【摘要】 针对GF-5卫星搭载的多角度偏振探测仪（DPC）平台上气溶胶细粒子反演算法中半经验模型不适用于城市地表偏振反射率估算的问题。基于DPC的经验正交函数方法，开展了气溶胶细粒子光学厚度反演研究。基于米散射计算气溶胶辐射贡献，采用经验正交函数方法计算地表贡献，利用多角度偏振数据以及矢量辐射传输方程，反演气溶胶细粒子光学厚度。本研究的反演结果与中分辨率成像光谱仪的气溶胶细粒子光学厚度产品分布趋势具有一致性，然后与AERONET北京、香河、香港站点的测量结果进行定量对比，相关系数为0.97、0.96、0.9，平均绝对误差为0.08、0.07、0.12，均方根误差为0.12、0.11、0.17，验证了算法的高精度与合理性。最后呈现2019年中国部分地区的气溶胶细粒子光学厚度月平均数据，并分析山东地区气溶胶细粒子光学厚度变化情况，发现6月是全年最高的时期，均值为0.7。上述结果验证了本文算法的可靠性，可为DPC有效监测气溶胶的时空分布提供技术支持更多还原

【Abstract】 Aiming at the problem that the semi-empirical model in the aerosol fine particle retrieval algorithm on the directional polarimetric camera(DPC) platform carried by the GF-5 satellite is not suitable for the estimation of urban surface polarization reflectance. In this paper, based on the empirical orthogonal function method of DPC, the inversion of the optical thickness of aerosol fine particles is carried out. The aerosol radiative contribution is calculated based on Mie scattering, the surface contribution is calculated by the empirical orthogonal function method, and the optical thickness of aerosol fine particles is inverted by using the multi-angle polarization data and the vector radiative transfer equation. The inversion results of this study are consistent with the moderate-resolution imaging spectroradiometer distribution trend of aerosol fine particle optical depth products, quantitatively compared with the measurement results of AERONET Beijing,Xianghe, and Hong Kong stations, the correlation coefficients are 0. 97, 0. 96, and 0. 9, the mean absolute error is 0. 08,0. 07, and 0. 12, and the root mean square error is 0. 12, 0. 11, and 0. 17, which verifies the high precision and rationality of the algorithm. Finally, the monthly average data of aerosol fine particle optical thickness in some areas of China in 2019are presented, and the changes of aerosol fine particle optical thickness in Shandong are analyzed. It is found that June is the highest period of the year, with an average value of 0. 7. The above results verify the reliability of the algorithm and provide technical support for DPC to effectively monitor the spatiotemporal distribution of aerosols更多还原