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遥感技术与应用  2020, Vol. 35 Issue (1): 163-173    DOI: 10.11873/j.issn.1004-0323.2020.1.0163
模型与反演     
基于GOCI卫星的大气细颗粒物PM2.5的遥感反演及其时空分布规律研究
李志鹏1(),陈健2()
1. 上海市测绘院,上海 200063
2. 南京信息工程大学 遥感与测绘工程学院,江苏 南京 210044
Remote Sensing Retrieval of Atmospheric Fine Particle PM2.5 based on GOCI Satellite and Its Temporal and Spatial Distribution
Zhipeng Li1(),Jian Chen2()
1. Shanghai Institute of Surveying and Mapping, Shanghai 200063, China
2. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Techology, Nanjing 210044, China
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摘要:

随着我国经济的快速发展和城市化进程的加快,大气细颗粒物PM2.5已经成为影响我国大气环境污染的主要因素之一。利用静止卫星数据可以获取大范围的面状PM2.5信息,为我国大气环境的监测、治理、预测等提供了不可替代的数据源。以江苏省为研究区,利用静止卫星GOCI数据,在反演逐时气溶胶光学厚度(AOD)的基础上,结合气象因子,利用多元统计分析进行了研究区PM2.5的遥感反演研究。结果表明:基于AOD的多元统计模型,在估计的PM2.5浓度和观测值之间表现出良好的一致性,拟合度R 2为0.665 2。在对AOD进行湿度订正后得到的dry AOD进行多元统计建模,预测的PM2.5浓度与观测值之间的拟合度R 2达到了0.702 6,证明了经过湿度订正后的“干”AOD与PM2.5之间建立的关系更加可靠。使用GOCI反演的AOD计算PM2.5浓度,在空间分辨率和时间分辨率上充分体现了GOCI作为静止卫星监测PM2.5的优势。在空间分分辨率上,基于GOCI卫星获取AOD的空间分辨率为500 m,优于MODIS 10 km的AOD产品;时间分辨率上,基于GOCI获取AOD实现每日自9:00~16:00逐小时监测,优于MODIS每日两次的AOD产品。

关键词: GOCI卫星遥感PM2.5气溶胶光学厚度江苏省    
Abstract:

With the rapid development of China's economy and the acceleration of urbanization, PM2.5 has become one of the major factors affecting atmospheric environmental pollution in China. The use of geostationary satellite data can obtain a wide range of regional PM2.5information, providing irreplaceable data sources for China's atmospheric environment monitoring, control, and forecasting. This paper uses the geostationary satellite GOCI data, based on Aerosol Optical Depth (AOD) retrieveal, combined with meteorological factors, and uses multivariate statistical analysis to study the remote sensing retrieval of PM2.5 in the study area. The results show that the multivariate statistical model based on AOD shows a good agreement between the estimated PM2.5 concentration and the observed values, and the fitting degreeR 2 is 0.665 2. After multivariate statistical modeling of dry AOD obtained after moisture correction of AOD, the fitting degree R2 between the predicted concentration of PM2.5 and the observed value reached 0.702 6, which proved the relationship established betweenthe “dry” AOD after the humidity correction and PM2.5 is more reliable.The use of GOCI-retrieved AOD to calculate PM2.5 concentration fully reflects the advantages of GOCI as a geostationary satellite in spatial resolution and temporal resolution. In terms of spatial resolution, the spatial resolution of AOD based on GOCI satellite reachs to 500 meters, which is better than MODIS 10 km AOD product.In terms of temporal resolution,hourly AOD monitoring from 9:00 to 16:00 based on GOCI can be obtained,which is better than MODIS twice daily AOD products.

Key words: GOCI    Remote sensing    PM2.5    Aerosol optical thickness    Jiangsu Province
收稿日期: 2018-10-11 出版日期: 2020-04-01
ZTFLH:  X513  
通讯作者: 陈健     E-mail: rserlzp@aliyun.com;21117592@qq.com
作者简介: 李志鹏(1996-),男,江苏淮安人,助理工程师,主要从事卫星遥感、气溶胶反演及城市测绘研究。E?mail:rserlzp@aliyun.com
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引用本文:

李志鹏,陈健. 基于GOCI卫星的大气细颗粒物PM2.5的遥感反演及其时空分布规律研究[J]. 遥感技术与应用, 2020, 35(1): 163-173.

Zhipeng Li,Jian Chen. Remote Sensing Retrieval of Atmospheric Fine Particle PM2.5 based on GOCI Satellite and Its Temporal and Spatial Distribution. Remote Sensing Technology and Application, 2020, 35(1): 163-173.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0163        http://www.rsta.ac.cn/CN/Y2020/V35/I1/163

图1  研究区域PM2.5站点图
波段 波段中心/nm 标准辐亮度/W·m-2·μm-1·sr-1 最大辐亮度/W·m-2·μm-1·sr-1
B1 412 100 150.0
B2 443 92.5 145.8
B3 490 72.2 115.5
B4 555 55.3 85.2
B5 660 32.0 58.3
B6 680 27.1 46.2
B7 745 17.7 33.0
B8 865 12.0 23.4
表1  GOCI波段及参数信息
图2  GOCI气溶胶光学厚度反演处理流程图
图5  方法1预测结果
图3  PM2.5反演技术流程图
图4  GOCI反演的AOD和PM2.5质量浓度的散点图,以及经过湿度订正的“干”AOD和PM2.5质量浓度的散点图
PM2.5
AOD Pearson 相关性 0.701
显著性(双侧) 0*
N 116
Dry AOD Pearson 相关性 0.727
显著性(双侧) 0*
N 116
T2 Pearson 相关性 0.740
显著性(双侧) 0*
N 116
表2  AOD、dry AOD、温度与PM2.5的相关性分析
图6  方法2预测结果
图7  江苏省PM2.5预测图
图8  PM2.5质量浓度图与夜间灯光指数图的对照分析
1 Mu Quan , Zhang Shiqiu .Assessment of the Trend of Heavy PM2.5 Pollution Days and Economic Loss of Health Effects during 2001~2013[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2015,51(4):694-706.穆泉,张世秋.中国 2001~2013年PM2.5重污染的历史变化与健康影响的经济损失评估[J].北京大学学报,2015,51(4):694-706.
2 Dockery,D W .Heath Eeffects of Particulate Air Pollution[J].Annals of Epidemiology,2009,19(4):257-263.
3 Lim S S , Vos T , Flaxman A D ,et al .A Comparative Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor Clusters in 21 Regions, 1990-2010: A Systematic Analysis for the Global Burden of Disease Study 2010[J]Lancet,2012,380(9859):2224-2260.
4 Lee M , Kloog I , Chudnovsky A ,et al .Spatiotemporal Prediction of Fine Particulate Matter Using High-resolution Satellite Images in the Southeastern U.S. 2003~2011[J].Exposure Science and Environmental Epidemiology,2016,26(4):377-384.
5 Sinha P R , Gupta P , Kaskaoutis D G ,et al .Estimation of Particulate Matter from Satellite- and Ground-based Observations over Hyderabad, India. Int[J].Remote Sensing,2015,36(24):6192-6213.
6 Mordukhovich I , Coull B , Kloog I ,et al .Exposure to Sub-chronic and Long-term Particulate Air Pollution and Heart Rate Variability in an Elderly Cohort: The Normative Aging Study[J].Environmental Health,2015,14(87):1-10.
7 Van Donkelaar A , Martin R V , Brauer M ,et al .Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-based Aerosol Optical Depth: Development and Application[J].Environmental Health Perspectives,2010,118(6):847-855.
8 Li C C , Mao J T , Liu Q H ,et al .Application of MODIS Aerosol Sroducts in the Air Pollution in Beijing Research[J].Science in China Series D: Earth Sciences,2005,35(Suppl):177-186.
9 Chu D A , Kaufman Y J , Zibordi G ,et al .Global Monitoring of Air Pollution over Land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS)[J].Geophysical Research,2003,108(21):ACH4-1–ACH4-18.
10 Slater J F , Dibb J E , Campbell J W ,et al .Physical and Chemical Properties of Surface and Column Aerosols at a Rural New England Site during MODIS Overpass[J].Remote Sensing of Environment,2004,92(2):173-180.
11 Engle-Cox J A , Holloman C H , Coutant B W .Qualitative and Auantitative Evaluation of MODIS Satellite Sensor Data for Regional and Urban Scale Air Quality[J].Atmospheric Environment,2004,38(16):2495-2509.
12 Wang J , Christopher A .Intercomparison between Satellite-derived Aerosol Optical Thickness and PM2.5 Mass: Implications for Air Quality Studies[J].Geophysical Research Letters,2003,30(21):1-4.
13 Chen H , Li Q , Wang Z ,et al .Utilization of MERSI and MODIS Data to Monitor PM2.5 Concentration in Beijing-Tianjin-Hebei and Its Surrounding Areas[J].Journal of Remote Sensing,2018,22(5):822-832.陈辉,厉青,王中挺, 等 .MERSI和MODIS卫星监测京津冀及周边地区PM2.5浓度[J].遥感学报,2018,22(5):822-832.
14 Peng Wei , Jiang Hong , Xiao Zhongyong ,et al .Influence of Land Cover on Aerosol Optical Thickness in theYangtze River Delta Region[J].Environmental Science & Technology,2014,37(6):177-190.彭威,江宏,肖钟湧, 等 .长三角地区土地覆盖对气溶胶光学厚度的影响[J].环境科学与技术,2014,37(6):177-190.
15 Luo Yunfeng , Daren Lü , Zhou Xiuji ,et al .Analyses on the Spatial Distribution of Aerosol Optical Depth over China in Recent 30 Years[J].Chinese Journal of Atmospheric Sciences (in Chinese),2002,26(6):721-730.罗云峰,吕达仁,周秀骥, 等 .30年来我国大气气溶胶光学厚度平均分布特征分析[J].大气科学,2002,26(6):722-730.
16 Wang Jiming , Cao Yanhua , Ye Xiaofeng ,et al .A Study on the Characteristics of Aerosol Chemical Composition and Its Numerical Modeling in East Asia[J].Acta Agriculturae Universitatis Jiangxiensis,2010,32(1):191-198.王吉明,曹艳华,叶小峰, 等 .东亚地区气溶胶化学成分特性分析及数值模拟研究[J].江西农业大学学报,2010,32(1):191-198.
17 Chen Jian , Zhou Jie , Li Yawen .Retrieving Aerosol Optical Depth over Land based on GOCI Data Onboard Geostationary Satellite[J].Remote Sensing Technology and Application,2017,32(6):1040-1047.陈健,周杰,李雅雯.基于静止卫星GOCI数据的陆地上空气溶胶光学非得度遥感反演[J].遥感技术与应用,2017,32(6):1040-1047.
18 Cheng Chen .Aerosol Optical Thickness Retrieval and Spatial-temporal Variation Analysis in Nanjing based on HJ-1 Satellite[D].Nanjing:Nanjing Unniversity of Information Science and Technology,2014.程晨.基于HJ-1卫星的南京气溶胶光学厚度反演和时空变化分析[D].南京:南京信息工程大学,2014.
19 Remer L A , Kaufman Y J , Tanré D ,et al .The MODIS Aerosol Algorithm, Products, and Validation[J].Journal of the Atmospheric Sciences,2005,62(4):947-973.
20 Vermote E F , Kotchenova S Y .MOD09 User’s Guide [EB/OL],,2018.
21 Zhou Jie .Remote Sensing Study of Aerosol Direct Radiation Force in the Yangtze River Delta[D].Nanjing:Nanjing Unniversity of Information Science and Technology,2015.周杰.长三角地区气溶胶直接辐射强迫遥感研究[D].南京: 南京信息工程大学,2015.
22 Kizhner L I , Bart A A , Nahtigalova D P .Using the Nnumerical WRF Model for the Prediction of Weather Parameters in Tomsk Region[J].Bio Clim Land,2013,1:29-35.
23 Kloog I , Koutrakis P , Coull B A ,et al .Assessing Temporally and Spatially Resolved PM 2.5 Exposures for Epidemiological Studies Using Satellite Aerosol Optical Depth Measurements[J].Atmospheric Environment.2011,45:6267-6275.
24 Wang Xinqiang , Yang Shizhi , Zhu Yonghao .Aerosol Optical Thickness Retrieval over Land from MODIS Data based on the Inversion of the 6S Model[J].Chinese Journal of Quantum Electronics,2003,20(5):629-634.王新强,杨世植,朱永豪.基于6S模型从MODIS图像反演陆地上空大气气溶胶光学厚度[J].量子电子学报,2003,20(5):629-634.
25 Liu X G .Monitoring and Modelling Research on the Aerosol Hygroscopicity—Taking Beijing, Pearl River Delta for example[D].Beijing:Peking University,2008.刘新罡,大气气深吸湿性质观测、模型研究—以北京、珠江三角洲地区为例[D].北京:北京大学,2008.
26 Pan X L .Observation Study of Atmospheric Aerosol Scattering Characteristics as A Function of Relative Humidity[D].Beijing:Chinese Academy of Meteorological Sciences,2007.潘小乐.相对湿度对气溶胶散射特征影响的观测研究[D].北京:中国气象科学研究院,2007.
27 Wang Z F .Research in Estimating PM Concentration Using Satellite Remote Sensing[D].Beijing:Institute of Remote Sensing Applications,Chinese Academy of Sciences,2010.王子峰.卫星遥感估算近地面颗粒物浓度的算法研究[D].北京:中国科学院遥感应用研究所,2010.
28 Hansen J E , Travis L D .Light Scattering in Planetary Atmospheres[J].Space Science Reviews,1974,16(4):527–610.
29 Wang Z F , Chen L F , Tao J H ,et al .Satellite-based Estimation of Regional Particulate Matter (PM) in Beijing Using Vertical-and-RH Correcting Method[J].Remote Sensing of Environment,2010,114(1):50-63.
30 He X , Deng Z Z , Li C C .Application of MODIS AOD in Surface PM10 Evaluation[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2010,46(2):78-184.
31 Liu Y , Sarnat J A , Kilaru V ,et al .Estimating Ground-level PM 2.5 in the Eastern United States Using Satellite Remote Sensing[J].Environmental Science and Technology,2005,39(9):3269-3278.
32 Gupta P , Christopher S A .Seven Year Particulate Matter Air Quality Assessment from Surface and Satellite Measurements[J].Atmopheric Chemistry and Physics.2008,8(12):3311-3324.
33 Sutton P C , Costanza R .Global Estimates of Market and Non-market Values derived from Nighttime Satellite Imagery, Land Cover, and Ecosystem Service Valuation[J].Ecological Economics,2002,41(1):509-527.
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