Review of the urban aerosol retrieval research based on high-resolution images

doi:10.6046/gtzyyg.2016.03.01

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Abstract

This paper describes the influence of aerosols on global climate change and air pollution situation, points out the importance of using high-resolution satellite images for urban aerosols inversion, elaborates the research status of the satellite remote sensing retrieval of aerosol, and briefly introduces the principle of satellite remote sensing retrieval of aerosol, and the single channel and multi -channel method, the contrast method based on differences in the spatial structure, the inversion method based on multi-angle data, the inversion method based on polarization data and the inversion method based on laser radar satellite data, which constitute five kinds of satellite remote sensing inversion method widely used nowadays. According to the inversion method and the difficulties of the urban aerosol inversion, the inversion of aerosol in urban areas is analyzed and summarized, with a detailed discussion on the shortcomings of current inversion methods based on high-resolution images of urban areas and a forecast of breakthrough points and the solutions of the existing problems.

Keywords vector data image classification object SVM temporal feature transition probability

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Issue Date: 01 July 2016

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	LI Liang
	YING Guowei
	WEN Xuehu
	HE Xin

Cite this article:

LI Liang,YING Guowei,WEN Xuehu, et al. Review of the urban aerosol retrieval research based on high-resolution images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 1-6.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.03.01 OR https://www.gtzyyg.com/EN/Y2016/V28/I3/1

[1] 毛节泰,张军华,王美华.中国大气气溶胶研究综述[J].气象学报,2002,60(5):625-634. Mao J T,Zhang J H,Wang M H.Summary comment on research of atmospheric aerosol in China[J].Journal of Tropical Meteorology,2002,60(5):625-634.
[2] 李成林.城市大气污染的定量遥感监测方法研究[D].兰州:兰州大学,2012. Li C L.Remote Sensing of Aerosols Over Urban Areas[D].Lanzhou:Lanzhou University,2012.
[3] Diner D J,Abdou W A,Bruegge C J.MISR aerosol optical depth retrievals over southern Africa during the SAFARI-2000 dry season campaign[J].Geophysical Research Letters,2001,28(16):3127-3130.
[4] Vachon F,Royer A,Aube M,et al.Remote sensing of aerosols over North American land surfaces from POLDER and MODIS measurements[J].Atmospheric Environment,2004,38(21):3501-3515.
[5] Thomason L W,Pitts M C,Winker D M.CALIPSO observations of stratospheric aerosols:A preliminary assessment[J].Atmospheric Chemistry and Physics,2007,7(20):5283-5290.
[6] Hsu N C,Tsay S C.Aerosol properties over bright-reflecting source regions[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):557-569.
[7] Hsu N C,Jeong M J,Bettenhausen C.Enhanced deep blue aerosol retrieval algorithm:The second generation[J].Journal of Geophysical Research:Atmospheres,2013(118):9296-9315.
[8] 赵柏林,俞小鼎.海洋大气气溶胶光学厚度的卫星遥感研究[J].科学通报,1986(31):1645-1649. Zhao B L,Yu X D.Satellite remote sensing research of Marine atmospheric aerosol optical thickness[J].Chinese Science Bulletin,1986(31):1645-1649.
[9] 毛节泰,李成才,张军华,等.MODIS卫星遥感北京地区气溶胶光学厚度及与地面光度计遥感的对比[J].应用气象学报,2002,13(特刊):127-135. Mao J T,Li C C,Zhang J H,et al.The comparison of remote sensing aerosol optical depth from MODIS data and ground sun-photometer observations[J].Quarterly Journal of Applied Meteorology,2002,13(suppl):127-135.
[10] Huang J,Minnis P,Yi Y,et al.Summer dust aerosols detected from CALIPSO over the Tibetan Plateau[J].Geophysical Research Letters,2007,34(18):1-5.
[11] 王磊.基于AATSR数据的双角度气溶胶反演研究[D].北京:中国气象科学研究院,2011. Wang L.Dual angle aerosol inversion based on AATSR data[D].Beijing:Chinese Academy of Meteorological Sciences,2011.
[12] 王中挺,厉青,王桥,等.利用深蓝算法从HJ-1数据反演陆地气溶胶[J].遥感学报,2012,16(3):596-610. Wang Z T,Li Q,Wang Q,et al.HJ-1 terrestrial aerosol data retrieval using deep blue algorithm[J].Journal of Remote Sensing,2012,16(3):596-610.
[13] 李莘莘,陈良富,陶金花,等.城市与冬季北方亮目标地区气溶胶光学厚度反演[J].中国科学:地球科学,2012,42(8):1253-1263. Li S S,Chen L F,Tao J H,et al.Retrieval of aerosol optical depth over bright targets in the urban areas of North China during winter[J].Sci China Earth Sci,2012,42(8):1253-1263.
[14] 宋薇.利用扩展暗像元法和V5.2算法反演兰州地区气溶胶光学厚[D].兰州:兰州大学,2007. Song W.Retrieval of aerosol optical depth by extended dark target method and V5.2 algorithm over Lanzhou areas[D].Lanzhou:Lanzhou University,2007
[15] Herman J R,Celarier E A.Earth surface reflectivity climatology at 340~380 nm from TOMS data[J].Journal of Geophysical Research,1997,102(D23):22801-28003.
[16] 胡引翠.气溶胶多尺度定量遥感监测及其网格计算研究[D].北京:中国科学院遥感应用研究所,2006. Hu Y C.Multi-spatial scale aerosol distribution monitoring using remote sensing technique based on grid platform[D].Beijing:Institute of Remote Sensing Applications Chinese Academy of Sciences,2006.
[17] Flowerdew R J,Haigh J D.An approximation to improve accuracy in the derivation of surface reflectance from multi-look satellite radiometers[J].Geophysical Research Letters,1995(23):1693-1696.
[18] 孙夏,赵慧洁.基于POLDER数据反演陆地上空气溶胶光学特性[J].光学学报,2009,29(7):1772-1777. Sun X,Zhao H J.Retrieval algorithm for optical parameters of aerosol over land surface from POLDER data[J].Acta Optica Sinica,2009,29(7):1772-1777.
[19] 段民征,吕达仁.利用多角度POLDER偏振资料实现陆地上空大气气溶胶光学厚度和地表反照率的同时反演II.实例分析[J].大气科学,2008,32(1):27-35. Duan M Z,Lyu D R.Simultaneously retrieving aerosol optical depth and surface albedo over land from polder's multi-angle polarized measurement.II:A case study[J].Chinese Journal of Atmospheric Sciences,2008,32(1):27-35.
[20] 于娜娜.气溶胶/云星载激光雷达数据反演算法初步研究[D].青岛:中国海洋大学,2012. Yu N N.Cloud-Aerosol satellite borne lidar data retrieval algorithms' preliminary study[D].Qingdao:Ocean university of China,2012.

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