干旱区生态环境知识资源中心(Arid): Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation

Arid

DOI	10.1016/j.atmosenv.2019.06.004
	Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation
	Tao, Minghui 1; Wang, Jun 2; Li, Rong 3; Wang, Lili 4; Wang, Lunche 1; Wang, Zifeng 5; Tao, Jinhua 5; Che, Huizheng 6; Chen, Liangfu 5
通讯作者	Tao, Minghui ; Li, Rong
来源期刊	ATMOSPHERIC ENVIRONMENT
ISSN	1352-2310
EISSN	1873-2844
出版年	2019
卷号	213 页码:159-169
英文摘要	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 similar to 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.
英文关键词	Aerosols MAIAC Algorithm MODIS China
类型	Article
语种	英语
国家	Peoples R China ; USA
收录类别	SCI-E
WOS记录号	WOS:000484870900014
WOS关键词	OPTICAL DEPTH ; MICROPHYSICAL PROPERTIES ; INVERSION ALGORITHM ; RETRIEVAL ; AERONET ; LAND ; VALIDATION ; NETWORK ; PRODUCT ; CLIMATE
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
来源机构	中国科学院大气物理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/214504
作者单位	1.China Univ Geosci, Sch Geog & Informat Engn, Hubei Key Lab Crit Zone Evolut, Wuhan 430074, Hubei, Peoples R China; 2.Univ Iowa, Dept Chem & Environm Engn, Iowa City, IA 52242 USA; 3.Hubei Univ, Sch Resources & Environm Sci, Wuhan 430062, Hubei, Peoples R China; 4.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atm, Beijing 100029, Peoples R China; 5.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; 6.Chinese Acad Meteorol Sci, Key Lab Atmospher Chem, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Tao, Minghui,Wang, Jun,Li, Rong,et al. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation[J]. 中国科学院大气物理研究所,2019,213:159-169.
APA	Tao, Minghui.,Wang, Jun.,Li, Rong.,Wang, Lili.,Wang, Lunche.,...&Chen, Liangfu.(2019).Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation.ATMOSPHERIC ENVIRONMENT,213,159-169.
MLA	Tao, Minghui,et al."Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation".ATMOSPHERIC ENVIRONMENT 213(2019):159-169.