华东地区MODIS与OMI气溶胶光学厚度数据融合

doi:10.3724/SP.J.1047.2015.01224

地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (10): 1224-1233.doi: 10.3724/SP.J.1047.2015.01224

华东地区MODIS与OMI气溶胶光学厚度数据融合

李龙^1,²(), 施润和^1,^2,^3*(), 张璐^1,², 张颉⁴, 周聪^1,², 徐彦平⁵, 高炜^1,^2,³

1. 华东师范大学地理信息科学教育部重点实验室,上海 200241
2. 华东师范大学环境遥感与数据同化联合实验室,上海 200241
3. 华东师范大学、美国科罗拉多州立大学中美新能源与环境联合研究院,上海 200062
4. 佛山市气象局,佛山 528000
5. 开县气象局,开县 405499

收稿日期:2014-12-24 修回日期:2015-02-26 出版日期:2015-10-10 发布日期:2015-10-19
作者简介:
作者简介：李龙(1988-),男,硕士生,主要从事大气与环境遥感方面研究。E-mail: lennonrs@126.com
基金资助:
上海市科委重点支撑项目(13231203804);国家自然科学基金项目(41201358)

Data Fusion of MODIS AOD and OMIAOD over East China Using Universal Kriging

LI Long^1,²(), SHI Runhe^1,^2,^3,^*(), ZHANG Lu^1,², ZHANG Jie⁴, ZHOU Cong^1,², XU Yanping⁵, GAO Wei^1,^2,³

1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
2. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE, Shanghai 200241, China
3. Joint Research Institute for New Energy and the Environment, East China Normal University and Colorado State University, Shanghai 200062,China
4. Foshan Bureau of Meteorology, Foshan 528000, China
5. Kai xian Bureau of meterological, kai xian 405499, China

Received:2014-12-24 Revised:2015-02-26 Online:2015-10-10 Published:2015-10-19
Contact: SHI Runhe E-mail:lennonrs@126.com;rhshi@geo.ecnu.edu.cn
About author:
*The author: CHEN Nan, E-mail:fjcn99@163.com

摘要/Abstract

摘要：

气溶胶光学厚度（AOD）表征气溶胶对光的衰减作用,体现大气混浊度或大气中气溶胶总含量,其卫星产品是研究近年来不断恶化的大气环境与空气质量的良好数据源。AOD卫星产品种类较多,但数据存在较大的不确定性;气溶胶全球监测网（AERONET）的地基数据精度高,但空间覆盖度较差。泛克里金法（UK）能在数据融合过程中更多地考虑描述对象的空间相关性,并且简单易行、结果可靠。因此,本文采用该方法,结合二次多项式波段插值法和回归分析方法,在AERONET AOD数据的基础上,对2008年11月华东地区臭氧监测仪（OMI）和中分辨率成像光谱仪（MODIS）的AOD产品进行了融合。结果表明：二次多项式的AOD波段插值方法,能提供比Angstrom波长指数法更为精准的AOD插值结果;AOD融合产品的空间分辨率高于OMI AOD,覆盖率大于OMIAOD和MODIS AOD,且其精度优于这2种AOD卫星产品;融合产品图显示,2008年11月,华东地区的AOD总体呈现南低北高的趋势,高值区主要分布在长江三角洲部分地区、安徽东北部、苏鲁交界处,以及山东西部;低值区主要为江苏以南大部。相比于前人研究,本文证实了AERONET AOD站点数据少、融合的数据源（卫星AOD产品）过境时间不一致的情况下,UK方法仍然有效。本文提出的融合系统,可为相关研究提供空间覆盖更全、精度更高的AOD数据。

关键词: 泛克里金法, AOD, 数据融合, 华东地区

Abstract:

Aerosol optical thickness (AOD) can represent the attenuation of solar radiation caused by aerosol, and reflect the atmospheric turbidity or air quality conditions as one of the most important parameters. It is an excellent data source for atmosphere and air quality studies as the atomosphere environment is deteriorating in recent years. Nowadays, there are many kinds of AOD satellite products with good spatial coverage which can be employed to atmospheric environmental researches and the related studies. However, the accuracy and certainty of these satellite products can not meet the increasing demands. Fortunately, the AOD retrieved from AErosol RObotic NETwork (AERONET) exhibits better quality, although their spatial representativeness are localized. With the help of quadratic polynomial interpolation and regression analysis methods, we employed the universal kriging method to integrate both types of AOD products (satellite AOD and AERONET AOD) for analyzing Eastern China in November 2008. Results showed that the quadratic polynomial interpolation method is much better than Angstrom on band interpolation in this study. The comparison between AERONET AOD products and satellite AOD products showed that the fused results (integrated with AOD products) retrieved from universal kriging data fusion method presented a better spatial resolution, wider coverage and higher accuracy. For East China, AOD values in the northern part are higher than the southern part in November 2008; for Yangtze River Delta region, the northeastern of Anhui and the eastern part of Shanxi hold higher AOD values, while the AOD values in southern part of Jiangsu are lower. In addition, this paper has confirmed that the universal kriging data fusion method can provide good fused AOD products in the cases that AERONET AOD data is inadequate and the transit time of different sensors is asynchronous. The AOD data fusion system proposed in this paper can provide better AOD products for relevant scientific researches.

Key words: universal kriging, AOD, data fusion, East China

李龙, 施润和, 张璐, 张颉, 周聪, 徐彦平, 高炜. 华东地区MODIS与OMI气溶胶光学厚度数据融合[J]. 地球信息科学学报, 2015, 17(10): 1224-1233.

LI Long,SHI Runhe,ZHANG Lu,ZHANG Jie,ZHOU Cong,XU Yanping,GAO Wei. Data Fusion of MODIS AOD and OMIAOD over East China Using Universal Kriging[J]. Journal of Geo-information Science, 2015, 17(10): 1224-1233.

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参考文献 34

[1]	Andreae M O.Climatic effects of changing atmospheric aerosol levels[J]. World survey of climatology, 1995,16:347-398.
[2]	罗宇翔,陈娟,郑小波.近10年中国大陆MODIS遥感气溶胶光学厚度特征[J].生态环境学报,2012,21(5):876-883.
[3]	Hutchison K D, Faruqui S J, Smith S.Improving correlations between MODIS aerosol optical thickness and ground-based PM2.5 observations through 3D spatial analyses[J]. Atmospheric Environment, 2008,42(3):530-543.
[4]	Hutchison K D, Smith S, Faruqui S J.Correlating MODIS aerosol optical thickness data with ground-based PM2.5 observations across Texas for use in a real-time air quality prediction system[J]. Atmospheric Environment, 2005,39(37):7190-7203.
[5]	Schaap M, Apituley A, Timmermans R M A, et al. Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands[J]. Atmospheric Chemistry and Physics, 2009,9(3):909-925.
[6]	Yao L, Lu N, Jiang S.Artificial neural network (ANN) for multi-source PM2.5 Estimation using surface, MODIS, and meteorological data[C]. 2012 IEEE International Conference on Biomedical Engineering and Biotechnology, 2012:1228-1231.
[7]	Zhao Q, Gao W, Xiang W, et al.Analysis of air quality variability in Shanghai using AOD and API data in the recent decade[J]. Frontiers of Earth Science, 2013,7(2):1-10.
[8]	Gupta P, Patadia F, Christopher S A.Multisensor data product fusion for aerosol research[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(5):1407-1415.
[9]	Nirala M.Technical Note: Multi-sensor data fusion of aerosol optical thickness[J]. International Journal of Remote Sensing, 2008,29(7):2127-2136.
[10]	Xu Q, Obradovic Z, Han B, et al.Improving aerosol retrieval accuracy by integrating AERONET, MISR and MODIS data[C]. 8th IEEE International Conference on Information Fusion, 2005:1-7.
[11]	Chatterjee A, Michalak A M, Kahn R A, et al.A geostatistical data fusion technique for merging remote sensing and ground-based observations of aerosol optical thickness[J]. Journal of Geophysical Research: Atmospheres (1984-2012), 2010,115(D20):898-907.
[12]	Nguyen H, Cressie N, Braverman A.Spatial statistical data fusion for remote sensing applications[J]. Journal of the American Statistical Association, 2012,107(499):1004-1018.
[13]	Jinnagara Puttaswamy S, Nguyen H M, Braverman A, et al.Statistical data fusion of multi-sensor AOD over the Continental United States[J]. Geocarto International, 2014,29(1):1-17.
[14]	Agterberg F P.Autocorrelation functions in geology[A]. In Merriam D F(ed.). Geostatistics[M]. Springer, 1970:113-141.
[15]	Bretherton F P, Davis R E, Fandry C B.A technique for objective analysis and design of oceanographic experiments applied to MODE-73[J]. Deep Sea Research and Oceanographic Abstracts, 1976,23(7):559-582.
[16]	Guzzi R, Ballista G, Nicolantonio W D, et al.Aerosol maps from GOME data[J]. Atmospheric environment, 2001,35(30):5079-5091.
[17]	Bergamasco A, Teatini P, Carbognin L.Confronto critico trakriging e analisi oggettiva[J]. Il Nuovo Cimento C, 1993,16(3):289-302.
[18]	林忠辉,莫兴国,李宏轩.中国陆地区域气象要素空间插值[J].地理学报,2002,57(1):47-56.
[19]	李军龙,张剑,张丛,等.气象要素空间插值方法的比较分析[J].草业科学,2006,23(8):6-11.
[20]	Wang J F, Li L F, Christakos G.Sampling and Kriging spatial means: Efficiency and conditions[J]. Sensors, 2009,9(7):5224-5240.
[21]	Hao W, Chang X.Comparison of Spatial Interpolation Methods for Precipitation in Ningxia, China[J]. International Journal of Science and Research,2013,2(8):181-184.
[22]	Ge J M, Su J, Fu Q, et al.Dust aerosol forward scattering effects on ground-based aerosol optical depth retrievals[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2011,112(2):310-319.
[23]	Xia X A, Chen H B, Wang P C.Validation of MODIS aerosol retrievals and evaluation of potential cloud contamination in East Asia[J]. Journal of Environmental Sciences, 2004,16(5):832-837.
[24]	Dubovik O, Smirnov A, Holben B N, et al.Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements[J]. Journal of Geophysical Research: Atmospheres (1984-2012), 2000,105(D8):9791-9806.
[25]	King M D, Kaufman Y J, Menzel W P, et al.Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS)[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992,30(1):2-27.
[26]	Levelt P F, Hilsenrath E, Leppelmeier G W, et al.Science objectives of the ozone monitoring instrument[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,44(5):1199-1208.
[27]	Livingston J M, Redemann J, Russell P B, et al.Comparison of aerosol optical depths from the Ozone Monitoring Instrument (OMI) on Aura with results from airborne sunphotometry, other space and ground measurements during MILAGRO/INTEX-B[J]. Atmospheric Chemistry and Physics, 2009,9(18):6743-6765.
[28]	Eck T F, Holben B N, Reid J S, et al.Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols[J]. Journal of Geophysical Research: Atmospheres (1984-2012), 1999,104(D24):31333-31349.
[29]	Junge C.The size distribution and aging of natural aerosols as determined from electrical and optical data on the atmosphere[J]. Journal of Meteorology, 1955,12(1):13-25.
[30]	King M D, Byrne D M.A method for inferring total ozone content from the spectral variation of total optical depth obtained with a solar radiometer[J]. Journal of the Atmospheric Sciences, 1976,33(11):2242-2251.
[31]	Armstrong M.Problems with universal kriging[J]. Mathematical Geology, 1984,16(1):101-108.
[32]	Christopher S A, Johnson B, Jones T A, et al.Vertical and spatial distribution of dust from aircraft and satellite measurements during the GERBILS field campaign[J]. Geophysical Research Letters, 2009,36(6):150-164.
[33]	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.
[34]	Remer L A, Kleidman R G, Levy R C, et al.Global aerosol climatology from the MODIS satellite sensors[J]. Journal of Geophysical Research: Atmospheres (1984-2012), 2008,113(D14):762-770.

卫星参数	Aqua（MODIS）	Aura（OMI）
发射时间	2002-05-04	2004-07-15
轨道类型	太阳同步	太阳同步
轨道高度（km）	705	705
轨道周期（min）	98.8	98.8
过境时间	L.T 13:30	L.T 13:45

传感器	二次多项式法				Angstrom波长指数法
传感器	插值波长		结果波长	MAE	插值波长	结果波长	MAE
OMI	342.5, 388, 442	483.5	0.001		342.5, 388	483.5	0.019
OMI	342.5, 442, 483.5	388	0.001		342.5, 483.5	388	0.009
AERONET	440, 500, 675	870	0.010		440, 675	870	0.017
AERONET	440, 500, 870	675	0.007	440, 870		675	0.014

站点	MODIS	OMI	UK	AERONET	MODIS-AERONET	OMI-AERONET	UK-AERONET
合肥	0.590	0.688	0.493	0.536	0.054	0.152	-0.043
寿县	0.435	0.366	0.534	0.504	-0.069	-0.138	0.030
太湖	0.453	0.487	0.474	0.467	-0.014	0.020	0.010

华东地区MODIS与OMI气溶胶光学厚度数据融合

Data Fusion of MODIS AOD and OMIAOD over East China Using Universal Kriging

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