Articles | Volume 11, issue 3
https://doi.org/10.5194/amt-11-1529-2018
© Author(s) 2018. 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-11-1529-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2018
Research article |
| 19 Mar 2018
Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Tero Mielonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Mikko R. A. Pitkänen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
Robert C. Levy
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Virginia R. Sawyer
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sami Romakkaniemi
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Ville Kolehmainen
University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
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Cited
12 citations as recorded by crossref.
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
- Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5 J. Li et al. 10.1016/j.atmosenv.2020.117293
- A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. 10.5194/amt-13-373-2020
- Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method X. Chen et al. 10.1016/j.rse.2020.112006
- Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface Y. Huang et al. 10.1029/2019EA000809
- An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation N. Schutgens et al. 10.5194/acp-20-12431-2020
- SALSA2.0: The sectional aerosol module of the aerosol–chemistry–climate model ECHAM6.3.0-HAM2.3-MOZ1.0 H. Kokkola et al. 10.5194/gmd-11-3833-2018
- Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo R. Müller & U. Pfeifroth 10.5194/amt-15-1537-2022
- Aerosol optical depth retrieval over land from OCEANSAT-2/ OCM- 2 data – A simple physics based approach M. Mehta et al. 10.1016/j.apr.2022.101339
- A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements I. Katsev et al. 10.3390/rs13091831
- A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. 10.5194/essd-14-1193-2022
- Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS) D. Wang et al. 10.1007/s10661-020-08299-x
11 citations as recorded by crossref.
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
- Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5 J. Li et al. 10.1016/j.atmosenv.2020.117293
- A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. 10.5194/amt-13-373-2020
- Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method X. Chen et al. 10.1016/j.rse.2020.112006
- Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface Y. Huang et al. 10.1029/2019EA000809
- An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation N. Schutgens et al. 10.5194/acp-20-12431-2020
- SALSA2.0: The sectional aerosol module of the aerosol–chemistry–climate model ECHAM6.3.0-HAM2.3-MOZ1.0 H. Kokkola et al. 10.5194/gmd-11-3833-2018
- Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo R. Müller & U. Pfeifroth 10.5194/amt-15-1537-2022
- Aerosol optical depth retrieval over land from OCEANSAT-2/ OCM- 2 data – A simple physics based approach M. Mehta et al. 10.1016/j.apr.2022.101339
- A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements I. Katsev et al. 10.3390/rs13091831
- A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. 10.5194/essd-14-1193-2022
Latest update: 29 Mar 2023
Short summary
Atmospheric aerosols are small solid or liquid particles suspended in the atmosphere and they have a significant effect on the climate. Satellite data are used to get global estimates of atmospheric aerosols. In this work, a statistics-based Bayesian aerosol retrieval algorithm was developed to improve the accuracy and quantify the uncertainties related to the aerosol estimates. The algorithm is tested with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data.
Atmospheric aerosols are small solid or liquid particles suspended in the atmosphere and they...
