Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1655-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-1655-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2021
Research article |
| 01 Mar 2021
| 01 Mar 2021
Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison
Yang Zhang
College of Resources and Environment, University of Information
Technology, Chengdu 610225, China
Zhengqiang Li
CORRESPONDING AUTHOR
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
Zhihong Liu
College of Resources and Environment, University of Information
Technology, Chengdu 610225, China
Yongqian Wang
College of Resources and Environment, University of Information
Technology, Chengdu 610225, China
Chongqing Institute of Meteorological Sciences, Chongqing 401147,
China
Lili Qie
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
Yisong Xie
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
Weizhen Hou
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
Lu Leng
Beijing Enterprises (Chengdu Shuangliu) Water Co., Ltd., Chengdu
610000, China
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Cited
21 citations as recorded by crossref.
- Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models Z. Zang et al. https://doi.org/10.3390/rs13142779
- Air Quality over China G. de Leeuw et al. https://doi.org/10.3390/rs13173542
- Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. https://doi.org/10.1016/j.envint.2022.107343
- Estimation of the Mass Concentration of Volcanic Ash Using Ceilometers: Study of Fresh and Transported Plumes from La Palma Volcano A. Bedoya-Velásquez et al. https://doi.org/10.3390/rs14225680
- Multi-angle aerosol optical depth retrieval method based on improved surface reflectance L. Chen et al. https://doi.org/10.5194/amt-17-4411-2024
- Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.09.001
- Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N. Luo et al. https://doi.org/10.1016/j.envpol.2024.123838
- 基于神经网络的星载高精度偏振扫描仪数据气溶胶光学厚度反演研究 杨. Yang Chenyu et al. https://doi.org/10.3788/AOS250438
- An Enhanced Aerosol Optical Depth Retrieval Algorithm for Particulate Observing Scanning Polarimeter (POSP) Data Over Land Z. Ji et al. https://doi.org/10.1109/TGRS.2024.3514170
- A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al. https://doi.org/10.1016/j.atmosenv.2025.121083
- A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China Y. Shao et al. https://doi.org/10.34133/remotesensing.0032
- Aerosol Fine-Mode-Fraction Retrieval From GEO-KOMPSAT-2A/AMI Using a Deep Neural Network and Spectral Deconvolution Algorithm M. Kim et al. https://doi.org/10.1109/TGRS.2025.3591177
- Towards improved retrieval of aerosol properties from the geostationary orbit with the new Meteosat Third Generation-Imager satellite A. Georgeot et al. https://doi.org/10.5194/amt-18-4665-2025
- A Capsule Network Model for Aerosol Retrieval from DPC/Gaofen-5(02) Satellite Multi-Angle Polarimetric Observation H. Gu et al. https://doi.org/10.34133/remotesensing.1008
- A Novel Physics-Informed Transformer Algorithm (PITA) for Near-Real-Time Synergistic Multiparameter Aerosol Retrieval From FY-4B/AGRI Geostationary Satellite Y. Jiao et al. https://doi.org/10.1109/TGRS.2026.3700045
- 大气气溶胶尺度信息的卫星遥感反演：进展、挑战与展望（特邀） 晏. Yan Xing et al. https://doi.org/10.3788/AOS251947
- Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. https://doi.org/10.1016/j.earscirev.2023.104461
- Evaluation of the MODIS Collection 6.1 3 km aerosol optical depth product over China M. Zhang et al. https://doi.org/10.1016/j.atmosenv.2022.118970
- A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. https://doi.org/10.1109/TGRS.2023.3307934
- Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data Z. Shi et al. https://doi.org/10.1109/TGRS.2022.3192908
- A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. https://doi.org/10.5194/essd-14-1193-2022
21 citations as recorded by crossref.
- Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models Z. Zang et al. https://doi.org/10.3390/rs13142779
- Air Quality over China G. de Leeuw et al. https://doi.org/10.3390/rs13173542
- Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. https://doi.org/10.1016/j.envint.2022.107343
- Estimation of the Mass Concentration of Volcanic Ash Using Ceilometers: Study of Fresh and Transported Plumes from La Palma Volcano A. Bedoya-Velásquez et al. https://doi.org/10.3390/rs14225680
- Multi-angle aerosol optical depth retrieval method based on improved surface reflectance L. Chen et al. https://doi.org/10.5194/amt-17-4411-2024
- Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.09.001
- Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N. Luo et al. https://doi.org/10.1016/j.envpol.2024.123838
- 基于神经网络的星载高精度偏振扫描仪数据气溶胶光学厚度反演研究 杨. Yang Chenyu et al. https://doi.org/10.3788/AOS250438
- An Enhanced Aerosol Optical Depth Retrieval Algorithm for Particulate Observing Scanning Polarimeter (POSP) Data Over Land Z. Ji et al. https://doi.org/10.1109/TGRS.2024.3514170
- A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al. https://doi.org/10.1016/j.atmosenv.2025.121083
- A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China Y. Shao et al. https://doi.org/10.34133/remotesensing.0032
- Aerosol Fine-Mode-Fraction Retrieval From GEO-KOMPSAT-2A/AMI Using a Deep Neural Network and Spectral Deconvolution Algorithm M. Kim et al. https://doi.org/10.1109/TGRS.2025.3591177
- Towards improved retrieval of aerosol properties from the geostationary orbit with the new Meteosat Third Generation-Imager satellite A. Georgeot et al. https://doi.org/10.5194/amt-18-4665-2025
- A Capsule Network Model for Aerosol Retrieval from DPC/Gaofen-5(02) Satellite Multi-Angle Polarimetric Observation H. Gu et al. https://doi.org/10.34133/remotesensing.1008
- A Novel Physics-Informed Transformer Algorithm (PITA) for Near-Real-Time Synergistic Multiparameter Aerosol Retrieval From FY-4B/AGRI Geostationary Satellite Y. Jiao et al. https://doi.org/10.1109/TGRS.2026.3700045
- 大气气溶胶尺度信息的卫星遥感反演：进展、挑战与展望（特邀） 晏. Yan Xing et al. https://doi.org/10.3788/AOS251947
- Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. https://doi.org/10.1016/j.earscirev.2023.104461
- Evaluation of the MODIS Collection 6.1 3 km aerosol optical depth product over China M. Zhang et al. https://doi.org/10.1016/j.atmosenv.2022.118970
- A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. https://doi.org/10.1109/TGRS.2023.3307934
- Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data Z. Shi et al. https://doi.org/10.1109/TGRS.2022.3192908
- A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. https://doi.org/10.5194/essd-14-1193-2022
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Short summary
The aerosol fine-mode fraction (FMF) is an important parameter reflecting the content of man-made aerosols. This study carried out the retrieval of FMF in China based on multi-angle polarization data and validated the results. The results of this study can contribute to the FMF retrieval algorithm of multi-angle polarization sensors. At the same time, a high-precision FMF dataset of China was obtained, which can provide basic data for atmospheric environment research.
The aerosol fine-mode fraction (FMF) is an important parameter reflecting the content of...
