Articles | Volume 9, issue 11
https://doi.org/10.5194/amt-9-5575-2016
© Author(s) 2016. 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-9-5575-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Nov 2016
Research article |
| 23 Nov 2016
Improved MODIS Dark Target aerosol optical depth algorithm over land: angular effect correction
Yerong Wu
CORRESPONDING AUTHOR
Department of Geoscience and Remote Sensing (GRS), Delft University of
Technology (TUDelft), Stevinweg 1, 2628 CN Delft, the Netherlands
Martin de Graaf
Department of Geoscience and Remote Sensing (GRS), Delft University of
Technology (TUDelft), Stevinweg 1, 2628 CN Delft, the Netherlands
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg
297, 3731 GA De Bilt, the Netherlands
Massimo Menenti
Department of Geoscience and Remote Sensing (GRS), Delft University of
Technology (TUDelft), Stevinweg 1, 2628 CN Delft, the Netherlands
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Cited
15 citations as recorded by crossref.
- 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
- High-resolution urban air quality monitoring using sentinel satellite images and low-cost ground-based sensor networks J. Gitahi et al. 10.1051/e3sconf/202017102002
- MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach M. Pashayi et al. 10.1016/j.apr.2024.102128
- Can explicit convection improve modelled dust in summertime West Africa? A. Roberts et al. 10.5194/acp-18-9025-2018
- Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf M. Pashayi et al. 10.1016/j.atmosenv.2022.119410
- A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. 10.5194/amt-13-2257-2020
- Assessment of aerosol burden over Ghana K. Fosu-Amankwah et al. 10.1016/j.sciaf.2021.e00971
- Particle Size Analysis of African Dust Haze over the Last 20 Years: A Focus on the Extreme Event of June 2020 L. Euphrasie-Clotilde et al. 10.3390/atmos12040502
- Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land S. Jin et al. 10.1109/TGRS.2020.3021021
- A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8 X. Yan et al. 10.1016/j.atmosres.2018.02.021
- Influence of industrial sustainability transition on air quality in a typical resource-exhausted city J. Wang & X. Li 10.1016/j.heliyon.2024.e25138
- The Response of the Aerosol Distribution to Monsoon Intensity Over the Summer Monsoon Transition Zone B. Bai et al. 10.3389/feart.2019.00356
- SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. 10.3390/rs9030253
- High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique L. Xiao et al. 10.1016/j.atmosenv.2017.10.062
- The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data Y. Wu et al. 10.3390/rs8090765
14 citations as recorded by crossref.
- 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
- High-resolution urban air quality monitoring using sentinel satellite images and low-cost ground-based sensor networks J. Gitahi et al. 10.1051/e3sconf/202017102002
- MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach M. Pashayi et al. 10.1016/j.apr.2024.102128
- Can explicit convection improve modelled dust in summertime West Africa? A. Roberts et al. 10.5194/acp-18-9025-2018
- Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf M. Pashayi et al. 10.1016/j.atmosenv.2022.119410
- A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. 10.5194/amt-13-2257-2020
- Assessment of aerosol burden over Ghana K. Fosu-Amankwah et al. 10.1016/j.sciaf.2021.e00971
- Particle Size Analysis of African Dust Haze over the Last 20 Years: A Focus on the Extreme Event of June 2020 L. Euphrasie-Clotilde et al. 10.3390/atmos12040502
- Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land S. Jin et al. 10.1109/TGRS.2020.3021021
- A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8 X. Yan et al. 10.1016/j.atmosres.2018.02.021
- Influence of industrial sustainability transition on air quality in a typical resource-exhausted city J. Wang & X. Li 10.1016/j.heliyon.2024.e25138
- The Response of the Aerosol Distribution to Monsoon Intensity Over the Summer Monsoon Transition Zone B. Bai et al. 10.3389/feart.2019.00356
- SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. 10.3390/rs9030253
- High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique L. Xiao et al. 10.1016/j.atmosenv.2017.10.062
1 citations as recorded by crossref.
Latest update: 19 Apr 2024
Short summary
In this paper, we developed a new algorithm to improve the retrieval of aerosol optical depth (AOD) over land with satellite measurement by refining the shape of the spectral surface reflectance to improve the estimated background reflectance in the channel used for AOD retrieval. The results show that the angular effects of the retrievals are largely reduced, including fewer occurrences of negative retrievals. This implies that the users can get more accurate data without angular bias.
In this paper, we developed a new algorithm to improve the retrieval of aerosol optical depth...
