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
| 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
20 citations as recorded by crossref.
- Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt M. Elshora 10.1007/s10661-023-11118-8
- 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
- Bayesian Aerosol Retrieval-Based PM2.5 Estimation through Hierarchical Gaussian Process Models J. Zhang et al. 10.3390/math10162878
- Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf M. Pashayi et al. 10.1016/j.apr.2023.101948
- Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI F. Yin et al. 10.5194/gmd-15-7933-2022
- 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
- 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
- A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
- 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
- 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
- Design of a Computer-Based Legal Information Retrieval System F. Wu et al. 10.1155/2022/6942773
- Satellite-based evaluation of AeroCom model bias in biomass burning regions Q. Zhong et al. 10.5194/acp-22-11009-2022
- 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
- Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification A. Ibrahim et al. 10.1364/AO.461861
- 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
- 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
19 citations as recorded by crossref.
- Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt M. Elshora 10.1007/s10661-023-11118-8
- 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
- Bayesian Aerosol Retrieval-Based PM2.5 Estimation through Hierarchical Gaussian Process Models J. Zhang et al. 10.3390/math10162878
- Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf M. Pashayi et al. 10.1016/j.apr.2023.101948
- Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI F. Yin et al. 10.5194/gmd-15-7933-2022
- 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
- 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
- A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
- 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
- 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
- Design of a Computer-Based Legal Information Retrieval System F. Wu et al. 10.1155/2022/6942773
- Satellite-based evaluation of AeroCom model bias in biomass burning regions Q. Zhong et al. 10.5194/acp-22-11009-2022
- 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
- Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification A. Ibrahim et al. 10.1364/AO.461861
- 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
Latest update: 29 Jun 2024
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...
