扫 描 看 全 文
马小雨, 陈正华, 宿鑫, 等. GF-4增强型地表反射率库支持法的气溶胶光学厚度反演[J]. 遥感学报, 2020,24(5):578-595.
Xiaoyu MA, Zhenghua CHEN, Xin SU, et al. GF-4 aerosol retrieval study of enhanced surface reflectance library support algorithm[J]. Journal of Remote Sensing(Chinese), 2020,24(5):578-595.
针对GF-4等国产卫星气溶胶光学厚度反演算法存在的地表反射率估计困难、云像元污染等问题，本文发展了一种增强型地表反射率库支持的气溶胶光学厚度反演方法，改进了云筛选与地表反射率确定方案，在考虑GF-4逐像元成像角度的情况下，使用6SV模型与MOD09-CMA数据对季度尺度上的GF-4 PMS传感器数据进行大气校正，提出了百分比最小值均值法建立地表反射率库，并据此建立了NDVI与红蓝反射率关系模型，根据地表反射率的分布特点，当NDVI小于0.2的时候使用地表反射率库估计地表反射率，而当NDVI大于0.2时，则使用NDVI来估计地表反射率。使用MOD04气溶胶模式时空分布确定气溶胶参数。在京津冀地区开展气溶胶光学厚度反演实验，使用Aeronet站点数据与MOD04产品对反演结果进行了对比验证，与Aeronet相关系数,R,为0.964，均方根误差RMSE为0.13，满足,,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372981&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372979&type=,18.88066673,3.21733332,的点多于78.9%，相关系数与均方根误差优于MODIS暗目标法产品，满足期望误差线的数量优于MODIS暗目标与深蓝算法产品。
The multispectral camera carried by the GF-4 satellite was featured by high spatial resolution and high frequency observations. It played an important role in atmospheric aerosol monitoring. The most two mature satellite Aerosol Optical Depth(AOD) retrieval algorithms were Dark Target algorithm(DT) and Deep Blue algorithm(DB).While the former one was limited in the low reflectivity area and 2.1,,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372985&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372983&type=,1.69333339,2.79399991,m wavelength band. An AOD inversion algorithm was developed in this work based on the enhanced surface reflectance library support algorithm by using GF-4 data.,The key question of AOD inversion were the estimate of surface reflectivity and the assumption of aerosol types. The MOD09-CMA data was applied to perform atmospheric correction with Second Simulation of Satellite Signal in the Solar Spectrum Vector(6SV) model for GF-4 data. To make more accurate, it was specified that no treatment be performed in the condition of cloud and high aerosol load. A quarter-period reflectance library for the GF-4 data was synthesized using the percentage minimum mean method. The surface reflectance library was reanalyzed to obtain the relationship model between NDVI and red, blue reflectivity. We used NDVI to determine surface reflectance when NDVI was greater than 0.2 and used static surface reflectance library to determine surface reflectance when NDVI was less than 0.2.The aerosol type parameters were determined by the MODIS global spatial-temporal distribution map in terms of aerosol types.,The algorithm was validated against two datasets: Aeronet dataset and MOD04 products. Statistical analysis of the validation results was based on the linear regression model using the goodness-of-fit indicators: correlation coefficient(,R,), Root Mean Squared Error(,,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372993&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372989&type=,8.72066689,2.20133328,),and Expected Error(,,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372997&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20372995&type=,4.06400013,2.11666679,). The accurate values in this works were derived with ,R,:0.964, RMSE:0.13, and the percentage of falling within ,,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20373003&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20373000&type=,17.61066628,2.79399991, was more than 78.9%.The ,,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20373007&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20373005&type=,2.20133328,2.11666679, and RMSE was better than the MODIS-DT product, which was slightly worse than the MODIS-DB product. Compared with the MODIS-DB and MODIS-DT algorithm, our algorithm fell more in the expected error line. The 6SV model was used to simulate the error. It was found that the pixel-by-pixel imaging angle can effectively reduce the error. The surface reflectivity library error was minimum in summer. The change in solar angle was suggested to be considered to build the surface reflectivity library in other seasons. In the mean time, the single aerosol model assumption was one cause of errors as well.
遥感GF-4地表反射率库气溶胶光学厚度（AOD）逐像元卫星角度6SV模型京津冀地区
remote sensingGF-4surface reflectivity libraryaerosol optical depth (AOD)Satellite-by-pixel angle6SV modelBeijing-Tianjin-Hebei area
Ångström A . 1929. On the atmospheric transmission of sun radiation and on dust in the air.GeografiskaAnnaler, 11(2): 156-166[DOI:10.1080/20014422.1929.11880498http://dx.doi.org/10.1080/20014422.1929.11880498 ]
Bilal M, Nichol J E, Bleiweiss M PandDubois D .2013. A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces. Remote Sensing of Environment, 136: 135-145 [DOI: 10.1016/j.rse.2013.04.014http://dx.doi.org/10.1016/j.rse.2013.04.014 ]
Chen H . 2013.The Characteristics of Aerosol Types over China and its Application in Remote Sensing.Beijing: University of Chinese Academy of Sciences
陈好.2013.中国气溶胶类型特性分析及其在遥感反演中的应用.北京:中国科学院大学
Deuzé J L, Goloub P, Herman M, MarchandA, Perry G, Susana S and Tanré D . 2000. Estimate of the aerosol properties over the ocean with POLDER. Journal of Geophysical Research: Atmospheres, 105(D12): 15329-15346 [DOI: 10.1029/2000JD900148http://dx.doi.org/10.1029/2000JD900148 ]
Diner D J, Martonchik J V, Kahn R A, PintyB, GobronN, Nelson D LandHolbenB N .2005. Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land. Remote Sensing of Environment, 94(2): 155-171 [DOI: 10.1016/j.rse.2004.09.009http://dx.doi.org/10.1016/j.rse.2004.09.009 ]
El-Fadel MandHashisho Z . 2001. Vehicular emissions in roadway tunnels: a critical review. Critical Reviews in Environmental Science and Technology, 31(2): 125-174 [DOI: 10.1080/20016491089190http://dx.doi.org/10.1080/20016491089190 ]
Fan X H . 2006. Retrieval of Aerosol Optical Properties Over Beijing from Polarized Signals of PARASOL.Beijing: Chinese Academy of Sciences
范学花. 2006. PARASOL卫星偏振信息遥感北京地区气溶胶光学特性的研究. 北京: 中国科学院大气物理研究所
Gao L, Ren T, Li C C, Yang D W, Shi G M and Mao J T . 2012. A retrieval of the atmospheric aerosol optical depth from MTSAT. ActaMeteorologicaSinica, 70(3): 598-608
高玲, 任通, 李成才, 杨东伟,石光明,毛节泰 . 2012. 利用静止卫星MTSAT反演大气气溶胶光学厚度. 气象学报, 70(3): 598-608 [DOI:10.11676/qxxb2012.049http://dx.doi.org/10.11676/qxxb2012.049 ]
Hsu N C, Tsay S C, King M D and Herman J R .2004. Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569 [DOI: 10.1109/TGRS.2004.824067http://dx.doi.org/10.1109/TGRS.2004.824067 ]
Hsu N C, Jeong M J, Bettenhausen C, Sayer A M, Hansell R, Seftor C S, Huang J and Tsay S C . 2013. Enhanced deep blue aerosol retrieval algorithm: the second generation. Journal of Geophysical Research: Atmospheres, 118(16): 9296-9315 [DOI: 10.1002/jgrd.50712http://dx.doi.org/10.1002/jgrd.50712 ]
Hsu N C, Tsay S C, King M D and Herman J R .2006. Deep blue retrievals of Asian aerosol properties during ACE-Asia. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3180-3195 [DOI: 10.1109/TGRS.2006.879540http://dx.doi.org/10.1109/TGRS.2006.879540 ]
Kaufman Y J, Tanré D, Remer L A, Vermote E F, Chu A and Holben B N .1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067 [DOI: 10.1029/96JD03988http://dx.doi.org/10.1029/96JD03988 ]
Levy R C, Remer L AandDubovik O . 2007a. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. Journal of Geophysical Research: Atmospheres, 112(D13): D13210[DOI: 10.1029/2006JD007815http://dx.doi.org/10.1029/2006JD007815 ]
Levy R C, Remer L A, Mattoo S, Vermote E F and Kaufman Y J . 2007b. Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of Geophysical Research: Atmospheres, 112(D13): D13211[DOI: 10.1029/2006JD007811http://dx.doi.org/10.1029/2006JD007811 ]
Li Z, Zhao X, Kahn R, Mishchenko M, Remer L, Lee K H, Wang M, Laszlo I, Nakajima T and Maring H . 2009. Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective. AnnalesGeophysicae, 27(7): 2755-2770 [DOI: 10.5194/angeo-27-2755-2009http://dx.doi.org/10.5194/angeo-27-2755-2009 ]
Lyapustin A, Wang Y, Laszlo I, Kahn I, Korkin S, Remer L, Levy R and Reid J S . 2011. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. Journal of Geophysical Research: Atmospheres, 116 (D 3: D03211 [DOI: 10.1029/2010JD014986http://dx.doi.org/10.1029/2010JD014986 ]
Mei L L . 2013. Quantitative Retrieval of Aerosol Optical Depth from Satellite Data. Beijing: University of Chinese Academy of Sciences
梅林露. 2013. 多源遥感卫星陆地气溶胶光学厚度反演建模. 北京: 中国科学院大学
Pan Z Q, Fu Q Y and Zhang H P .2008. Retrieval and Application of Band Mean Solar Irradiance of CBERS-02 CCD. Geo-Information Science, 10(1): 109-113
潘志强, 傅俏燕, 张浩平 . 2008. CBERS-02星CCD波段平均太阳辐照度反演及应用. 地球信息科学学报, 10(1): 109-113 [DOI:10.3969/j.issn.1560-8999.2008.01.018http://dx.doi.org/10.3969/j.issn.1560-8999.2008.01.018 ]
SayerAM, 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(14): 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” datasets, and usage recommendations. Journal of Geophysical Research: Atmospheres, 119(24): 13965-1398 9 [DOI: 10.1002/2014JD022453http://dx.doi.org/10.1002/2014JD022453 ]
She L, Mei L L, Xue Y, Che Y H and Guang J .2017. SAHARA: a simplified AtmospHeric correction AlgoRithm for Chinese gAofen data: 1. Aerosol algorithm. Remote Sensing, 9(3): 253[DOI: 10.3390/rs9030253http://dx.doi.org/10.3390/rs9030253 ]
Soler T and Eisemann D W .1994. Determination of look angles to geostationary communication satellites. Journal of Surveying Engineering, 120(3): 115-127 [DOI: 10.1061/(ASCE)0733-9453(1994)120:3(115)http://dx.doi.org/10.1061/(ASCE)0733-9453(1994)120:3(115)
Sun L . 2006. Remote Sensing ofAerosols over Urban Areas. Beijing: Institute of Remote Sensing Applications,Chinese Academy of Sciences
孙林. 2006. 城市地区大气气溶胶遥感反演研究. 北京: 中国科学院研究生院遥感应用研究所
Tang X Y, Zhang Y H and Shao M . 2006. Atmospheric Environmental Chemistry. 2nd ed. Beijing: Higher Education Press
唐孝炎, 张远航, 邵敏. 2006.大气环境化学. 2版. 北京:高等教育出版社
Tanré D, Deschamps P Y, Devaux C and Herman M .1988. Estimation of Saharan aerosol optical thickness from blurring effects in thematic mapper data. Journal of Geophysical Research: Atmospheres, 93(D12): 15955-15964 [DOI: 10.1029/JD093iD12p15955http://dx.doi.org/10.1029/JD093iD12p15955 ]
Tanré D, Kaufman Y J, Herman M and Mattoo S .1997. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. Journal of Geophysical Research: Atmospheres, 102(D14): 16971-16988 [DOI: 10.1029/96JD03437http://dx.doi.org/10.1029/96JD03437 ]
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 ]
Tong X D . 2016. Development of China high-resolution earth observation system. Journal of Remote Sensing, 20(5): 775-780
童旭东 . 2016. 中国高分辨率对地观测系统重大专项建设进展. 遥感学报, 20(5): 775-780 [DOI: 10.11834/jrs.20166302http://dx.doi.org/10.11834/jrs.20166302 ]
Vermote E F, Tanré D, Deuzé J L, Herman M and Morcette J J .1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675-686 [DOI: 10.1109/36.581987http://dx.doi.org/10.1109/36.581987 ]
Wang D Z and He H Y .2017. Observation capability and application prospect of GF-4 satellite. Spacecraft Recovery and Remote Sensing, 38(1): 98-106
王殿中, 何红艳 . 2017. “高分四号”卫星观测能力与应用前景分析. 航天返回与遥感, 38(1): 98-106 [DOI: 10.3969/j.issn.1009-8518.2017.01.013http://dx.doi.org/10.3969/j.issn.1009-8518.2017.01.013 ]
Wang Z T . 2008. Multi-Angle Polarization Remote Sensing Study of Land Aerosols. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences
王中挺. 陆地气溶胶的多角度偏振遥感研究[D]. 中国科学院遥感应用研究所, 2008.
Wang Z T, Li Q, Wang Q, Li S S, Chen L F, Zhou C Y, Zhang L J and Xu Y J . 2012. HJ-1 terrestrial aerosol data retrieval using deep blue algorithm. Journal of Remote Sensing, 16(3): 596-610
王中挺, 厉青, 王桥, 李莘莘,陈良富,周春艳,张丽娟,徐拥军 . 2012. 利用深蓝算法从HJ-1数据反演陆地气溶胶. 遥感学报, 16(3): 596-610 [DOI:10.11834/jrs.20121148http://dx.doi.org/10.11834/jrs.20121148 ]
Wang Z T, Zhang Y H, Yuan S Y, Zhao S H, Zhou C Y, Chen H and Ma P F . 2016. The aerosol monitoring over Beijing-Tianjin-Hebei region from GF-4 data. Environment and Sustainable Development, 41(5): 113-116
王中挺, 张玉环, 袁淑云, 赵少华,周春艳,陈辉,马鹏飞 . 2016. 利用高分四号数据监测“京津冀”地区陆地气溶胶. 环境与可持续发展, 41(5): 113-116 [DOI:10.3969/j.issn.1673-288X.2016.05.033http://dx.doi.org/10.3969/j.issn.1673-288X.2016.05.033 ]
Zhang C C and Zhou W X .1995. Atmospheric Aerosol Tutorial. Beijing: Meteorological Press
章澄昌, 周文贤. 1995. 大气气溶胶教程. 北京: 气象出版社
Zhang L . 2016. Retrieval of Aerosol Optical Depth using Domestic Remote Sensing Data over Beijing.Shanghai: East China Normal University
张璐. 2016. 基于国产遥感卫星数据的北京市气溶胶光学厚度反演研究. 上海: 华东师范大学
Zhang W H . 2016. Remote Sensing Inversion of Aerosol Characteristics in East Asia. Beijing: University of Chinese Academy of Sciences
张文豪. 2016. 东亚地区高时相气溶胶特性遥感反演研究. 北京: 中国科学院大学
Zhao T X P, Stowe L L, Smirnov A, Crosby D, Sapper J and McClain C R .2002. Development of a global validation package for satellite oceanic aerosol optical thickness retrieval based on AERONET observations and its application to NOAA/NESDIS operational aerosol retrievals.Journal of the Atmospheric Sciences, 59(3): 294-312 [DOI: 10.1175/1520-0469(2002)059<0294:DOAGVP>2.0.CO;2http://dx.doi.org/10.1175/1520-0469(2002)059<0294:DOAGVP>2.0.CO;2 ]
相关作者
相关机构
京公网安备11010802024621
