Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia

被引:167
作者
Mhawish, Alaa [1 ]
Banerjee, Tirthankar [1 ,2 ]
Sorek-Hamer, Meytar [3 ]
Lyapustin, Alexei [4 ]
Broday, David M. [5 ]
Chatfield, Robert [3 ]
机构
[1] Banaras Hindu Univ, Inst Environm & Sustainable Dev, Varanasi 221005, Uttar Pradesh, India
[2] Banaras Hindu Univ, DST Mahamana Ctr Excellence Climate Change Res, Varanasi, Uttar Pradesh, India
[3] NASA, Ames Res Ctr, Moffett Field, CA 94035 USA
[4] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA
[5] Technion, Civil & Environm Engn, Haifa, Israel
基金
以色列科学基金会;
关键词
AERONET; AOD; MODIS; MAIAC; Aerosols; IGP; South Asia; BIOMASS BURNING EMISSIONS; OPTICAL DEPTH; RETRIEVAL ALGORITHMS; SOURCE APPORTIONMENT; DAILY PM2.5; CLIMATE; POLLUTION; AIR; LAND; VALIDATION;
D O I
10.1016/j.rse.2019.01.033
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. Capsule: In comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage.
引用
收藏
页码:12 / 28
页数:17
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