Articles | Volume 16, issue 15
https://doi.org/10.5194/acp-16-9655-2016
© Author(s) 2016. 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-16-9655-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note
| 
02 Aug 2016
Technical note |
| 02 Aug 2016
Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China
Yahui Che
State Key Laboratory of Remote Sensing Science, jointly sponsored by
the Institute of Remote Sensing and Digital Earth of the Chinese Academy of
Sciences and Beijing Normal University, Institute of Remote Sensing and
Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
University of Chinese Academy of Sciences, 100049 Beijing, China
State Key Laboratory of Remote Sensing Science, jointly sponsored by
the Institute of Remote Sensing and Digital Earth of the Chinese Academy of
Sciences and Beijing Normal University, Institute of Remote Sensing and
Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
Department of Computing and Mathematics, College of Engineering and
Technology, University of Derby, Kedleston Road, Derby, DE22 1GB, UK
Linlu Mei
Key Laboratory of Digital Earth Science, Institute of Remote Sensing
and Digital Earth, Chinese Academy of Sciences, 100094 Beijing, China
Jie Guang
Key Laboratory of Digital Earth Science, Institute of Remote Sensing
and Digital Earth, Chinese Academy of Sciences, 100094 Beijing, China
State Key Laboratory of Remote Sensing Science, jointly sponsored by
the Institute of Remote Sensing and Digital Earth of the Chinese Academy of
Sciences and Beijing Normal University, Institute of Remote Sensing and
Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
University of Chinese Academy of Sciences, 100049 Beijing, China
Jianping Guo
Centre for Atmosphere Watch and Services, Chinese Academy of
Meteorological Sciences, 46 Zhongguancun South Avenue, Haidian District,
100081 Beijing, China
Yincui Hu
Hebei Key Laboratory of Environmental Change and Ecological
Construction, College of Resources and Environment Science, Hebei Normal
University, Shijiazhuang, Hebei Province, China
Hui Xu
Key Laboratory of Digital Earth Science, Institute of Remote Sensing
and Digital Earth, Chinese Academy of Sciences, 100094 Beijing, China
Xingwei He
State Key Laboratory of Remote Sensing Science, jointly sponsored by
the Institute of Remote Sensing and Digital Earth of the Chinese Academy of
Sciences and Beijing Normal University, Institute of Remote Sensing and
Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
University of Chinese Academy of Sciences, 100049 Beijing, China
Aojie Di
State Key Laboratory of Remote Sensing Science, jointly sponsored by
the Institute of Remote Sensing and Digital Earth of the Chinese Academy of
Sciences and Beijing Normal University, Institute of Remote Sensing and
Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
University of Chinese Academy of Sciences, 100049 Beijing, China
Cheng Fan
State Key Laboratory of Remote Sensing Science, jointly sponsored by
the Institute of Remote Sensing and Digital Earth of the Chinese Academy of
Sciences and Beijing Normal University, Institute of Remote Sensing and
Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
University of Chinese Academy of Sciences, 100049 Beijing, China
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Cited
16 citations as recorded by crossref.
- Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
- 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
- Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective H. Lappalainen et al. 10.5194/acp-22-4413-2022
- Ensemble of ESA/AATSR Aerosol Optical Depth Products Based on the Likelihood Estimate Method With Uncertainties Y. Xie et al. 10.1109/TGRS.2017.2757910
- Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms—Part 1: Global Evaluation Y. Che et al. 10.3390/rs10091414
- Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China Y. Che et al. 10.1016/j.isprsjprs.2018.09.004
- Assessment of the aerosol optical depths measured by satellite-based passive remote sensors in the Alberta oil sands region C. Sioris et al. 10.5194/acp-17-1931-2017
- Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012 Y. Che et al. 10.5194/amt-12-4091-2019
- Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites H. Shi et al. 10.1016/j.atmosres.2019.01.009
- A Physically Based PM$_{\text{2.5}}$ Estimation Method Using AERONET Data in Beijing Area G. Chen et al. 10.1109/JSTARS.2018.2817243
- Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns G. de Leeuw et al. 10.5194/acp-18-1573-2018
- Characteristic and Driving Factors of Aerosol Optical Depth over Mainland China during 1980–2017 W. Qin et al. 10.3390/rs10071064
- Temporal and spatial variations in dust activity in Australia based on remote sensing and reanalysis datasets Y. Che et al. 10.5194/acp-24-4105-2024
- Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia Y. Che et al. 10.1016/j.atmosenv.2022.119054
- Aerosol Optical Depth Retrieval Over Land from Particulate Observing Scanning Polarimeter (POSP) Using a New Look-Up Table (LUT) Method Z. Ji et al. 10.1007/s41810-024-00236-6
- 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
16 citations as recorded by crossref.
- Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
- 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
- Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective H. Lappalainen et al. 10.5194/acp-22-4413-2022
- Ensemble of ESA/AATSR Aerosol Optical Depth Products Based on the Likelihood Estimate Method With Uncertainties Y. Xie et al. 10.1109/TGRS.2017.2757910
- Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms—Part 1: Global Evaluation Y. Che et al. 10.3390/rs10091414
- Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China Y. Che et al. 10.1016/j.isprsjprs.2018.09.004
- Assessment of the aerosol optical depths measured by satellite-based passive remote sensors in the Alberta oil sands region C. Sioris et al. 10.5194/acp-17-1931-2017
- Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012 Y. Che et al. 10.5194/amt-12-4091-2019
- Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites H. Shi et al. 10.1016/j.atmosres.2019.01.009
- A Physically Based PM$_{\text{2.5}}$ Estimation Method Using AERONET Data in Beijing Area G. Chen et al. 10.1109/JSTARS.2018.2817243
- Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns G. de Leeuw et al. 10.5194/acp-18-1573-2018
- Characteristic and Driving Factors of Aerosol Optical Depth over Mainland China during 1980–2017 W. Qin et al. 10.3390/rs10071064
- Temporal and spatial variations in dust activity in Australia based on remote sensing and reanalysis datasets Y. Che et al. 10.5194/acp-24-4105-2024
- Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia Y. Che et al. 10.1016/j.atmosenv.2022.119054
- Aerosol Optical Depth Retrieval Over Land from Particulate Observing Scanning Polarimeter (POSP) Using a New Look-Up Table (LUT) Method Z. Ji et al. 10.1007/s41810-024-00236-6
- 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
Discussed (preprint)
Latest update: 10 Dec 2024
Short summary
Remotely sensed data could provide continuous spatial coverage of aerosol property over the pan-Eurasian area for PEEX program. The AATSR data can be used to retrieve aerosol optical depth (AOD). The Aerosol_cci project provides users with three AOD retrieval algorithms for AATSR data. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the Level 2 AOD products from AATSR data more comprehensively.
Remotely sensed data could provide continuous spatial coverage of aerosol property over the...
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