我国面临严重的雾霾问题，尤其是卫星观测发现大范围的霾层常出现在我国东部，并具有较大时空变化。但是常用MODIS气溶胶数据在中国东部重霾、霾与云混合情况下存在数据缺失问题，难以反映区域霾的变化过程，获取的光学信息也比较有限。本项目拟利用A-Train系列卫星可以紫外、可见光-短波红外、多角度偏振、激光雷达等多种手段对同一地区霾层进行同时探测的优势，识别区域霾的分布范围，并探索通过霾层的光学特性和时空特征初步诊断我国区域霾的主要类型。主要研究内容包括：1）针对霾与云的区分难题，发展区域霾的多源遥感识别方法；2）研究区域霾层光学特性的多源遥感探测机理，根据不同季节和地区的霾层在紫外吸收、粗细模态、垂直结构等光学特性方面的差异判别不同的霾类型；3）结合地基观测和气象数据，研究区域霾事件的时空变化过程，并在此基础上改进霾类别的光学诊断，探讨中国东部区域性霾的典型类型及时空分布。

China suffers serious fog-haze pollution problem, especially that satellite observations show common widespread haze clouds over eastern China with large spatial variations. However, MODIS aerosol data is usually invalid in regions covered by heavy haze or mixing of haze-cloud. Also, optical information from single satellite sensor is limited in analyzing haze pollution. Based on the combined ultraviolet、visible and short infrared、multi-angle and polarized、and cloud-aerosol Lidar remote sensing in the A-Train satellite constellation, this study tries to identify haze distribution and investigate the main regional pollution processes over eastern China by analyzing optical properties and spatial features of the haze clouds. The main content is as follows: 1) for the difficulty in discriminating haze and cloud, develop a multi-satellite remote sensing method for identifying regional haze pollution; 2) investigate multi-satellite remote sensing mechanism in detecting optical properties of haze layers, and discriminate different haze types according to their optical properties in ultraviolet absorption、size mode、vertical structures and so on; 3) combined with ground observation and meteorological data, improve haze optical types based on the spatial information, and then analyze the typical types and distribution of the haze pollution over eastern China.