Application of a channel-selection method on the retrieval of O3 and CH4 profiles from Ultra-Spectral Thermal Infrared Data
- Vol. 28, Issue 2, Pages: 385-397(2024)
Published： 07 February 2024
DOI： 10.11834/jrs.20211223
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摘要
与高光谱热红外数据相比，超光谱热红外数据中包含了臭氧（O
3
）和甲烷（CH
4
）在大气垂直剖面上更多的状态信息，为提升O
3
和CH
4
廓线的反演精度提供了可能。然而，超光谱热红外数据通道之间的间隔较窄，这在给数据引入一些特有可反演信息的同时还引入了大量的相似信息，这些特征均无法被现有的基于高光谱热红外数据的通道选择方法识别。为了保证超光谱热红外数据反演O
3
和CH
4
廓线的效率和精度，我们提出了一种基于大气灵敏度和雅可比剖面的通道优选方法（OWSP法）。该方法首先通过分析通道对不同气体的灵敏度情况，优选出受其他气体干扰较小的通道为初选通道；其次，深度分析通道雅可比特征后提出了优化雅可比矩阵的策略，具体为将通道雅可比量化为表征通道信息容量的因素，并采用迭代的方法获取最终的通道选择结果。本文将OWSP方法应用在阿拉善、北京—天津、长江三角洲和珠江三角洲4个典型地区的冬夏季大气条件下，与常用的最佳灵敏度法（OSP法）相比，OWSP方法所选的通道集合中冗余信息少，同时也可以识别一些具有特殊有效信息但灵敏度相对较低且受其他干扰因素干扰较大的通道。反演结果进一步表明，在多数情况下，OWSP方法可以有效提升廓线的反演精度，O
3
廓线的平均反演精度提高了9.30%，CH
4
廓线的平均反演精度提高了4.90%。本文能为中国超光谱热红外载荷开发以及数据应用提供必要的技术支撑，具有重要的理论和应用价值。
Abstract
Compared with high-spectral thermal infrared data
ultra-spectral thermal infrared data contains enhanced atmospheric vertical information of ozone (O
3
) and methane (CH
4
). This finding indicates the possibility to improve the accuracy of retrieved O
3
and CH
4
profiles. Due to the narrow channel intervals of the ultra-spectral thermal infrared data
abundant special information and redundant information is induced. However
information cannot be detected by channel-selection methods for high-spectral thermal infrared data
thereby impeding the superiority of ultra-spectral data for the retrieval of trace-gas profiles. As such
a novel channel-selection method based on the gas-sensitivity and weighting-function characteristics (OWSP) has been promoted
aiming to enhance the retrieval efficiency and accuracy of O
3
and CH
4
profiles from ultra-spectral thermal infrared data. The method comprises two steps. First
the sensitivities of the channels to different gases are analyzed
and the signal-to-interference ratio (
r
STI
) are obtained. On this basis
channels with abundant information for retrieved gas and insensitivity to other gases can be detected
which are taken as the initial channel group. Second
a strategy of optimizing the distribution of the weighting function is promoted based on the features of Jacobians to O
3
and CH
4
. The channel information content can then be quantified by the optimized weighting function. An iterative approach is applied to select the optimal channel group to retrieve atmospheric profiles. In this paper
the promotion effect of OWSP method for O
3
and CH
4
profile retrieval from ultra-spectral thermal infrared data is evaluated by applying in the winter and summer atmospheric situation of the regions of Alxa Desert (AL)
Beijing Tianjin district (JJ)
Yangtze River Basin (YRD)
and Pearl River Basin (PRD). The optimal sensitivity profile (OSP) method
which suggests good performance for high-spectral thermal infrared data in literature
is used in the control group. By comparing with the channel selection results of OSP method
it shows that the OWSP method can effectively screen the correlated channels with similar information for the strong infrared radiation gas
O
3
. It can also select some channels with special information. Conversely
it has relatively low sensitivity for the weak infrared radiation gas
CH
4
thereby ensuring the accuracy and efficiency of the subsequent retrieval process. The retrieval results of O
3
and CH
4
profiles with the channel group selected by the two methods further prove that the OWSP method can efficiently improve the accuracy of the retrieved profiles in most situations
and the mean retrieval accuracy of the O
3
and CH
4
profiles increase 9.30% and 4.90%
respectively. This research has important theoretical and application value
which can provide some essential technological support for the development and data application of ultra-spectral TIR sensor for our country in the future.
关键词
遥感热红外数据超光谱通道选择雅可比气体敏感性O3和CH4廓线反演
Keywords
remote sensingthermal infrared dataultra-spectralchannel selectionjacobiansgas sensitivityO3 and CH4 profile retrieval
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Aerospace Information Research Institute, Chinese Academy of Sciences
State Key Laboratory of Remote Sensing Science, Beijing Normal University
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Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University
Academy of Plateau Science and Sustainability, Qinghai Normal University
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