Articles | Volume 7, issue 8
https://doi.org/10.5194/amt-7-2411-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/amt-7-2411-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
08 Aug 2014
Research article |
| 08 Aug 2014
Retrieval of aerosol optical depth over land surfaces from AVHRR data
L. L. Mei
The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
University of the Chinese Academy of Sciences, Beijing 100039, China
The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Faculty of Life Sciences and Computing, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, UK
A. A. Kokhanovsky
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
W. von Hoyningen-Huene
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
G. de Leeuw
Department of Physics, University of Helsinki, Helsinki, Finland
Finnish Meteorological Institute, Climate Change Unit, Helsinki, Finland
J. P. Burrows
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
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Cited
30 citations as recorded by crossref.
- SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. https://doi.org/10.3390/rs9030253
- Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. https://doi.org/10.3390/rs12060978
- Retrieval and application of leaf area index over China using HJ-1 data X. Zhao et al. https://doi.org/10.1080/19475705.2016.1238854
- Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method X. Tian et al. https://doi.org/10.1109/TGRS.2021.3129853
- A new generation aerosol optical depth dataset based on AVHRR data over China from 1981 to 2000 Y. Che et al. https://doi.org/10.1016/j.rse.2025.114899
- Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China Y. Che et al. https://doi.org/10.1016/j.isprsjprs.2018.09.004
- A Long-Term Historical Aerosol Optical Depth Data Record (1982–2011) Over China From AVHRR L. Gao et al. https://doi.org/10.1109/TGRS.2018.2873944
- Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces Y. Wang et al. https://doi.org/10.1016/j.atmosenv.2018.12.023
- Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models C. Samuel et al. https://doi.org/10.1002/gj.4780
- Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review F. Mucomole et al. https://doi.org/10.3390/en18061460
- Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS) S. Sim et al. https://doi.org/10.5194/amt-17-5601-2024
- Aerosol Retrieval Study from a Particulate Observing Scanning Polarimeter Onboard Gao-Fen 5B without Prior Surface Knowledge, Based on the Optimal Estimation Method Y. Fan et al. https://doi.org/10.3390/rs15020385
- Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results L. Mei et al. https://doi.org/10.1016/j.rse.2016.11.015
- Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements A. Sayer et al. https://doi.org/10.1002/2017JD026934
- A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. https://doi.org/10.1007/s13143-020-00215-0
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. https://doi.org/10.1080/10643389.2019.1665944
- A Critical Evaluation of Deep Blue Algorithm Derived AVHRR Aerosol Product Over China L. Mei et al. https://doi.org/10.1029/2018JD029929
- Review of surface particulate monitoring of dust events using geostationary satellite remote sensing M. Sowden et al. https://doi.org/10.1016/j.atmosenv.2018.04.020
- Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8 Y. Xie et al. https://doi.org/10.1109/TGRS.2019.2944949
- On the retrieval of aerosol optical depth over cryosphere using passive remote sensing L. Mei et al. https://doi.org/10.1016/j.rse.2020.111731
- An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data R. Li et al. https://doi.org/10.1007/s12524-020-01301-6
- Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval X. Yan et al. https://doi.org/10.1016/j.atmosenv.2020.117362
- Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm Y. Chen et al. https://doi.org/10.3390/rs14132967
- Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe Y. Xue et al. https://doi.org/10.1016/j.rse.2017.06.036
- Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012 Y. Che et al. https://doi.org/10.5194/amt-12-4091-2019
- A comparison study between AOD data from MODIS deep blue collections 51 and 06 and from AERONET over Saudi Arabia M. Almazroui https://doi.org/10.1016/j.atmosres.2019.03.040
- Spatiotemporal Investigations of Aerosol Optical Properties Over Bangladesh for the Period 2002–2016 M. Islam et al. https://doi.org/10.1007/s41748-019-00120-1
- Aerosol remote sensing in polar regions C. Tomasi et al. https://doi.org/10.1016/j.earscirev.2014.11.001
- A review of extreme air pollution measurement and modeling techniques with applications K. Rautela et al. https://doi.org/10.1016/j.rser.2026.117178
- A Dual-Channel Aerosol Optical Depth Retrieval Algorithm Incorporating the BRDF Effect from AVHRR over Eastern Asia Y. Wang et al. https://doi.org/10.3390/rs13030365
30 citations as recorded by crossref.
- SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. https://doi.org/10.3390/rs9030253
- Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. https://doi.org/10.3390/rs12060978
- Retrieval and application of leaf area index over China using HJ-1 data X. Zhao et al. https://doi.org/10.1080/19475705.2016.1238854
- Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method X. Tian et al. https://doi.org/10.1109/TGRS.2021.3129853
- A new generation aerosol optical depth dataset based on AVHRR data over China from 1981 to 2000 Y. Che et al. https://doi.org/10.1016/j.rse.2025.114899
- Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China Y. Che et al. https://doi.org/10.1016/j.isprsjprs.2018.09.004
- A Long-Term Historical Aerosol Optical Depth Data Record (1982–2011) Over China From AVHRR L. Gao et al. https://doi.org/10.1109/TGRS.2018.2873944
- Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces Y. Wang et al. https://doi.org/10.1016/j.atmosenv.2018.12.023
- Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models C. Samuel et al. https://doi.org/10.1002/gj.4780
- Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review F. Mucomole et al. https://doi.org/10.3390/en18061460
- Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS) S. Sim et al. https://doi.org/10.5194/amt-17-5601-2024
- Aerosol Retrieval Study from a Particulate Observing Scanning Polarimeter Onboard Gao-Fen 5B without Prior Surface Knowledge, Based on the Optimal Estimation Method Y. Fan et al. https://doi.org/10.3390/rs15020385
- Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results L. Mei et al. https://doi.org/10.1016/j.rse.2016.11.015
- Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements A. Sayer et al. https://doi.org/10.1002/2017JD026934
- A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. https://doi.org/10.1007/s13143-020-00215-0
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. https://doi.org/10.1080/10643389.2019.1665944
- A Critical Evaluation of Deep Blue Algorithm Derived AVHRR Aerosol Product Over China L. Mei et al. https://doi.org/10.1029/2018JD029929
- Review of surface particulate monitoring of dust events using geostationary satellite remote sensing M. Sowden et al. https://doi.org/10.1016/j.atmosenv.2018.04.020
- Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8 Y. Xie et al. https://doi.org/10.1109/TGRS.2019.2944949
- On the retrieval of aerosol optical depth over cryosphere using passive remote sensing L. Mei et al. https://doi.org/10.1016/j.rse.2020.111731
- An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data R. Li et al. https://doi.org/10.1007/s12524-020-01301-6
- Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval X. Yan et al. https://doi.org/10.1016/j.atmosenv.2020.117362
- Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm Y. Chen et al. https://doi.org/10.3390/rs14132967
- Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe Y. Xue et al. https://doi.org/10.1016/j.rse.2017.06.036
- Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012 Y. Che et al. https://doi.org/10.5194/amt-12-4091-2019
- A comparison study between AOD data from MODIS deep blue collections 51 and 06 and from AERONET over Saudi Arabia M. Almazroui https://doi.org/10.1016/j.atmosres.2019.03.040
- Spatiotemporal Investigations of Aerosol Optical Properties Over Bangladesh for the Period 2002–2016 M. Islam et al. https://doi.org/10.1007/s41748-019-00120-1
- Aerosol remote sensing in polar regions C. Tomasi et al. https://doi.org/10.1016/j.earscirev.2014.11.001
- A review of extreme air pollution measurement and modeling techniques with applications K. Rautela et al. https://doi.org/10.1016/j.rser.2026.117178
- A Dual-Channel Aerosol Optical Depth Retrieval Algorithm Incorporating the BRDF Effect from AVHRR over Eastern Asia Y. Wang et al. https://doi.org/10.3390/rs13030365
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