PSO聚类算法的京津冀地区气溶胶光学厚度反演

doi:10.3964/j.issn.1000-0593(2020)11-3321-07

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摘要：气溶胶光学厚度(AOD)是气溶胶浓度和大气浊度的重要表征参数。通过遥感手段实现大气气溶胶光学厚度的反演是大气监测与治理过程中的重要方式，其中遥感反演AOD的重点和难点是如何选择适合卫星传感器成像特点的方法和符合研究区域的气溶胶类型。针对传统暗目标法无法直接应用于高分四号(GF-4)卫星多光谱遥感数据的问题，通过研究得出了GF-4卫星多光谱数据中红、蓝波段等效地表反射率的分布和两者之间的线性关系，结合AOD反演原理改进暗目标法使其适用于GF-4卫星多光谱遥感数据；分析6S辐射传输模型输入参数中气溶胶类型对AOD反演精度的影响，结果表明气溶胶类型是影响AOD高精度反演的关键要素之一；利用粒子群(PSO)聚类算法对京津冀地区气溶胶特性实测样本进行聚类分析，通过分析各个气溶胶类型聚类结果的占比和半衰期变化情况，最终确定聚类得到的C1、C4型和6S模型内置的大陆型气溶胶类型进行京津冀地区的AOD反演。为了验证不同气溶胶类型AOD反演结果的精度，将反演结果与MODIS气溶胶产品和气溶胶自动观测网(AERONET)地基站点数据进行对比验证，通过相关系数、绝对误差等评价标准对不同气溶胶类型的适用性和特点进行评价。实验结果表明，以细粒子为主导的C4型气溶胶更满足京津冀地区夏秋两季的气溶胶特点，与AERONET地基数据的一致性较好，进一步证明了PSO聚类算法能够有效减小气溶胶类型的差异对AOD反演精度的影响。

关键词：气溶胶；GF-4卫星多光谱数据；京津冀地区；PSO聚类算法

Abstract：Aerosol optical thickness (AOD) is an important characterization parameter of aerosol concentration and atmospheric turbidity. Inversion of atmospheric AOD by remote sensing is an important way in the process of atmospheric monitoring and management, and in which the selection of methods suitable for the imaging characteristics of satellite sensors and the type of aerosols in line with the study area has always been the focus and difficulty of AOD inversion. In view of the problem that the traditional dark target method can not be directly applied to the multispectral remote sensing data of Gaofen Ⅳ (GF-4) satellite, this paper studies the distribution of the red and blue band equivalent surface reflectivity in GF-4 multispectral data and the linear relationship between them, and improves the dark target method to make it suitable for GF-4 satellite multispectral remote sensing data in combination with AOD inversion principle. The effect of input parameters on AOD inversion accuracy in the 6S radiation transfer model was analyzed, and the experimental results showed that aerosol type is one of the key factors affecting the high-precision inversion of AOD. The samples of aerosol characteristics in Beijing-Tianjin-Hebei area was analyzed by particle swarm optimization (PSO) cluster algorithm, by analyzing the proportion and half-life changes of the clustering results of each aerosol type, the C1 and C4 aerosol types in cluster results and the continental aerosol type of 6S models are finally determined to invert the AOD in Beijing-Tianjin-Hebei region. The inversion results were compared with MODIS aerosol products and AErosol RObotic NETwork (AERONET) ground-based site data, and the suitability and characteristics of different aerosol types are evaluated by evaluation criteria such as correlation coefficient and absolute error. The experimental results show that the C4 aerosol type, which is dominated by fine particles, is more satisfied with the characteristics of aerosols in the summer and autumn of Beijing-Tianjin-Hebei, and has better consistency with AERONET ground-based data. It is further proved that the PSO clustering algorithm can effectively reduce the influence of aerosol type difference on AOD inversion accuracy.

Key words：Aerosol; GF-4 satellite multispectral data; Beijing-Tianjin-Hebei region; PSO clustering algorithm

收稿日期: 2019-11-18 修订日期: 2020-03-09

中图分类号:

X87

基金资助: 国家自然科学基金项目(61771419)，河北省自然科学基金项目(F2017203220)资助

通讯作者: 王贵川 E-mail: wang.guichuan@outlook.com

作者简介: 王书涛，1978年生，燕山大学电气工程学院教授 e-mail：wangshutao@ysu.edu.cn

引用本文:

王书涛，王贵川，凡堃堃，吴兴，王玉田. PSO聚类算法的京津冀地区气溶胶光学厚度反演[J]. 光谱学与光谱分析, 2020, 40(11): 3321-3327.
WANG Shu-tao, WANG Gui-chuan, FAN Kun-kun, WU Xing, WANG Yu-tian. Inversion of Aerosol Optical Depth in the Beijing-Tianjin-Hebei Region Based on PSO Clustering Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3321-3327.

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