Information Systems Laboratory (Branch Code 619), Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
Oleg Dubovik
Laboratoire d'Optique Atmosphérique, Université de Lille 1,
Villeneuve d'Ascq, 59665, France
Natacha Kalecinski
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Terrestrial Information Systems Laboratory (Branch Code 619), Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
Bruno Korgo
Laboratory of Thermal and Renewable Energy, Université Joseph
KI-ZERBO, Ouagadougou, Burkina Faso
Brent Holben
Biospheric Sciences Laboratory (Branch Code 618), Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA
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Cited
12 citations as recorded by crossref.
- A method of aerosol types classification combining MODIS data and Mie-scattering lidar detection data Z. Yang et al. https://doi.org/10.1016/j.optcom.2025.132694
- Collaborative Neural Networks Significantly Improve Global Aerosol Retrievals From Multiangle Polarimeters G. Fu et al. https://doi.org/10.1109/TGRS.2026.3685881
- Polarized reflectance in China from DPC-2/GF-5(02) polarimetric satellite measurements: dataset and model inter-comparison Q. Liu et al. https://doi.org/10.1016/j.jqsrt.2026.109961
- Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land G. Doxani et al. https://doi.org/10.1016/j.rse.2022.113412
- Validation of Global Gridded Aerosol Models in Inland/Coastal Water Atmospheric Correction for MODIS, VIIRS, and Landsat J. Lv et al. https://doi.org/10.1109/TGRS.2024.3427836
- Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters M. Montes et al. https://doi.org/10.3389/frsen.2022.860816
- Meteorology-constrained classification framework of pollution-dominated aerosols across China based on AERONET measurements C. Zhao et al. https://doi.org/10.1364/AO.609633
- Implementation of the MOSAIC aerosol module (v1.0) in the Canadian air quality model GEM-MACH (v3.1) K. Semeniuk et al. https://doi.org/10.5194/gmd-18-6479-2025
- Optical and Microphysical Properties of the Aerosol Field over Sofia, Bulgaria, Based on AERONET Sun-Photometer Measurements T. Evgenieva et al. https://doi.org/10.3390/atmos13060884
- Development and Classification of Japanese-Region-Specific Aerosol Models Based on 10-Year Sky Radiometer Observations H. Kobayashi et al. https://doi.org/10.2151/sola.2023-027
- The 50-year Landsat collection 2 archive C. Crawford et al. https://doi.org/10.1016/j.srs.2023.100103
- Aerosol types classification in coastal areas of China using sun photometer and Mie-scattering lidar simultaneous detection data Z. Yang et al. https://doi.org/10.1016/j.jqsrt.2026.109976
12 citations as recorded by crossref.
- A method of aerosol types classification combining MODIS data and Mie-scattering lidar detection data Z. Yang et al. https://doi.org/10.1016/j.optcom.2025.132694
- Collaborative Neural Networks Significantly Improve Global Aerosol Retrievals From Multiangle Polarimeters G. Fu et al. https://doi.org/10.1109/TGRS.2026.3685881
- Polarized reflectance in China from DPC-2/GF-5(02) polarimetric satellite measurements: dataset and model inter-comparison Q. Liu et al. https://doi.org/10.1016/j.jqsrt.2026.109961
- Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land G. Doxani et al. https://doi.org/10.1016/j.rse.2022.113412
- Validation of Global Gridded Aerosol Models in Inland/Coastal Water Atmospheric Correction for MODIS, VIIRS, and Landsat J. Lv et al. https://doi.org/10.1109/TGRS.2024.3427836
- Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters M. Montes et al. https://doi.org/10.3389/frsen.2022.860816
- Meteorology-constrained classification framework of pollution-dominated aerosols across China based on AERONET measurements C. Zhao et al. https://doi.org/10.1364/AO.609633
- Implementation of the MOSAIC aerosol module (v1.0) in the Canadian air quality model GEM-MACH (v3.1) K. Semeniuk et al. https://doi.org/10.5194/gmd-18-6479-2025
- Optical and Microphysical Properties of the Aerosol Field over Sofia, Bulgaria, Based on AERONET Sun-Photometer Measurements T. Evgenieva et al. https://doi.org/10.3390/atmos13060884
- Development and Classification of Japanese-Region-Specific Aerosol Models Based on 10-Year Sky Radiometer Observations H. Kobayashi et al. https://doi.org/10.2151/sola.2023-027
- The 50-year Landsat collection 2 archive C. Crawford et al. https://doi.org/10.1016/j.srs.2023.100103
- Aerosol types classification in coastal areas of China using sun photometer and Mie-scattering lidar simultaneous detection data Z. Yang et al. https://doi.org/10.1016/j.jqsrt.2026.109976
Saved (final revised paper)
Latest update: 27 Oct 2026
Short summary
From measurements of the sky performed by AERONET, we determined the microphysical properties of the atmospheric particles (aerosols) for each AERONET site. We used the aerosol optical thickness and its variation over the visible spectrum. This allows us to determine an aerosol model useful for (but not only) the validation of the surface reflectance satellite-derived product. The impact of the aerosol model uncertainties on the surface reflectance validation has been found to be 1 % to 3 %.
From measurements of the sky performed by AERONET, we determined the microphysical properties of...
