基于氧气A 带的高光谱卫星气溶胶层高优化反演

大气与环境光学学报 ›› 2022, Vol. 17 ›› Issue (6): 630-639.

• “新型卫星载荷大气遥感及应用” 专辑 • 上一篇下一篇

基于氧气A 带的高光谱卫星气溶胶层高优化反演

许健1, 饶兰兰2, DOICU Adrian2, 胡斯勒图3∗, 秦凯4∗

1 中国科学院国家空间科学中心, 北京100190; 2 德国宇航中心遥感技术研究所, 奥伯法芬霍芬82234, 德国; 3 中国科学院空天信息创新研究院, 北京100094; 4 中国矿业大学环境与测绘学院, 江苏徐州221116

收稿日期:2022-10-17 修回日期:2022-11-04 出版日期:2022-11-28 发布日期:2022-12-14
通讯作者: husiletu@radi.ac.cn; qinkai@cumt.edu.cn E-mail:qinkai@cumt.edu.cn
作者简介:许健(1982 - ), 江苏无锡人, 博士, 研究员, 主要从事大气遥感反演。E-mail: xujian@nssc.ac.cn
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 41975041)

An optimized retrieval algorithm of aerosol layer height from hyperspectral satellites using O2-A band

XU Jian1, RAO Lanlan2, DOICU Adrian2, HUSI Letu3∗, QIN Kai4∗

1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; 2 Remote Sensing Technology Institute, German Aerospace Center, Oberpfaffenhofen 82234, Germany; 3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 4 School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Received:2022-10-17 Revised:2022-11-04 Published:2022-11-28 Online:2022-12-14
Contact: Kai Qin E-mail:qinkai@cumt.edu.cn

摘要/Abstract

摘要： 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。

关键词: 大气遥感, 反演, 气溶胶层高, TROPOMI

Abstract: 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.

Key words: atmospheric remote sensing, retrieval, aerosol layer height, TROPOMI

中图分类号:

X513
TP79

许健, 饶兰兰, DOICU Adrian, 胡斯勒图∗, 秦凯∗. 基于氧气A 带的高光谱卫星气溶胶层高优化反演[J]. 大气与环境光学学报, 2022, 17(6): 630-639.

XU Jian, RAO Lanlan, DOICU Adrian, HUSI Letu∗, QIN Kai∗. An optimized retrieval algorithm of aerosol layer height from hyperspectral satellites using O2-A band[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(6): 630-639.

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基于氧气A 带的高光谱卫星气溶胶层高优化反演

An optimized retrieval algorithm of aerosol layer height from hyperspectral satellites using O2-A band

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