Evaluation of MAIAC aerosol retrievals over China

被引:71
|
作者
Zhang, Zhaoyang [1 ]
Wu, Weiling [2 ]
Fan, Meng [3 ]
Wei, Jing [4 ]
Tan, Yunhui [1 ]
Wang, Quan [5 ]
机构
[1] Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua, Zhejiang Provin, Peoples R China
[2] Chinese Acad Environm Planning, Beijing, Peoples R China
[3] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[4] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[5] Shizuoka Univ, Fac Agr, Shizuoka, Japan
基金
美国国家科学基金会;
关键词
Aerosol optical depth; Validation; AERONET; MAIAC; OPTICAL DEPTH; AIR-POLLUTION; MODIS; VALIDATION; VISIBILITY; PRODUCTS; LAND; ALGORITHM; THICKNESS;
D O I
10.1016/j.atmosenv.2019.01.013
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Multiangle Implementation of Atmospheric Correction (MAIAC) is a new aerosol algorithm developed to retrieve aerosol optical depth (AOD) over land using the time series data to dynamically isolate aerosol and land contributions. However, there are still no comprehensive research on the quality of MAIAC AOD over China. In this paper, 1 km MAIAC AOD over China were examined against ground-based measurements to evaluate the performance of the data. In general, Aqua and Terra MAIAC retrievals have a high correlation coefficient (0.924 for Aqua and 0.933 for Terra) with ground-based observations and there are more than 72% of retrievals falling within the Expected Errors (EE = +/- (0.05 + 0.2*AOD)). We found that the accuracy of MAIAC AOD is related with the AOD magnitude, aerosol size, seasons and surface types. In spring and summer, the AOD bias was influenced by both aerosol model and surface reflectance estimation. We also found that the aerosol model is the main source of AOD bias over desert regions. These results indicated that the MAIAC AOD could be used as a new aerosol data source for air quality and climate studies in China.
引用
收藏
页码:8 / 16
页数:9
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