Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas

CHEN Xiangyue; DING Jianli; WANG Jingzhe; GE Xiangyu; ZHANG Zipeng; ZHANG Zhe; ZUO Hongchao

doi:10.11834/jrs.20220508

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Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas
Vol. 27, Issue 2, Pages: 406-419(2023)
Published： 07 February 2023 ，
DOI： 10.11834/jrs.20220508

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陈香月，丁建丽，王敬哲，葛翔宇，张子鹏，张喆，左洪超.2023.MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究.遥感学报，27（2）： 406-419

Chen X Y，Ding J L，Wang J Z，Ge X Y，Zhang Z P，Zhang Z and Zuo H C. 2023. Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas. National Remote Sensing Bulletin， 27（2）：406-419
陈香月，丁建丽，王敬哲，葛翔宇，张子鹏，张喆，左洪超.2023.MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究.遥感学报，27（2）： 406-419 DOI： 10.11834/jrs.20220508.

Chen X Y，Ding J L，Wang J Z，Ge X Y，Zhang Z P，Zhang Z and Zuo H C. 2023. Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas. National Remote Sensing Bulletin， 27（2）：406-419 DOI： 10.11834/jrs.20220508.

摘要

多角度大气校正MAIAC（Multiangle Implementation of Atmospheric Correction）是一种新的获取高分辨率气溶胶光学厚度AOD（Aerosol Optical Depth）的通用算法，其空间分辨率高达1 km，在改善陆地暗表面和亮表面AOD反演方面具有很大潜力，但在资料稀缺的干旱区的适用性尚不可知。基于此，利用2000年—2019年AERONET气溶胶站点观测数据，对干旱背景下的MAIAC AOD开展适用性评价。结果表明：大量的MAIAC和AERONET AOD匹配显示了MAIAC AOD在不同时间尺度和下垫面类型都具有较好的信息表征能力。MAIAC AOD在月、季和年尺度上都具有良好的应用前景，总体效果呈现出较好的适用性（

= 0.821，RMSE = 0.117），但存在轻微的低估现象。MAIAC AOD在4种典型地表都具有良好的应用优势，其中草地有效匹配数据最多并呈现高估现象，建筑用地受气溶胶模型影响最大，农田受地表反射率干扰强烈且低估最明显（Below EE = 46.5%）。MAIAC AOD双星观测Terra和Aqua准确性较为一致，

都达到了0.75以上，同时也都存在低估现象，其中Aqua的低估效果更明显。总体而言，MAIAC AOD在干旱背景下适用性良好，具有研究干旱区精细气溶胶特性的潜力，并可进一步促进干旱区大气污染研究。

Abstract

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). It is expected to have good potential in improving the AOD inversion of dark and bright surfaces of land. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and determine subtle aerosol features. Retrieval of satellite aerosol properties is therefore often challenging due to considerable seasonal variations in surface reflectance and aerosol properties. To date

MAIAC AOD over arid regions under data-scarce environments has been evaluated. Considering these uncertainties

a systematic effort was made to evaluate the MAIAC AOD over arid areas using Aerosol Robotic Network (AERONET) ground-based AOD from 2000 to 2019. Considerable MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied ground surfaces and temporal scales. We employed a broader perspective and evaluated MAIAC performance under varying aerosol loading

aerosol types

surface coverage

and viewing geometry. The results show that (1) MAIAC performed well over various temporal scales

including monthly

seasonal and annual scales. Although underestimation is prevalent

MAIAC AOD in the spring and winter months correspond to the highest and lowest retrieval accuracies

respectively. (2) MAIAC performed well over four different typical land surface land surfaces

showing the highest retrieval accuracy over grassland

yet it slightly overestimated AOD. Construction land is most affected by the aerosol model

and farmland is strongly disturbed by surface reflectance and underestimated most obviously (Below EE = 46.5%). (3) The accuracies of the MAIAC AOD observations of Terra and Aqua are similar;

is more than 0.75

but both are underestimated

especially for Aqua. In general

MAIAC’s ability to provide AOD at high spatial resolution appears promising over arid areas and is expected to be helpful to study the characteristics of fine aerosols in arid areas and promote the study of local air quality.

关键词

遥感气溶胶AODAERONETMAIAC干旱区

Keywords

remote sensingaerosolsAODAERONETMAIACarid areas

references

Ali M A and Assiri M. 2019. Analysis of AOD from MODIS-merged DT-DB products over the Arabian Peninsula. Earth Systems and Environment, 3(3): 625-636 [DOI: 10.1007/s41748-019-00108-xhttp://dx.doi.org/10.1007/s41748-019-00108-x]

Almazroui M. 2019. A comparison study between AOD data from MODIS deep blue collections 51 and 06 and from AERONET over Saudi Arabia. Atmospheric Research, 225: 88-95 [DOI: 10.1016/j.atmosres.2019.03.040http://dx.doi.org/10.1016/j.atmosres.2019.03.040]

Ångström A. 1964. The parameters of atmospheric turbidity. Tellus, 16(1): 64-75 [DOI: 10.3402/tellusa.v16i1.8885http://dx.doi.org/10.3402/tellusa.v16i1.8885]

Carslaw K S, Lee L A, Reddington C L, Pringle K J, Rap A, Forster P M, Mann G W, Spracklen D V, Woodhouse M T, Regayre L A and Pierce J R. 2013. Large contribution of natural aerosols to uncertainty in indirect forcing. Nature, 503(7474): 67-71 [DOI: 10.1038/nature12674http://dx.doi.org/10.1038/nature12674]

Chen F H, Fu B J, Xia J, Wu D, Wu S H, Zhang Y L, Sun H, Liu Y, Fang X M, Qin B Q, Li X, Zhang T J, Liu B Y, Dong Z B, Hou S G, Tian L D, Xu B Q, Dong G H, Zheng J Y, Yang W, Wang X, Li Z J, Wang F, Hu Z B, Wang J, Liu J B, Chen J H, Huang W, Hou J Z, Cai Q F, Long H, Jiang M, Hu Y X, Feng X M, Mo X G, Yang X Y, Zhang D J, Wang X H, Yin Y H and Liu X C. 2019. Major advances in studies of the physical geography and living environment of China during the past 70 years and future prospects. Science China Earth Sciences, 62(11): 1665-1701 [DOI: 10.1007/s11430-019-9522-7http://dx.doi.org/10.1007/s11430-019-9522-7]

Chen X Y, Ding J L, Liu J, Wang J Z, Ge X Y, Wang R and Zuo H C. 2021. Validation and comparison of high-resolution MAIAC aerosol products over Central Asia. Atmospheric Environment, 251: 118273 [DOI: 10.1016/j.atmosenv.2021.118273http://dx.doi.org/10.1016/j.atmosenv.2021.118273]

Chen X Y, Ding J L, Wang J Z, Ge X Y and Liang J. 2019. Spatiotemporal evolution and driving mechanism of aerosol optical depth in the Ebinur lake basin. Environmental Science, 40(11): 4824-4832

陈香月, 丁建丽, 王敬哲, 葛翔宇, 梁静. 2019. 艾比湖流域气溶胶光学厚度时空演变及影响因素. 环境科学, 40(11): 4824-4832 [DOI: 10.13227/j.hjkx.201904200http://dx.doi.org/10.13227/j.hjkx.201904200]

Chen X Y, Ding J L, Wang J Z, Ge X Y, Raxidin M, Liang J, Chen X X, Zhang Z P, Cao X Y and Ding Y. 2020. Retrieval of fine-resolution aerosol optical depth (AOD) in semiarid urban areas using landsat data: a case study in Urumqi, NW China. Remote Sensing, 12(3): 467 [DOI: 10.3390/rs12030467http://dx.doi.org/10.3390/rs12030467]

Chin M, Chu A, Levy R, Remer L, Kaufman Y, Holben B, Eck T, Ginoux P and Gao Q X. 2004. Aerosol distribution in the Northern Hemisphere during ACE-Asia: results from global model, satellite observations, and Sun photometer measurements. Journal of Geophysical Research: Atmospheres, 109(D23): D23S90 [DOI: 10.1029/2004jd004829http://dx.doi.org/10.1029/2004jd004829]

Ge Y X, Abuduwaili J, Ma L and Liu D W. 2016. Temporal variability and potential diffusion characteristics of dust aerosol originating from the Aral Sea basin, central Asia. Water, Air, and Soil Pollution, 227(3): 63 [DOI: 10.1007/s11270-016-2758-6http://dx.doi.org/10.1007/s11270-016-2758-6]

Holben B N, Eck T F, Slutsker I, Tanré D, Buis J P, Setzer A, Vermote E, Reagan J A, Kaufman Y J, Nakajima T, Lavenu F, Jankowiak I and Smirnov A. 1998. AERONET—A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1): 1-16 [DOI: 10.1016/S0034-4257(98)00031-5http://dx.doi.org/10.1016/S0034-4257(98)00031-5]

Huang J P, Yu H P, Dai A G, Wei Y and Kang L T. 2017. Drylands face potential threat under 2 C global warming target. Nature Climate Change, 7(6): 417-422 [DOI: 10.1038/nclimate3275http://dx.doi.org/10.1038/nclimate3275]

Kaufman Y J, Tanré D and Boucher O. 2002. A satellite view of aerosols in the climate system. Nature, 419(6903): 215-223 [DOI: 10.1038/nature01091http://dx.doi.org/10.1038/nature01091]

Kaufman Y J, Tanré D, Gordon H R, Nakajima T, Lenoble J, Frouin R, Grassl H, Herman B M, King M D and Teillet P M. 1997. Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. Journal of Geophysical Research: Atmospheres, 102(D14): 16815-16830 [DOI: 10.1029/97JD01496http://dx.doi.org/10.1029/97JD01496]

Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R and Eck T F. 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420 [DOI: 10.5194/acp-10-10399-2010http://dx.doi.org/10.5194/acp-10-10399-2010]

Li R K, Ma T X, Xu Q and Song X F. 2018. Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model. Environmental Pollution, 243: 501-509 [DOI: 10.1016/j.envpol.2018.09.026http://dx.doi.org/10.1016/j.envpol.2018.09.026]

Li Z, Yan F L and Fan X T. 2005. The variability of NDVI over Northwest China and its relation to temperature and precipitation. Journal of Remote Sensing, 9(3): 308-313

李震, 阎福礼, 范湘涛. 2005. 中国西北地区NDVI变化及其与温度和降水的关系. 遥感学报, 9(3): 308-313 [DOI: 10.3321/j.issn:1007-4619.2005.03.013http://dx.doi.org/10.3321/j.issn:1007-4619.2005.03.013]

Liang D, Zuo Y, Huang L S, Zhao J L, Teng L and Yang F. 2015. Evaluation of the consistency of MODIS land cover product (MCD12Q1) based on Chinese 30 m GlobeLand30 datasets: a case study in Anhui Province, China. ISPRS International Journal of Geo-Information, 4(4): 2519-2541 [DOI: 10.3390/ijgi4042519http://dx.doi.org/10.3390/ijgi4042519]

Liu A X. 2004. Remote Sensing Monitoring of Desertification in China and Central Asia. Beijing: Chinese Academy of Sciences

刘爱霞. 2004. 中国及中亚地区荒漠化遥感监测研究. 北京: 中国科学院研究生院

Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765 [DOI: 10.5194/amt-11-5741-2018http://dx.doi.org/10.5194/amt-11-5741-2018]

Lyapustin A I, Wang Y J, Laszlo I, Hilker T, Hall F G, Sellers P J, Tucker C J and Korkin S V. 2012. Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction. Remote Sensing of Environment, 127: 385-393 [DOI: 10.1016/j.rse.2012.09.002http://dx.doi.org/10.1016/j.rse.2012.09.002]

Martins V S, Lyapustin A, Carvalho L A S, Barbosa C C F and Novo E M L M. 2017. Validation of high‐resolution MAIAC aerosol product over South America. Journal of Geophysical Research: Atmospheres, 122(14): 7537-7559 [DOI: 10.1002/2016JD026301http://dx.doi.org/10.1002/2016JD026301]

Mhawish A, Banerjee T, Sorek-Hamer M, Lyapustin A, Broday D M and Chatfield R. 2019. Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia. Remote Sensing of Environment, 224: 12-28 [DOI: 10.1016/j.rse.2019.01.033http://dx.doi.org/10.1016/j.rse.2019.01.033]

Nabavi S O, Haimberger L and Abbasi E. 2019. Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms. Atmospheric Pollution Research, 10(3): 889-903 [DOI: 10.1016/j.apr.2018.12.017http://dx.doi.org/10.1016/j.apr.2018.12.017]

Omar A H, Won J G, Winker D M, Yoon S C, Dubovik O and McCormick M P. 2005. Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements. Journal of Geophysical Research: Atmospheres, 110(D10): D10S14 [DOI: 10.1029/2004JD004874http://dx.doi.org/10.1029/2004JD004874]

Remer L A, Kaufman Y J, Tanré D, Mattoo S, Chu D A, Martins J V, Li R R, Ichoku C, Levy R C, Kleidman R G, Eck T F, Vermote E and Holben B N. 2005. The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences, 62(4): 947-973 [DOI: 10.1175/JAS3385.1http://dx.doi.org/10.1175/JAS3385.1]

Sayer A M, Hsu N C, Bettenhausen C and Jeong M J. 2013. Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. Journal of Geophysical Research: Atmospheres, 118(1): 7864-7872 [DOI: 10.1002/jgrd.50600http://dx.doi.org/10.1002/jgrd.50600]

Sayer A M, Munchak L A, Hsu N C, Levy R C, Bettenhausen C and Jeong M J. 2014. MODIS Collection 6 aerosol products: comparison between Aqua’s e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations. Journal of Geophysical Research: Atmospheres, 119(24): 13965-13989 [DOI: 10.1002/2014JD022453http://dx.doi.org/10.1002/2014JD022453]

Tao M H, Chen L F, Wang Z F, Tao J H, Che H Z, Wang X H and Wang Y. 2015. Comparison and evaluation of the MODIS Collection 6 aerosol data in China. Journal of Geophysical Research: Atmospheres, 120(14): 6992-7005 [DOI: 10.1002/2015JD023360http://dx.doi.org/10.1002/2015JD023360]

Tao M H, Chen L F, Wang Z F, Wang J, Che H Z, Xu X G, Wang W C, Tao J H, Zhu H and Hou C. 2017. Evaluation of MODIS deep blue aerosol algorithm in desert region of east asia: ground validation and intercomparison. Journal of Geophysical Research: Atmospheres, 122(19): 10357-10368 [DOI: 10.1002/2017JD026976http://dx.doi.org/10.1002/2017JD026976]

Tao M H, Wang J, Li R, Wang L L, Wang L C, Wang Z F, Tao J H, Che H Z and Chen L F. 2019. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: characterization and limitation. Atmospheric Environment, 213: 159-169 [DOI: 10.1016/j.atmosenv.2019.06.004http://dx.doi.org/10.1016/j.atmosenv.2019.06.004]

Tian X P, Liu Q, Li X H and Wei J. 2018. Validation and comparison of MODIS C6.1 and C6 aerosol products over Beijing, China. Remote Sensing, 10(12): 2021 [DOI: 10.3390/rs10122021http://dx.doi.org/10.3390/rs10122021]

Tian X P, Sun L, Liu Q and Li X H. 2018. Retrieval of high-resolution aerosol optical depth using Landsat 8 OLI data over Beijing. Journal of Remote Sensing, 22(1): 51-63

田信鹏, 孙林, 刘强, 李秀红. 2018. 北京地区Landsat8OLI高空间分辨率气溶胶光学厚度反演. 遥感学报, 22(1): 51-63 [DOI: 10.11834/jrs.20186362http://dx.doi.org/10.11834/jrs.20186362]

Wang S M, Liu Q H and Huang C. 2021. Vegetation change and its response to climate extremes in the arid region of Northwest China. Remote Sensing, 13(7): 1230 [DOI: 10.3390/rs13071230http://dx.doi.org/10.3390/rs13071230]

Wang Y, Yuan Q Q, Shen H F, Zheng L and Zhang L P. 2020. Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: evaluation, comparison, and merging. Atmospheric Environment, 230: 117548 [DOI: 10.1016/j.atmosenv.2020.117548http://dx.doi.org/10.1016/j.atmosenv.2020.117548]

Yu H B, Yang Y, Wang H L, Tan Q, Chin M, Levy R C, Remer L A, Smith S J, Yuan T L and Shi Y X. 2020. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003-2017. Atmospheric Chemistry and Physics, 20(1): 139-161 [DOI: 10.5194/acp-20-139-2020http://dx.doi.org/10.5194/acp-20-139-2020]

Zhang Z, Ding J L, Wang J J and Chen W Q. 2017. Observational study on salt dust aerosol optical properties using the ground-based and satellite remote sensing. Journal of Remote Sensing, 21(5): 665-678

张喆, 丁建丽, 王瑾杰, 陈文倩. 2017. 艾比湖盐尘气溶胶光学特性卫星和地基遥感观测. 遥感学报, 21(5): 665-678 [DOI: 10.11834/jrs.20176394http://dx.doi.org/10.11834/jrs.20176394]

Zhang Z Y, Wu W L, Fan M, Wei J, Tan Y H and Wang Q. 2019. Evaluation of MAIAC aerosol retrievals over China. Atmospheric Environment, 202: 8-16 [DOI: 10.1016/j.atmosenv.2019.01.013http://dx.doi.org/10.1016/j.atmosenv.2019.01.013]

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