Volume 45 Issue 4
Apr.  2020
Turn off MathJax
Article Contents
Abstract
References
Related Articles
FANG Xin, ZOU Bin, LIU Ning. An Aerosol Optical Depth Spatial Coverage Improvement Method Based on Spatial-Temporal Random Effects with Uncertainty Constraint[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 534-541. DOI: 10.13203/j.whugis20180271
Citation: FANG Xin, ZOU Bin, LIU Ning. An Aerosol Optical Depth Spatial Coverage Improvement Method Based on Spatial-Temporal Random Effects with Uncertainty Constraint[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 534-541. DOI: 10.13203/j.whugis20180271
shu

An Aerosol Optical Depth Spatial Coverage Improvement Method Based on Spatial-Temporal Random Effects with Uncertainty Constraint

  • 1.

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • 2.

    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China

Funds: 

The National Key Research and Development Program of China 2016YFC0206205

the National Natural Science Foundation of China 41871317

Open Fund of Hunan Provincial University Innovation Platform 5K132

the Fundamental Research Funds for the Central Universities, Central South University 2016zzts089

Outstanding Youth Project of Hunan Department of Education 19B107

More Information
  • Author Bio:

    FANG Xin，PhD, specializes in GIS environmental modeling and mapping. E-mail:xinfang@csu.edu.cn

  • Corresponding author:

    ZOU Bin, PhD, professor. E-mail:210010@csu.edu.cn

  • Received Date: January 12, 2019
  • Published Date: April 04, 2020
    Graphical Abstract
    • Abstract
  • According to the existing researches of aerosol optical depth(AOD) spatial coverage improvement, the spatial-temporal correlation of multi-source products is not fully considered in the fusion process, and the uncertainty of the estimated results is not considered. This paper proposes an AOD spatial coverage improvement method based on spatiotemporal random effects (STRE) modeling with the uncertainty constraint. And this method is applied to the spatial coverage improvement of daily Moderate-resolution Imaging Spectroradiomete(MODIS) AOD products in eastern China from January 2013 to December 2017. The results show that the STRE model AOD spatial coverage improvement method can effectively improve the spatial coverage and temporal completeness of AOD by considering the temporal and spatial correlation of AOD data. And the introduction of uncertainty analysis in the STRE model AOD spatial coverage improvement can effectively reduce the accuracy of the overall coverage improvement results, especially for the region with serious missing original AOD data on time and space.
    • FullText(HTML)
    • References (19)
  • [1]
    Lee H J, Liu Y, Coull B A, et al. A Novel Calibration Approach of MODIS AOD Data to Predict PM2.5 Concentrations[J]. Atmospheric Chemistry & Physics, 2011, 11:7991-8002 http://www.oalib.com/paper/1369011#.XsIFj_m-DT4
    [2]
    Puttaswamy S J, 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):48-64 doi: 10.1080/10106049.2013.827750
    [3]
    Fang X, Zou B, Liu X, et al. Satellite-Based Ground PM2.5 Estimation Using Timely Structure Adaptive Modeling[J]. Remote Sensing of Environment, 2016, 186:152-163 doi: 10.1016/j.rse.2016.08.027
    [4]
    李广超, 李如仁, 赵阳阳, 等.基于二次多项式的AOD数据融合方法[J].测绘通报, 2018(1):67-71 http://d.old.wanfangdata.com.cn/Periodical/chtb201801012

    Li Guangchao, Li Ruren, Zhao Yangyang, et al. AOD Data Fusion Method Based on Quadratic Polynomial[J]. Bulletin of Surveying and Mapping, 2018(1):67-71 http://d.old.wanfangdata.com.cn/Periodical/chtb201801012
    [5]
    王伟齐, 张成网, 臧增亮, 等. Terra和Aqua卫星MODIS 3 km AOD与北京PM2. 5对比分析[J].气象科学, 2017, 37(1):93-100 http://d.old.wanfangdata.com.cn/Periodical/qxkx201701011

    Wang Weiqi, Zhang Chengwang, Zang Zengliang, et al. Comparative Analysis Between Hourly PM2.5 Concentration and MODIS 3 km Aerosol Optical Depth Derived from Terra and Aqua Satellites in Beijing[J]. Journal of the Meteorological Sciences, 2017, 37(1):93-100 http://d.old.wanfangdata.com.cn/Periodical/qxkx201701011
    [6]
    Xu H, Guang J, Xue Y, et al. A Consistent Aerosol Optical Depth (AOD) Dataset over Mainland China by Integration of Several AOD Products[J]. Atmospheric Environment, 2015, 114:48-56 doi: 10.1016/j.atmosenv.2015.05.023
    [7]
    Tobler W R. A Computer Movie Simulating Urban Growth in the Detroit Region[J]. Economic Geography, 1970, 46:234-240 doi: 10.2307-143141/
    [8]
    李龙, 施润和, 张璐, 等.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 http://d.old.wanfangdata.com.cn/Periodical/dqxxkx201510011

    Li Long, Shi Runhe, Zhang Lu, et al. Data Fusion of MODIS AOD and OMI AOD over East China Using Universal Kriging[J]. Journal of Geo information Science, 2015, 17(10):1224-1233 http://d.old.wanfangdata.com.cn/Periodical/dqxxkx201510011
    [9]
    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 doi: 10.1080/01621459.2012.694717
    [10]
    Cressie N, Shi T, Kang E L. Fixed Rank Filtering for Spatio-temporal Data[J]. Journal of Computational & Graphical Statistics, 2010, 19(3):724-745 doi: 10.1198/jcgs.2010.09051
    [11]
    Kang E L, Cressie N, Shi T. Using Temporal Variability to Improve Spatial Mapping with Application to Satellite Data[J]. Canadian Journal of Statistics, 2010, 38(2):271-289 doi: 10.1002/cjs.10063
    [12]
    Tang Q, Bo Y, Zhu Y. Spatiotemporal Fusion of Multiple-satellite Aerosol Optical Depth (AOD) Products Using Bayesian Maximum Entropy Method[J]. Journal of Geophysical Research:Atmospheres, 2016, 121(8):4034-4048 doi: 10.1002/2015JD024571
    [13]
    Katzfuss M, Cressie N. Spatio-temporal Smoothing and EM Estimation for Massive Remote-Sensing Data Sets[J]. Journal of Time Series Analysis, 2011, 32(4):430-446 doi: 10.1111/j.1467-9892.2011.00732.x
    [14]
    Katzfuss M, Cressie N. Bayesian Hierarchical Spatio-temporal Smoothing for very Large Datasets[J]. Environmetrics, 2012, 23(1):94-107 https://www.researchgate.net/publication/263479580_Bayesian_hierarchical_spatio-temporal_smoothing_for_very_large_datasets
    [15]
    Cressie N, Johannesson G. Fixed Rank Kriging for very Large Spatial Data Sets[J]. Journal of the Royal Statistical Society, 2008, 70(1):209-226 https://www.jstor.org/stable/20203819
    [16]
    Liu Y, Sarnat J A, Coull B A, et al. Validation of Multiangle Imaging Spectroradiometer(MISR)Aerosol Optical Thickness Measurements Using Aerosol Robotic Network (AERONET) Observations over the Contiguous United States[J]. Journal of Geophysical Research:Atmospheres, 004, 109(D06):D06205 doi: 10.1029/2003JD003981
    [17]
    Tang G, Zhang J, Zhu X, et al. Mixing Layer Height and Its Implications for Air Pollution over Beijing, China[J]. Atmospheric Chemistry and Physics, 2016, 16(4):2459-2475 doi: 10.5194/acp-16-2459-2016
    [18]
    郭阳洁, 洪松, 庄艳华, 等.湖北省气溶胶光学厚度时空分布特征研究[J].武汉大学学报·信息科学版, 2010, 35(11):1381-1385 http://ch.whu.edu.cn/CN/abstract/abstract1114.shtml

    Guo Yangjie, Hong Song, Zhuang Yanhua, et al. Temporal Variation and Spatial Distribution of Atmospheric Aerosols over Hubei Province[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11):1381-1385 http://ch.whu.edu.cn/CN/abstract/abstract1114.shtml
    [19]
    王华, 郭阳洁, 洪松, 等.区域气溶胶学厚度空间格局特征研究[J].武汉大学学报·信息科学版, 2013, 38(7):869-874 http://ch.whu.edu.cn/CN/abstract/abstract2706.shtml

    Wang Hua, Guo Yangjie, Hong Song, et al. Spatial Pattern Characteristics of Aerosol Optical Depth in a Region Based on Spatial Autocorrelation[J]. Geomatics and Information Science of Wuhan University, 2013, 38(7):869-874 http://ch.whu.edu.cn/CN/abstract/abstract2706.shtml
    • Related Articles

  • Related Articles

    [1]GUO Wenfei, ZHU Mengmeng, GU Shengfeng, ZUO Hongming, CHEN Jinxin. GNSS Precise Time-Frequency Receiver Clock Steering Model and Parameter Design Method[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1126-1133. DOI: 10.13203/j.whugis20220458
    [2]SUN Leyuan, YANG Jun, GUO Xiye, HUANG Wende. Frequency Performance Evaluation of BeiDou-3 Satellite Atomic Clocks[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20200486
    [3]WU Yiwei, YANG Bin, XIAO Shenghong, WANG Maolei. Atomic Clock Models and Frequency Stability Analyses[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1226-1232. DOI: 10.13203/j.whugis20180058
    [4]AN Xiangdong, CHEN Hua, JIANG Weiping, XIAO Yugang, ZHAO Wen. GLONASS Ambiguity Resolution Method Based on Long Baselines and Experimental Analysis[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 690-698. DOI: 10.13203/j.whugis20170091
    [5]LI Mingzhe, ZHANG Shaocheng, HU Youjian, HOU Weizhen. Comparison of GNSS Satellite Clock Stability Based on High Frequency Observations[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1490-1495, 1503. DOI: 10.13203/j.whugis20160537
    [6]WANG Ning, WANG Yupu, LI Linyang, ZHAI Shufeng, LV Zhiping. Stability Analysis of the Space-borne Atomic Clock Frequency for BDS[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1256-1263. DOI: 10.13203/j.whugis20150806
    [7]LIU Zhiqiang, YUE Dongjie, WANG Hu, ZHENG Dehua. An Approach for Real-Time GPS/GLONASS Satellite Clock Estimation with GLONASS Code Inter-Frequency Biases Compensation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1209-1215. DOI: 10.13203/j.whugis20150542
    [8]HUANG Guanwen, YU Hang, GUO Hairong, ZHANG Juqing, FU Wenju, TIAN Jie. Analysis of the Mid-long Term Characterization for BDS On-orbit Satellite Clocks[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 982-988. DOI: 10.13203/j.whugis20140827
    [9]MAO Yue, CHEN Jianpeng, DAI Wei, JIA Xiaolin. Analysis of On-board Atomic Clock Stability Influences[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1182-1186.
    [10]GUO Hairong, YANG Yuanxi. Analyses of Main Error Sources on Time-Domain Frequency Stability for Atomic Clocks of Navigation Satellites[J]. Geomatics and Information Science of Wuhan University, 2009, 34(2): 218-221.

  • Cited by

    Periodical cited type(10)

    1. 黄观文，曹钰，谭粤，谢威. GNSS星载原子钟在轨性能评估技术进展. 测绘地理信息. 2024(01): 20-28 .
    2. 蒋春华，朱美珍，薛慧杰，刘广盛. 基于长短时记忆神经网络的Multi-GNSS卫星钟差建模预报. 大地测量与地球动力学. 2024(03): 257-262 .
    3. 艾孝军，孙大伟，贾小林，郭栋，彭腾. GNSS星载原子钟性能评估与噪声分析模型算法研究. 无线电工程. 2023(05): 1041-1051 .
    4. 李方能，梁益丰，许江宁，吴苗. BDS/GPS新型铷原子钟长期特性分析. 中国惯性技术学报. 2023(05): 452-461 .
    5. 张润哲，刘雪娇，王全喜. 基于方位导引的无人僚机着舰进近引导技术研究. 现代导航. 2023(06): 416-421 .
    6. 龚明杰. GPS与GLONASS多频组合伪距单点定位精度分析. 测绘与空间地理信息. 2022(02): 115-117+122 .
    7. 樊礼谦，焦文海，蔡洪亮，周巍，徐颖，周舒涵. 北斗三号卫星钟长期稳定性分析. 导航定位学报. 2022(04): 11-19 .
    8. 李特，张为成，王建敏，李秀海. 基于不同评价指标的北斗星载原子钟特性分析. 黑龙江工程学院学报. 2022(04): 1-7 .
    9. 伏军胜，贾小林，刘家龙，许瑾，贺延伟，张奋. BDS-3卫星与其他GNSS系统卫星原子钟性能分析. 真空与低温. 2022(05): 615-622 .
    10. 齐艳丽. 北斗星载原子钟频率稳定度评估. 科技视界. 2022(29): 83-85 .

    Other cited types(11)

Catalog

    Get Citation
    PDF
    XML
    Article views (1242) PDF downloads (42) Cited by(21)
    Related

    Copyright © 2013 《武汉大学学报·信息科学版》编辑部

    Address：武汉大学文理学部本科生院楼北5楼Post Code：430072

    Tel：027-68778465,68788631E-mail:whuxxb@vip.163.com

    Submit Article

    Submission Guidelines

    e

    Contact Us

    Qrcode

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return