Journal article
Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation
Atmospheric environment (1994), Vol.213, pp.159-169
09/15/2019
DOI: 10.1016/j.atmosenv.2019.06.004
Abstract
The MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) algorithm enables simultaneous retrieval of aerosol and bidirectional surface reflectance at high resolution of 1 km. Taking advantage of multi-angle and image-based information, the MAIAC algorithm has great potential for improving retrieval of aerosols over both dark and bright surfaces. Here, by comparing MAIAC aerosol products with the ground-based observations at 9 typical sites spread out in China, we gain the insights regarding the performance of MAIAC algorithm, for the first time, over Asia that has complicated surface types, diverse aerosol sources, and heavy loading of aerosols in the atmosphere. While aerosol products from MAIAC show similar spatial distribution as that from MODIS Dark-Target (DT) and Deep-Blue (DB) algorithms, they are superior to reveal numerous hotspots of high AOD values in fine scales due to their higher resolution at 1 km. Moreover, since MAIAC algorithm for cloud screening uses time series of observations, it shows higher effectiveness to mask cloudy pixels as well as the pixels of the melting and aging ice/snow surfaces. While MAIAC and ground-observed AOD values show high correlation coefficient of ∼0.94 in two AERONET sites of Beijing and Xianghe, considerable bias is prevalent in other regions of China. Systematic underestimation is found over the deserts in western China likely due to the high bias of single scattering properties of aerosol model prescribed in MAIAC algorithm. In eastern China, the distinct positive bias is found in conditions with low-moderate AOD values and likely results from errors in regression coefficients in the surface reflectance model. Given its advantages in cloud and snow/ice screening and retrieval in fine spatial resolution, MAIAC algorithm can be improved by further refinement of regional aerosol and surface properties.
•First comprehensive insight into performance of MAIAC aerosol algorithm in complicated background in China.•Accuracy of MAIAC retrievals exhibits distinct spatial variations and prevalent bias.•Aerosol and surface assumptions of MAIAC algorithm needs to be improved by considering regional variations.
Details
- Title: Subtitle
- Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation
- Creators
- Minghui Tao - Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, ChinaJun Wang - Dept. of Chemical & Environmental Engineering, University of Iowa, Iowa City, 52242-1503, USARong Li - School of Resources and Environmental Science, Hubei University, Wuhan, 430062, ChinaLili Wang - State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaLunche Wang - Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, ChinaZifeng Wang - State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, ChinaJinhua Tao - State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, ChinaHuizheng Che - Key Laboratory of Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing, 100081, ChinaLiangfu Chen - State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China
- Resource Type
- Journal article
- Publication Details
- Atmospheric environment (1994), Vol.213, pp.159-169
- DOI
- 10.1016/j.atmosenv.2019.06.004
- ISSN
- 1352-2310
- eISSN
- 1873-2844
- Publisher
- Elsevier Ltd
- Grant note
- name: National Key R&D Program of China, award: 2017YFB0503901; DOI: 10.13039/501100001809, name: National Science Foundation of China, award: 41601472, 41871262, 41830109, 41471306
- Language
- English
- Date published
- 09/15/2019
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984104806902771
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