Articles | Volume 11, issue 23
https://doi.org/10.5194/acp-11-11977-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-11-11977-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
02 Dec 2011
Research article |
| 02 Dec 2011
A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
H. Zhang
Joint Center for Earth Systems Technology (JCET), University of Maryland Baltimore County, Suite 320, 5523 Research Park Drive, Baltimore, MD 21228, USA
A. Lyapustin
Goddard Earth Sciences and Technology Center (GEST), University of Maryland Baltimore County, Suite 320, 5523 Research Park Drive, Baltimore, MD 21228, USA
Y. Wang
Goddard Earth Sciences and Technology Center (GEST), University of Maryland Baltimore County, Suite 320, 5523 Research Park Drive, Baltimore, MD 21228, USA
S. Kondragunta
NOAA/NESDIS/STAR, 5825 University Research Ct, College Park, MD 20740, USA
I. Laszlo
NOAA/NESDIS/STAR, 5825 University Research Ct, College Park, MD 20740, USA
P. Ciren
PSGS/Dell, 5825 University Research Ct, College Park, MD 20740, USA
R. M. Hoff
Joint Center for Earth Systems Technology (JCET), University of Maryland Baltimore County, Suite 320, 5523 Research Park Drive, Baltimore, MD 21228, USA
Goddard Earth Sciences and Technology Center (GEST), University of Maryland Baltimore County, Suite 320, 5523 Research Park Drive, Baltimore, MD 21228, USA
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35 citations as recorded by crossref.
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- Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations M. Zhou et al. 10.1016/j.rse.2021.112717
- Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS) M. Kim et al. 10.5194/acp-16-1789-2016
- Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review F. Mucomole et al. 10.3390/en18061460
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- Spectral and diurnal temporal suitability of GOES Advanced Baseline Imager (ABI) reflectance for burned area mapping D. Roy et al. 10.1016/j.jag.2020.102271
- Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies D. Lary et al. 10.4137/EHI.S15664
- Assessment of Aerosol optical depth under background and polluted conditions using AERONET and VIIRS datasets M. Kim et al. 10.1016/j.atmosenv.2020.117994
- Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data L. She et al. 10.1109/TGRS.2018.2867000
- Fengyun 4A Land Aerosol Retrieval: Algorithm Development, Validation, and Comparison With Other Datasets X. Su et al. 10.1109/TGRS.2023.3330544
- Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. 10.1109/TGRS.2021.3107542
- Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand P. Wongnakae et al. 10.1007/s11356-023-28698-0
- A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing Z. Zhang et al. 10.1016/j.atmosenv.2018.11.023
- Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region J. Zhou et al. 10.3390/rs15082172
- Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States H. Zhang et al. 10.5194/amt-6-471-2013
- Machine Learning Approach To Estimate Hourly Exposure to Fine Particulate Matter for Urban, Rural, and Remote Populations during Wildfire Seasons J. Yao et al. 10.1021/acs.est.8b01921
- Consistent retrieval of multiple parameters from GOES-R top of atmosphere reflectance data H. Xiong et al. 10.1080/01431161.2020.1766151
- GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison Y. Fan et al. 10.1016/j.atmosenv.2023.119965
- Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network W. Qin et al. 10.3390/rs10071022
- A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land G. Zhong et al. 10.3390/rs9060524
- Global observations of aerosol-cloud-precipitation-climate interactions D. Rosenfeld et al. 10.1002/2013RG000441
- Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California J. Su et al. 10.1016/j.envint.2024.109100
- Holistics 3.0 for Health D. Lary et al. 10.3390/ijgi3031023
- 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. 10.1109/TGRS.2019.2944949
- Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. 10.3390/rs10010137
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- Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization C. Jin et al. 10.3390/rs13224689
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
- Utilization of O4 slant column density to derive aerosol layer height from a space-borne UV–visible hyperspectral sensor: sensitivity and case study S. Park et al. 10.5194/acp-16-1987-2016
- Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land W. Zhang et al. 10.3390/rs11091108
- Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo X. Ceamanos et al. 10.3390/rs11242958
- Improvement of aerosol optical depth retrieval over Hong Kong from a geostationary meteorological satellite using critical reflectance with background optical depth correction M. Kim et al. 10.1016/j.rse.2013.12.003
- Assessment of long-term trends in chlorophyll-a and sea surface temperature in the Arabian Sea and their association with aerosols using remote sensing Y. Sun et al. 10.1016/j.ocecoaman.2023.106716
- Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application Y. Fan et al. 10.1080/01431161.2024.2398821
- Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment D. Lesser et al. 10.3390/s21010063
34 citations as recorded by crossref.
- An Improved Aerosol Retrieval Algorithm for FY-4A/AGRI Data Based on the GRASP Framework H. Wang et al. 10.1109/TGRS.2025.3546614
- Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations M. Zhou et al. 10.1016/j.rse.2021.112717
- Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS) M. Kim et al. 10.5194/acp-16-1789-2016
- Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review F. Mucomole et al. 10.3390/en18061460
- Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa C. Malings et al. 10.5194/amt-13-3873-2020
- Spectral and diurnal temporal suitability of GOES Advanced Baseline Imager (ABI) reflectance for burned area mapping D. Roy et al. 10.1016/j.jag.2020.102271
- Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies D. Lary et al. 10.4137/EHI.S15664
- Assessment of Aerosol optical depth under background and polluted conditions using AERONET and VIIRS datasets M. Kim et al. 10.1016/j.atmosenv.2020.117994
- Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data L. She et al. 10.1109/TGRS.2018.2867000
- Fengyun 4A Land Aerosol Retrieval: Algorithm Development, Validation, and Comparison With Other Datasets X. Su et al. 10.1109/TGRS.2023.3330544
- Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. 10.1109/TGRS.2021.3107542
- Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand P. Wongnakae et al. 10.1007/s11356-023-28698-0
- A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing Z. Zhang et al. 10.1016/j.atmosenv.2018.11.023
- Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region J. Zhou et al. 10.3390/rs15082172
- Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States H. Zhang et al. 10.5194/amt-6-471-2013
- Machine Learning Approach To Estimate Hourly Exposure to Fine Particulate Matter for Urban, Rural, and Remote Populations during Wildfire Seasons J. Yao et al. 10.1021/acs.est.8b01921
- Consistent retrieval of multiple parameters from GOES-R top of atmosphere reflectance data H. Xiong et al. 10.1080/01431161.2020.1766151
- GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison Y. Fan et al. 10.1016/j.atmosenv.2023.119965
- Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network W. Qin et al. 10.3390/rs10071022
- A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land G. Zhong et al. 10.3390/rs9060524
- Global observations of aerosol-cloud-precipitation-climate interactions D. Rosenfeld et al. 10.1002/2013RG000441
- Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California J. Su et al. 10.1016/j.envint.2024.109100
- Holistics 3.0 for Health D. Lary et al. 10.3390/ijgi3031023
- 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. 10.1109/TGRS.2019.2944949
- Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. 10.3390/rs10010137
- GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign M. Choi et al. 10.5194/amt-9-1377-2016
- Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization C. Jin et al. 10.3390/rs13224689
- Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
- Utilization of O4 slant column density to derive aerosol layer height from a space-borne UV–visible hyperspectral sensor: sensitivity and case study S. Park et al. 10.5194/acp-16-1987-2016
- Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land W. Zhang et al. 10.3390/rs11091108
- Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo X. Ceamanos et al. 10.3390/rs11242958
- Improvement of aerosol optical depth retrieval over Hong Kong from a geostationary meteorological satellite using critical reflectance with background optical depth correction M. Kim et al. 10.1016/j.rse.2013.12.003
- Assessment of long-term trends in chlorophyll-a and sea surface temperature in the Arabian Sea and their association with aerosols using remote sensing Y. Sun et al. 10.1016/j.ocecoaman.2023.106716
- Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application Y. Fan et al. 10.1080/01431161.2024.2398821
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