针对气溶胶被动卫星遥感中由于气溶胶模型的不确定性导致的反演误差, 引入了一种基于贝叶斯理论的新型气溶胶层高反演算法, 并应用于哨兵 5 先导 (Sentinel-5P) 卫星的 TROPOMI (TROPOspheric Monitoring Instrument) 载荷。该算法基于不同候选气溶胶模型的模型证据 (气溶胶模型的条件概率密度) 确定符合当前观测数据条件的气溶胶模型, 并通过两种模型选择方案分别得到估算最大值解和估算平均值解作为反演结果。以 TROPOMI 观测到的一次真实野火事件为例, 反演结果和官方产品具有很好的空间一致性, 且明显降低了低估现象, 证明在气溶胶先验知识缺乏的背景下该算法能够高效选择合适的气溶胶模型, 为今后高光谱卫星气溶胶层高反演的业务化数据处理提供了一种新的解决方案。

To address the retrieval errors in passive satellite remote sensing of aerosol parameters due to the uncertainty of aerosol models, a novel aerosol layer height retrieval algorithm based on Bayesian theory is introduced and applied to the TROPOspheric Monitoring Instrument (TROPOMI) of the Sentinel-5 Precursor (Sentinel-5P) satellite in this work. The algorithm determines the aerosol model that meets the current observation data conditions based on the model evidence (conditional probability density of aerosol models) of different candidate aerosol models, and obtains the estimated maximum and estimated mean values as the results by two model selection schemes, respectively. Taking a real wildfire event observed by TROPOMI as an example, the retrieval results show a good spatial agreement with the official products. The underestimation found in previous algorithms is significantly improved, which proves that the algorithm can efficiently select a suitable aerosol model in the lack of a prior knowledge, and will offer a new solution for future operational data processing of aerosol layer height inversion from hyperspectral satellites.