近年来, 我国频繁发生的灰霾污染事件和常态性的高细颗粒物浓度（PM2.5）, 已经引起了全世界范围的广泛关注。 卫星遥感能够对大气污染进行快速准确地监测。 然而在大气遥感领域具有代表性的中分辨率成像光谱仪(NASA/MODIS), 其云监测和暗像元反演算法通常把灰霾当做薄云、 雾或地表亮目标处理, 无法反演霾天的气溶胶光学厚度(aerosol optical depth, AOD)。 笔者研究了云、 雾、 霾、 地表覆盖等不同像元在可见光、 近红外以及红外通道的光谱特性。 基于MODIS数据, 参考相关的云监测和气溶胶反演算法, 选取多个对灰霾敏感的光谱通道, 计算表观反射率和亮度温度。 针对不同波段, 分别探讨了霾与薄云、 低层云、 雾、 浓密植被和地表亮目标等像元之间的光谱差异, 统计灰霾分布的阈值区间, 并设计基于MODIS卫星遥感数据的灰霾识别自动处理流程。 通过对2008年华北平原春夏两个重霾事件进行测试, 该算法的霾分布监测结果与卫星真彩图具有较好的一致性。 基于北京和香河AERONET站点观测的高AOD数据, 验证了本算法的霾识别率接近80%, 在一定程度能够弥补MODIS标准气溶胶算法用于灰霾天的不足。 最后, 分析了灰霾识别过程中的主要误差来源, 并提出了基于霾纹理特征, 以及其他辅助数据支撑的改进方法。

Frequent occurring of haze pollution events and high fine particulate matter (PM2.5) concentration in China have attracted more and more attention in the world. Satellite remote sensing can be used to characterize the air pollution. However, haze is usually misidentified as fog, thin cloud or bright surface in NASA’s Moderate Resolution Imaging Spectrometer (MODIS) cloud and clear days’ aerosol products, and the retrieval of its optical properties is not included in MODIS cloud detection and dark target algorithm. This approach first studies the spectral characters of cloud, fog, haze, and land cover pixels. Second, following the previous cloud detection and aerosol retrieval literatures, a threshold algorithm is developed to distinguish haze from other pixels based on MODIS multi-band apparent reflectance and brightness temperature. This algorithm is used to detect the haze distribution over North China Plain in 2008 spring and summer. Our result shows a good agreement with the true-color satellite images, which enhances MODIS’s ability to monitor the severe air pollution episodes. In addition, the high AOD data from Beijing and Xiang Aerosol Robotic NETwork (AERONET) sites indicate nearly 80% haze days are detected by our approach. Finally, we analyze the errors and uncertainties in haze detection algorithm, and put forward the potential improvements.