Articles | Volume 7, issue 9
https://doi.org/10.5194/amt-7-3151-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-7-3151-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Sep 2014
Research article |
| 26 Sep 2014
Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak
M. Taylor
Institute for Environmental Research and Sustainable Development (IERSD), National Observatory of Athens (NOA), Metaxa & Vas. Pavlou, Penteli, 15236, Athens, Greece
S. Kazadzis
Institute for Environmental Research and Sustainable Development (IERSD), National Observatory of Athens (NOA), Metaxa & Vas. Pavlou, Penteli, 15236, Athens, Greece
A. Tsekeri
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS), National Observatory of Athens (NOA), Metaxa & Vas. Pavlou, Penteli, 15236, Athens, Greece
A. Gkikas
Laboratory of Meteorology, Physics Department, University of Ioannina, Greece
V. Amiridis
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS), National Observatory of Athens (NOA), Metaxa & Vas. Pavlou, Penteli, 15236, Athens, Greece
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Cited
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- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. https://doi.org/10.1016/j.rse.2020.111716
- Analysis of aerosol absorption properties and transport over North Africa and the Middle East using AERONET data A. Farahat et al. https://doi.org/10.5194/angeo-34-1031-2016
- Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt P. Kosmopoulos et al. https://doi.org/10.3390/rs10121870
- A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements L. Clarisse et al. https://doi.org/10.1029/2018JD029701
- Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis M. Zempila et al. https://doi.org/10.1016/j.jqsrt.2016.06.003
- Neural network radiative transfer solvers for the generation of high resolution solar irradiance spectra parameterized by cloud and aerosol parameters M. Taylor et al. https://doi.org/10.1016/j.jqsrt.2015.08.018
- Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table J. Huttunen et al. https://doi.org/10.5194/acp-16-8181-2016
- Validation of OMI erythemal doses with multi-sensor ground-based measurements in Thessaloniki, Greece M. Zempila et al. https://doi.org/10.1016/j.atmosenv.2018.04.012
- A neural network aerosol-typing algorithm based on lidar data D. Nicolae et al. https://doi.org/10.5194/acp-18-14511-2018
- TEMIS UV product validation using NILU-UV ground-based measurements in Thessaloniki, Greece M. Zempila et al. https://doi.org/10.5194/acp-17-7157-2017
- A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119098
- Utilizing deep learning techniques in operational system of geo-KOMPSAT-2A satellite for estimating solar radiation and aerosol optical depth J. Ha et al. https://doi.org/10.1080/15481603.2026.2658318
- The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. https://doi.org/10.1016/j.rse.2022.113426
- Retrieval of aerosol extinction coefficients for spaceborne high-spectral-resolution lidar based on neural networks L. Zeng et al. https://doi.org/10.3788/COL202624.090101
- Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data L. She et al. https://doi.org/10.1016/j.rse.2024.114115
- Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data X. Li et al. https://doi.org/10.3390/rs70404473
- NILU-UV multi-filter radiometer total ozone columns: Comparison with satellite observations over Thessaloniki, Greece M. Zempila et al. https://doi.org/10.1016/j.scitotenv.2017.02.174
- A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning H. Wang et al. https://doi.org/10.1016/j.rse.2021.112665
- Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter A. Di Noia et al. https://doi.org/10.5194/amt-10-4235-2017
- Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements P. Kosmopoulos et al. https://doi.org/10.5194/amt-11-907-2018
- Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China N. Luo et al. https://doi.org/10.3390/atmos15050564
- An ensemble machine learning method to retrieve aerosol parameters from ground-based Sun-sky photometer measurements Q. Li et al. https://doi.org/10.5194/amt-19-2507-2026
- Air pollution in the Arabian Peninsula (Saudi Arabia, the United Arab Emirates, Kuwait, Qatar, Bahrain, and Oman): causes, effects, and aerosol categorization A. Farahat https://doi.org/10.1007/s12517-015-2203-y
23 citations as recorded by crossref.
- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. https://doi.org/10.1016/j.rse.2020.111716
- Analysis of aerosol absorption properties and transport over North Africa and the Middle East using AERONET data A. Farahat et al. https://doi.org/10.5194/angeo-34-1031-2016
- Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt P. Kosmopoulos et al. https://doi.org/10.3390/rs10121870
- A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements L. Clarisse et al. https://doi.org/10.1029/2018JD029701
- Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis M. Zempila et al. https://doi.org/10.1016/j.jqsrt.2016.06.003
- Neural network radiative transfer solvers for the generation of high resolution solar irradiance spectra parameterized by cloud and aerosol parameters M. Taylor et al. https://doi.org/10.1016/j.jqsrt.2015.08.018
- Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table J. Huttunen et al. https://doi.org/10.5194/acp-16-8181-2016
- Validation of OMI erythemal doses with multi-sensor ground-based measurements in Thessaloniki, Greece M. Zempila et al. https://doi.org/10.1016/j.atmosenv.2018.04.012
- A neural network aerosol-typing algorithm based on lidar data D. Nicolae et al. https://doi.org/10.5194/acp-18-14511-2018
- TEMIS UV product validation using NILU-UV ground-based measurements in Thessaloniki, Greece M. Zempila et al. https://doi.org/10.5194/acp-17-7157-2017
- A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119098
- Utilizing deep learning techniques in operational system of geo-KOMPSAT-2A satellite for estimating solar radiation and aerosol optical depth J. Ha et al. https://doi.org/10.1080/15481603.2026.2658318
- The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. https://doi.org/10.1016/j.rse.2022.113426
- Retrieval of aerosol extinction coefficients for spaceborne high-spectral-resolution lidar based on neural networks L. Zeng et al. https://doi.org/10.3788/COL202624.090101
- Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data L. She et al. https://doi.org/10.1016/j.rse.2024.114115
- Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data X. Li et al. https://doi.org/10.3390/rs70404473
- NILU-UV multi-filter radiometer total ozone columns: Comparison with satellite observations over Thessaloniki, Greece M. Zempila et al. https://doi.org/10.1016/j.scitotenv.2017.02.174
- A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning H. Wang et al. https://doi.org/10.1016/j.rse.2021.112665
- Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter A. Di Noia et al. https://doi.org/10.5194/amt-10-4235-2017
- Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements P. Kosmopoulos et al. https://doi.org/10.5194/amt-11-907-2018
- Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China N. Luo et al. https://doi.org/10.3390/atmos15050564
- An ensemble machine learning method to retrieve aerosol parameters from ground-based Sun-sky photometer measurements Q. Li et al. https://doi.org/10.5194/amt-19-2507-2026
- Air pollution in the Arabian Peninsula (Saudi Arabia, the United Arab Emirates, Kuwait, Qatar, Bahrain, and Oman): causes, effects, and aerosol categorization A. Farahat https://doi.org/10.1007/s12517-015-2203-y
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