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基于多特征参数的OMI遥感产品气溶胶分类研究——以广东省为例

郑仰成 黎丽莉 王云鹏

郑仰成, 黎丽莉, 王云鹏. 基于多特征参数的OMI遥感产品气溶胶分类研究——以广东省为例[J]. 华南师范大学学报(自然科学版), 2021, 53(4): 68-75. doi: 10.6054/j.jscnun.2021060
引用本文: 郑仰成, 黎丽莉, 王云鹏. 基于多特征参数的OMI遥感产品气溶胶分类研究——以广东省为例[J]. 华南师范大学学报(自然科学版), 2021, 53(4): 68-75. doi: 10.6054/j.jscnun.2021060
ZHENG Yangcheng, LI Lili, WANG Yunpeng. The Multi-feature Parameter Classification of Aerosol Based on OMI Remote Sensing Data: A Case Study in Guangdong Province[J]. Journal of South China normal University (Natural Science Edition), 2021, 53(4): 68-75. doi: 10.6054/j.jscnun.2021060
Citation: ZHENG Yangcheng, LI Lili, WANG Yunpeng. The Multi-feature Parameter Classification of Aerosol Based on OMI Remote Sensing Data: A Case Study in Guangdong Province[J]. Journal of South China normal University (Natural Science Edition), 2021, 53(4): 68-75. doi: 10.6054/j.jscnun.2021060

基于多特征参数的OMI遥感产品气溶胶分类研究——以广东省为例

doi: 10.6054/j.jscnun.2021060
基金项目: 

国家自然科学基金项目 42007205

广东省自然科学基金项目 2021A1515011375

详细信息
    通讯作者:

    黎丽莉,Email: lilili@gig.ac.cn

  • 中图分类号: X87

The Multi-feature Parameter Classification of Aerosol Based on OMI Remote Sensing Data: A Case Study in Guangdong Province

  • 摘要: 为了解决气溶胶分类精度低和特征参数冗杂的问题,基于OMI(Ozone Monitoring Instrument)遥感产品的气溶胶特征参数,利用随机森林算法,将广东省2014年的气溶胶类型划分为沙尘型气溶胶(Desert Dust, DST)、生物质燃烧型含碳气溶胶(Carbonaceous Aerosols Associated with Biomass Burning, CRB)和硫酸盐型城镇-工业气溶胶(Sulfate-based Urban-industrial Aerosols,SLF)3种类型. 并统计分析随机森林以及特征参数的重要性,将分类结果的空间分布与OMI气溶胶类型产品的空间分布进行对比. 结果表明:(1)随机森林算法仅需少量训练样本点即可达到97%以上的总体分类精度. (2)通过计算不同气溶胶特征参数在随机森林分类过程中的重要性高低,得到重要性排名前六的特征参数依次为α指数、UVAI、RI388、RI354、SSA500、AAOD500,表明在分类过程中,气溶胶粒径分布和吸收能力起到了最关键的作用. (3)3种气溶胶类型的空间分布显示,SLF型气溶胶为广东省最主要的气溶胶类型;DST型和CRB型气溶胶在珠三角地区占比最高,在粤东、粤北地区的占比最低.
  • 图  1  气溶胶分类流程图

    Figure  1.  The flow chart of aerosol type classification

    图  2  气溶胶分类精度与训练样本数量关系

    Figure  2.  The relationship between the training sample size and the classification precision

    图  3  广东省的气溶胶类型占比的空间分布(随机森林分类结果)

    Figure  3.  The spatial distribution of aerosol types proportion in Guangdong (results of RF classification)

    图  4  广东省的气溶胶类型占比的空间分布(OMI产品标签数据)

    Figure  4.  The spatial distribution of aerosol type proportion in Guangdong (label data from OMI product)

    表  1  各项气溶胶特征参数的重要性

    Table  1.   The importance of each aerosol feature parameter

    特征参数 重要性
    AAOD λ=354 nm 0.034
         λ=388 nm 0.019
         λ=500 nm 0.045
    AOD λ=354 nm 0.008
         λ=388 nm 0.009
         λ=500 nm 0.008
    SSA λ=354 nm 0.038
         λ=388 nm 0.020
         λ=500 nm 0.079
    RI λ=354 nm 0.141
         λ=388 nm 0.128
    经度 0.004
    纬度 0.004
    UVAI 0.218
    α指数 0.245
    下载: 导出CSV

    表  2  3类气溶胶特征参数的均值、标准差、最大值及最小值

    Table  2.   The mean value, standard deviation, and maximum and minimum values of three aerosol feature parameter

    气溶胶类型 特征参数 均值 标准差 最大值 最小值
    DST α指数 0.605 0.000 0.604 0.602
    UVAI 1.233 0.416 3.862 0.800
    RI388 0.007 0.004 0.028 0.003
    RI354 0.019 0.010 0.071 0.007
    SSA500 0.950 0.026 0.982 0.829
    AAOD500 0.039 0.018 0.155 0.011
    CRB α指数 1.614 0.059 1.740 1.544
    UVAI 1.168 0.460 5.604 0.801
    RI388 0.001 0.001 0.016 0.000
    RI354 0.002 0.002 0.022 0.000
    SSA500 0.985 0.010 0.997 0.869
    AAOD500 0.016 0.007 0.082 0.005
    SLF α指数 1.853 0.013 1.872 1.779
    UVAI 0.106 0.385 0.800 -2.186
    RI388 0.004 0.003 0.019 0.000
    RI354 0.004 0.003 0.019 0.000
    SSA500 0.964 0.021 1.000 0.853
    AAOD500 0.026 0.014 0.082 0.000
    下载: 导出CSV

    表  3  广东省各城市3种气溶胶类型占比

    Table  3.   The proportion of three aerosol types of each city in Guangdong  %

    城市 OMI产品标签数据 随机森林分类结果
    DST CRB SLF DST CRB SLF
    潮州市 6.6 2.5 90.9 6.6 4.1 89.3
    东莞市 23.9 1.2 74.9 19.6 4.3 76.1
    佛山市 16.4 1.3 82.3 19.6 2.2 78.2
    广州市 17.1 1.8 81.1 17.1 2.1 80.8
    河源市 7.8 1.2 9.1 7.8 1.9 90.3
    惠州市 9.6 1.0 89.4 9.6 2.5 87.9
    江门市 13.8 1.6 84.6 13.7 1.9 84.4
    揭阳市 7.3 1.7 91.0 7.3 1.9 90.8
    茂名市 12.8 0.7 86.5 12.8 0.7 86.5
    梅州市 5.9 1.7 92.4 5.9 3.0 91.1
    清远市 10.8 0.5 88.7 10.6 0.5 88.9
    汕头市 8.6 8.6 82.8 10.6 11.6 77.8
    汕尾市 6.9 4.0 89.1 6.9 3.9 89.2
    韶关市 9.6 1.8 88.6 8.1 1.9 90.0
    深圳市 10.1 2.9 87.0 10.1 5.0 84.9
    阳江市 7.7 2.1 90.2 8.2 2.1 89.7
    云浮市 5.3 1.1 93.6 8.2 1.9 89.9
    湛江市 12.7 3.2 84.1 16.7 6.9 76.4
    肇庆市 10.5 0.9 88.6 10.5 0.9 88.6
    中山市 8.4 3.1 88.5 10.8 4.1 85.1
    珠海市 8.7 3.3 88.0 10.8 4.1 85.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-08
  • 网络出版日期:  2021-09-03
  • 刊出日期:  2021-08-25

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