Accurate retrieval of Aerosol Optical Depth (AOD) data from satellite remote sensing images over urban areas is of great importance, e.g. for air-quality monitoring. However, limited by coarse spatial resolution, available aerosol products are unable to fulfil the increasing demand of aerosol retrieval below the city scale. To address this issue, a new high-resolution (60 m) aerosol retrieval algorithm based on Sentinel-2 data over urban areas was developed. To improve the estimation of surface reflectance, two algorithms: (1) the new visible/2.19 μm surface reflectance relationships as a function of normalized difference vegetation index (NDVI) for vegetated areas; and (2) the surface reflectance database preconstructed from the operational high-quality and high-resolution Landsat 8 surface reflectance products for bright areas, were adopted depending on the surface types. For validation, the derived retrievals from Sentinel-2, along with the operational MODerate resolution Imaging Spectrometer Collection 6 (C6) aerosol products (MOD04_L2 and MCD19A2), were compared with measurements from Aerosol Robotic Network (AERONET) stations. The validation results show that the Sentinel-2 AOD retrievals agreed well with the AERONET AOD measurements, with an overall correlation coefficient of 0.907, expected error (EE) of 73.76%, mean absolute error (MAE) of 0.087, and root mean square error (RMSE) of 0.117. Comparison of the results with MOD04_L2 products (10 km) show that the Sentinel-2 AODs were superior in the situations considered, indicating that the new algorithm performed better in AOD retrieval over urban areas. Compared with the fine spatial resolution MCD19A2 products (1 km), retrievals of the proposed algorithm were comparable. The new algorithm is able to retrieve high-resolution AODs reasonably well from Sentinel-2 images over urban areas, and can provide continuous and detailed aerosol spatial distributions. Owing to the ability of the Sentinel-2 satellite to provide observations at 5-day intervals, the proposed algorithm is capable of monitoring aerosol distributions at relatively fine temporal resolution.

从城市地区的卫星遥感图像中准确检索气溶胶光学深度 (AOD) 数据非常重要，例如对于空气质量监测。然而，受限于粗略的空间分辨率，现有的气溶胶产品无法满足城市尺度以下日益增长的气溶胶反演需求。为了解决这个问题，开发了一种基于城市地区 Sentinel-2 数据的新型高分辨率 (60 m) 气溶胶检索算法。为了改进表面反射率的估计，有两种算法：(1) 作为植被区域归一化差异植被指数 (NDVI) 的函数的新的可见光/2.19 μm 表面反射率关系；(2) 由高品质高分辨率 Landsat 8 明亮区域表面反射产品预先构建的表面反射数据库，根据表面类型采用。为了验证，将 Sentinel-2 的衍生检索以及操作中分辨率成像光谱仪集合 6 (C6) 气溶胶产品（MOD04_L2 和 MCD19A2）与来自气溶胶机器人网络 (AERONET) 站的测量值进行了比较。验证结果表明，Sentinel-2 AOD反演与AERONET AOD测量结果吻合较好，总体相关系数为0.907，预期误差（EE）为73.76%，平均绝对误差（MAE）为0.087，均方根误差(RMSE) 为 0.117。结果与MOD04_L2产品（10公里）的比较表明，Sentinel-2 AOD在所考虑的情况下具有优势，表明新算法在城市地区的AOD检索中表现更好。与精细空间分辨率MCD19A2产品（1公里）相比，该算法的检索具有可比性。新算法能够从城市地区的 Sentinel-2 图像中相当好地检索高分辨率 AOD，并且可以提供连续和详细的气溶胶空间分布。由于 Sentinel-2 卫星能够以 5 天的间隔提供观测，所提出的算法能够以相对精细的时间分辨率监测气溶胶分布。