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针对PM2.5遥感模型对气溶胶细粒子比（Fine mode fraction，FMF）参数的需求，结合多光谱偏振传感器对大气探测的优势，基于最优估计（Optimal Estimation，OE）反演框架，提出了一种基于线偏振度（Degree of Linear Polarization）测量的FMF参数反演方法。采用矢量化的辐射传输模式进行地基天空光的线偏振度观测模拟，分析了线偏振度对FMF参数的波段敏感性，并基于仿真数据开展了算法的反演测试。研究结果表明，偏振测量在长波近红外波段对FMF的敏感性高于可见光波段，基于OE框架的FMF反演算法具有良好的自洽性，在地基天顶观测模式下，反演误差从1.4%下降到了0.8%，表明引入线偏振度测量能够有效提高FMF反演精度。
Aerosol fine-mode fraction (FMF) is a key parameter in the near-surface PM2.5 physical estimation model of particle pollution. However, there still have great challenges for the remote sensing of FMF. Polarimetric measurements shows unique capabilities in aerosol remote sensing. In order to investigate the contribution of polarization to the inversion of FMF, an algorithm for FMF retrieval from multispectral intensity and degree of linear polarization (DOLP) measurements is proposed in this study. The algorithm is based on the optimal estimation (OE) inversion theory. The UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) is adopted as the forward model, and the quasi-Newton approach implemented by the L-BFGS-B code is used to find the minimum of the cost function. In order to test the performance of the algorithm, synthetic data for ground-based measurements of sky light are simulated. After several iterations, the aerosol optical depth (AOD) and FMF can be retrieved simultaneously. Sensitivity analysis show that the DOLP is more sensitive to FMF in the near-infrared band than in the visible band. Numerical inversion test show that the algorithm has well self-consistency. The average fitting residual, difference between the measurements and the simulations with best inversion results, is 5.2%. By introducing DOLP measurements into the retrieval, the inversion accuracy improved significantly than only using the intensity measurements. The retrieval error of AOD has decreased from 1% to 0.3%, and the error of FMF has decreased from 1.4% to 0.8%. The results strongly validate the feasibility and potentiality of the proposed OE inversion method in atmospheric aerosol remote sensing. It is expected to be a new way to improve the remote sensing capabilities of PM2.5 monitoring.