Articles | Volume 19, issue 12
https://doi.org/10.5194/acp-19-8243-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-8243-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China
Ning Liu
School of Geosciences and Info-Physics, Central South University,
Changsha, 410083, China
Bin Zou
CORRESPONDING AUTHOR
School of Geosciences and Info-Physics, Central South University,
Changsha, 410083, China
Key Laboratory of Metallogenic Prediction
of Nonferrous Metals and Geological Environment Monitoring (Central South
University), Ministry of Education, Changsha, 410083, China
Huihui Feng
School of Geosciences and Info-Physics, Central South University,
Changsha, 410083, China
School of Geosciences and Info-Physics, Central South University,
Changsha, 410083, China
Yuqi Tang
School of Geosciences and Info-Physics, Central South University,
Changsha, 410083, China
Yu Liang
School of Geosciences and Info-Physics, Central South University,
Changsha, 410083, China
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- Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
- Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020 Q. Zeng et al. 10.3390/rs14030623
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- Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications J. Wei et al. 10.1016/j.rse.2020.112136
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- A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation T. Li et al. 10.1109/JSTARS.2020.2977668
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27 citations as recorded by crossref.
- Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis X. Liu et al. 10.1016/j.jclepro.2020.121709
- Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 Q. He et al. 10.1016/j.envint.2021.106726
- The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China J. Wei et al. 10.1016/j.envint.2020.106290
- Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies J. Wu et al. 10.1016/j.envpol.2021.117138
- Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
- Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020 Q. Zeng et al. 10.3390/rs14030623
- VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China L. He et al. 10.1016/j.atmosenv.2021.118265
- An episode of transboundary air pollution in the central Himalayas during agricultural residue burning season in North India S. Khanal et al. 10.1016/j.apr.2021.101270
- Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data D. Lu et al. 10.3390/rs13081423
- Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea P. Ettehadi Osgouei et al. 10.1016/j.atmosenv.2021.118784
- Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications J. Wei et al. 10.1016/j.rse.2020.112136
- Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017 S. Li et al. 10.1016/j.scitotenv.2019.135755
- Improved 1 km resolution PM<sub>2.5</sub> estimates across China using enhanced space–time extremely randomized trees J. Wei et al. 10.5194/acp-20-3273-2020
- Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
- Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: Evaluation, comparison, and merging Y. Wang et al. 10.1016/j.atmosenv.2020.117548
- Should There Be Industrial Agglomeration in Sustainable Cities?: A Perspective Based on Haze Pollution P. Dai & Y. Lin 10.3390/su13126609
- A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation T. Li et al. 10.1109/JSTARS.2020.2977668
- Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8 T. Su et al. 10.1016/j.rse.2020.112093
- Validation of the aerosol optical property products derived by the GRASP/Component approach from multi-angular polarimetric observations X. Zhang et al. 10.1016/j.atmosres.2021.105802
- High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China L. Li 10.3390/rs13122324
- Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. 10.1016/j.atmosenv.2021.118659
- Efforts in reducing air pollution exposure risk in China: State versus individuals B. Zou et al. 10.1016/j.envint.2020.105504
- Long-term changes in aerosol loading over the ‘BIHAR’ State of India using nineteen years (2001–2019) of high-resolution satellite data (1 × 1 km2) M. Nair et al. 10.1016/j.apr.2021.101259
- Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks L. Zhang et al. 10.3390/rs13193834
- The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth Q. He et al. 10.5194/acp-21-18375-2021
- Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data Q. He et al. 10.1016/j.envint.2020.105536
- What drives long-term PM2.5-attributable premature mortality change? A case study in central China using high-resolution satellite data from 2003 to 2018 Q. He et al. 10.1016/j.envint.2022.107110
Latest update: 28 Mar 2023
Short summary
The systematic validation and comparison of MAIAC, DT, and DB AOD products across China from 2000 to 2017 is presented. In this process, the overall accuracy, land type dependence, view geometry dependence, spatiotemporal retrieval accuracy, spatial variation pattern, and spatiotemporal completeness of the three products are analyzed in detail. In general, MAIAC achieves higher accuracy than DT and DB, but DT and DB may perform better than MAIAC under some specific land cover types or seasons.
The systematic validation and comparison of MAIAC, DT, and DB AOD products across China from...
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