Articles | Volume 12, issue 20
https://doi.org/10.5194/acp-12-9679-2012
© Author(s) 2012. 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/acp-12-9679-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Oct 2012
Research article |
| 24 Oct 2012
Discrimination of biomass burning smoke and clouds in MAIAC algorithm
A. Lyapustin
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
S. Korkin
Universities Space Research Association, Columbia, Maryland, USA
Y. Wang
University of Maryland Baltimore County, Baltimore, Maryland, USA
B. Quayle
USDA Forest Service, Salt Lake City, Utah, USA
I. Laszlo
NOAA/NESDIS/STAR, Camp Springs, Maryland, USA
Viewed
Total article views: 3,847 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 31 Jul 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,138 | 1,561 | 148 | 3,847 | 133 | 102 |
- HTML: 2,138
- PDF: 1,561
- XML: 148
- Total: 3,847
- BibTeX: 133
- EndNote: 102
Total article views: 3,162 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 24 Oct 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,792 | 1,238 | 132 | 3,162 | 117 | 97 |
- HTML: 1,792
- PDF: 1,238
- XML: 132
- Total: 3,162
- BibTeX: 117
- EndNote: 97
Total article views: 685 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 31 Jul 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 346 | 323 | 16 | 685 | 16 | 5 |
- HTML: 346
- PDF: 323
- XML: 16
- Total: 685
- BibTeX: 16
- EndNote: 5
Cited
41 citations as recorded by crossref.
- Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data H. Nguyen & M. Wooster 10.1016/j.rse.2020.111971
- Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia A. Mhawish et al. 10.1016/j.rse.2019.01.033
- Satellite-based estimation of the impacts of summertime wildfires on PM<sub>2.5</sub> concentration in the United States Z. Xue et al. 10.5194/acp-21-11243-2021
- MAIAC Thermal Technique for Smoke Injection Height From MODIS A. Lyapustin et al. 10.1109/LGRS.2019.2936332
- MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
- Uncovering the dynamics of atmospheric aerosols in China: A comprehensive analysis of OMI-Retrieved aerosol index data M. Khan & S. Tariq 10.1016/j.jastp.2024.106251
- Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. 10.1029/2018JD028313
- The (mis)identification of high-latitude dust events using remote sensing methods in the Yukon, Canada: a sub-daily variability analysis R. Huck et al. 10.5194/acp-23-6299-2023
- Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts P. Saide et al. 10.5194/acp-13-10425-2013
- The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis I. Rogozovsky et al. 10.1016/j.atmosenv.2023.119930
- Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover J. Daniels et al. 10.1016/j.scitotenv.2024.171122
- Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. 10.5194/amt-13-877-2020
- Assessment of air pollution in Krasnoyarsk based on satellite data of different spatial resolution K. Krasnoshchekov & O. Yakubailik 10.1088/1757-899X/537/6/062083
- Observed aerosol-induced radiative effect on plant productivity in the eastern United States S. Strada et al. 10.1016/j.atmosenv.2015.09.051
- A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data L. Mei et al. 10.1016/j.rse.2016.11.016
- The Relationship Between MAIAC Smoke Plume Heights and Surface PM M. Cheeseman et al. 10.1029/2020GL088949
- Wildfire Smoke Cools Summer River and Stream Water Temperatures A. David et al. 10.1029/2018WR022964
- Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine A. Saim & M. Aly 10.1016/j.apr.2024.102226
- Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke L. Li et al. 10.1016/j.envint.2020.106143
- Satellite Observations of Cloud-Related Variations in Aerosol Properties T. Várnai & A. Marshak 10.3390/atmos9110430
- Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India H. Jethva et al. 10.1038/s41598-019-52799-x
- Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals A. Chudnovsky et al. 10.1016/j.atmosenv.2014.02.019
- Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. 10.5194/amt-12-4291-2019
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
- Satellite‐Based Daily PM2.5 Estimates During Fire Seasons in Colorado G. Geng et al. 10.1029/2018JD028573
- A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations W. Wang et al. 10.3390/rs14040964
- MODIS-based smoke detection shows that daily smoke cover dampens fire severity in initial burns but not reburns in complex terrain L. Harris & A. Taylor 10.1071/WF22061
- A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006–2020) R. Aguilera et al. 10.1016/j.envint.2022.107719
- Characterization of forest fire smoke event near Washington, DC in summer 2013 with multi-wavelength lidar I. Veselovskii et al. 10.5194/acp-15-1647-2015
- MODIS Collection 6 MAIAC algorithm A. Lyapustin et al. 10.5194/amt-11-5741-2018
- Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes V. Martins et al. 10.3390/rs9040322
- Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning O. Tomshin & V. Solovyev 10.3390/rs14194980
- A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions A. Chudnovsky et al. 10.5194/acp-13-10907-2013
- A Novel Ensemble-Based Statistical Approach to Estimate Daily Wildfire-Specific Pm2.5 in California (2006-2020) R. Aguilera et al. 10.2139/ssrn.4177030
- Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. 10.1029/2022JD037298
- Aerosol optical depth over Northeastern Brazil: A multi-platform intercomparison study G. Münchow et al. 10.1016/j.atmosres.2024.107864
- Forest Fire Smoke Detection Research Based on the Random Forest Algorithm and Sub-Pixel Mapping Method X. Li et al. 10.3390/f14030485
- Spatiotemporal variations and long term trends analysis of aerosol optical depth over the United Arab Emirates A. Abuelgasim et al. 10.1016/j.rsase.2021.100532
- Understanding the spatiotemporal distribution of aerosols and their association with natural and anthropogenic factors over Saudi Arabia using multi-sensor remote sensing data M. Khan et al. 10.1007/s11869-024-01578-3
- First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI S. Li et al. 10.3390/rs11242990
- Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model X. Hu et al. 10.1016/j.rse.2013.08.032
40 citations as recorded by crossref.
- Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data H. Nguyen & M. Wooster 10.1016/j.rse.2020.111971
- Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia A. Mhawish et al. 10.1016/j.rse.2019.01.033
- Satellite-based estimation of the impacts of summertime wildfires on PM<sub>2.5</sub> concentration in the United States Z. Xue et al. 10.5194/acp-21-11243-2021
- MAIAC Thermal Technique for Smoke Injection Height From MODIS A. Lyapustin et al. 10.1109/LGRS.2019.2936332
- MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
- Uncovering the dynamics of atmospheric aerosols in China: A comprehensive analysis of OMI-Retrieved aerosol index data M. Khan & S. Tariq 10.1016/j.jastp.2024.106251
- Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. 10.1029/2018JD028313
- The (mis)identification of high-latitude dust events using remote sensing methods in the Yukon, Canada: a sub-daily variability analysis R. Huck et al. 10.5194/acp-23-6299-2023
- Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts P. Saide et al. 10.5194/acp-13-10425-2013
- The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis I. Rogozovsky et al. 10.1016/j.atmosenv.2023.119930
- Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover J. Daniels et al. 10.1016/j.scitotenv.2024.171122
- Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. 10.5194/amt-13-877-2020
- Assessment of air pollution in Krasnoyarsk based on satellite data of different spatial resolution K. Krasnoshchekov & O. Yakubailik 10.1088/1757-899X/537/6/062083
- Observed aerosol-induced radiative effect on plant productivity in the eastern United States S. Strada et al. 10.1016/j.atmosenv.2015.09.051
- A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data L. Mei et al. 10.1016/j.rse.2016.11.016
- The Relationship Between MAIAC Smoke Plume Heights and Surface PM M. Cheeseman et al. 10.1029/2020GL088949
- Wildfire Smoke Cools Summer River and Stream Water Temperatures A. David et al. 10.1029/2018WR022964
- Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine A. Saim & M. Aly 10.1016/j.apr.2024.102226
- Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke L. Li et al. 10.1016/j.envint.2020.106143
- Satellite Observations of Cloud-Related Variations in Aerosol Properties T. Várnai & A. Marshak 10.3390/atmos9110430
- Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India H. Jethva et al. 10.1038/s41598-019-52799-x
- Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals A. Chudnovsky et al. 10.1016/j.atmosenv.2014.02.019
- Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. 10.5194/amt-12-4291-2019
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
- Satellite‐Based Daily PM2.5 Estimates During Fire Seasons in Colorado G. Geng et al. 10.1029/2018JD028573
- A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations W. Wang et al. 10.3390/rs14040964
- MODIS-based smoke detection shows that daily smoke cover dampens fire severity in initial burns but not reburns in complex terrain L. Harris & A. Taylor 10.1071/WF22061
- A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006–2020) R. Aguilera et al. 10.1016/j.envint.2022.107719
- Characterization of forest fire smoke event near Washington, DC in summer 2013 with multi-wavelength lidar I. Veselovskii et al. 10.5194/acp-15-1647-2015
- MODIS Collection 6 MAIAC algorithm A. Lyapustin et al. 10.5194/amt-11-5741-2018
- Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes V. Martins et al. 10.3390/rs9040322
- Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning O. Tomshin & V. Solovyev 10.3390/rs14194980
- A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions A. Chudnovsky et al. 10.5194/acp-13-10907-2013
- A Novel Ensemble-Based Statistical Approach to Estimate Daily Wildfire-Specific Pm2.5 in California (2006-2020) R. Aguilera et al. 10.2139/ssrn.4177030
- Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. 10.1029/2022JD037298
- Aerosol optical depth over Northeastern Brazil: A multi-platform intercomparison study G. Münchow et al. 10.1016/j.atmosres.2024.107864
- Forest Fire Smoke Detection Research Based on the Random Forest Algorithm and Sub-Pixel Mapping Method X. Li et al. 10.3390/f14030485
- Spatiotemporal variations and long term trends analysis of aerosol optical depth over the United Arab Emirates A. Abuelgasim et al. 10.1016/j.rsase.2021.100532
- Understanding the spatiotemporal distribution of aerosols and their association with natural and anthropogenic factors over Saudi Arabia using multi-sensor remote sensing data M. Khan et al. 10.1007/s11869-024-01578-3
- First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI S. Li et al. 10.3390/rs11242990
Saved (final revised paper)
Saved (final revised paper)
Latest update: 13 Dec 2024
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(2102 KB) - Metadata XML
Altmetrics
Final-revised paper
Preprint