甲烷柱浓度红外高光谱遥感反演与验证

周敏强; 倪启晨; 王佳欣; 蔡兆男; 南卫东; 王普才

doi:10.11834/jrs.20242530

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甲烷柱浓度红外高光谱遥感反演与验证
CH₄ column retrievals from ground-and space-based infrared spectra and satellite validation
2024年28卷第8期页码：1968-1985
收稿：2022-10-12，

纸质出版：2024-08-07
DOI： 10.11834/jrs.20242530
稿件说明：

移动端阅览

周敏强，倪启晨，王佳欣，蔡兆男，南卫东，王普才.2024.甲烷柱浓度红外高光谱遥感反演与验证.遥感学报，28（8）： 1968-1985 DOI： 10.11834/jrs.20242530.

Zhou M Q， Ni Q C， Wang J X， Cai Z N， Nan W D and Wang P C. 2024. CH₄ column retrievals from ground-and space-based infrared spectra and satellite validation. National Remote Sensing Bulletin， 28（8）：1968-1985 DOI： 10.11834/jrs.20242530.

摘要

甲烷（CH

）浓度变化是当前气候变化研究中的一个焦点问题。红外高光谱遥感技术已经成为探测大气CH

浓度变化的重要技术手段。本文针对红外高光谱CH

地基遥感反演技术，介绍了国际上重要的观测网络，包括TCCON（Total Carbon Column Observing Network），NDACC-IRWG（Network for the Detection of Atmospheric Composition Change-the Infrared Working Group）和COCCON（COllaborative Carbon Column Observing Network），讨论了这些观测网络的主要特点，包括观测仪器、波段设置、反演算法、产品特性等。针对红外高光谱CH

浓度卫星遥感反演，概述了国际上的CH

卫星遥感发展现状。同时，以日本GOSAT（Greenhouse gases Observing SATellite）卫星为例，探讨了卫星CH

遥感地基验证工作中的关键技术，阐明了地基遥感对于卫星CH

遥感产品改进的重要性。最后，利用TCCON香河站的观测资料，对最新版的TROPOMI（TROPOspheric Monitoring Instrument）卫星CH

观测反演数据产品进行了地基验证，表明在华北地区TROPOMI CH

柱浓度产品达到了其观测精度目标设定要求；TROPOMI观测得到的CH

柱浓度年增长率要略高于TCCON的观测结果，两者相差0.263±0.172%/a；地基与卫星的差值具有季节变化特征，春季卫星的观测值大于TCCON观测值约0.3%，秋季卫星的观测值小于TCCON观测值约0.2%。

Abstract

Methane (CH

) is the second most important greenhouse gas in the Earth’s atmosphere

after carbon dioxide (CO

). Understanding the change in CH

concentration is a challenging task in atmospheric research given that it has various sources. Remote sensing has now become an effective technique to monitor CH

concentrations globally. In this study

we presented an overview of CH

column retrievals based on ground-based Fourier Transform Infrared spectrometer (FTIR) and space-based infrared measurements. Satellite validations were also discussed.

Currently

three ground-based remote sensing international observation networks provide CH

columns: the Total Carbon Column Observing Network (TCCON)

the NDACC-IRWG (Network for the Detection of Atmospheric Composition Change - the Infrared Working Group)

and the COCCON (COllaborative Carbon Column Observing Network). The main characteristics of the three networks were presented and discussed in our study

such as the measuremen

t instrument

the observed spectra

the retrieval algorithm

and the post-correction. TCCON and COCCON provide dry-air column-averaged mole fraction of CH

(XCH

) measurements

with a systematic/random uncertainty of 0.1/0.5%. NDACC provides a total column of CH

with a slightly large systematic/random uncertainty of 0.2/1.0%. However

it also provides a vertical profile of CH

which allows us to observe the CH

variations in the troposphere and stratosphere separately.

Regarding the satellite CH

retrievals

we compared several popular sensors with a nadir-view geometry and their retrieval algorithms

such as the TANSO-FTS/GOSAT

TROPOMI/S5P

IASI/MetOp

and AIRS/Aqua. Basically

the short-wave infrared measurements (GOSAT and TROPOMI) have more sensitivity to the low troposphere

while the thermal infrared measurements (IASI and AIRS) are mainly sensitive to the mid- and upper troposphere. The difference in their vertical sensitivity comes from the CH

-specific absorption lines in the infrared region. All satellite retrievals are affected by the cloud

aerosol

and surface parameters. They also need to be validated and calibrated against ground-based measurements. Here

key steps during the satellite CH

validation were discussed

including the statistical parameters

the a priori substitution

the smoothing correction

and the surface altitude correction.

Finally

we showed the CH

retrievals observed by the ground-based FTIR system at Xianghe

North China. We operated TCCON-type and NDACC-type measurements for the Bruker 125HR instrument and COCCON-type measurements for the Bruker EM27/SUN instrument. The entire FTIR measurement system at Xianghe was well described. Then

we used the TCCON

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690320&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690329&type=

5.41866684

2.87866688

measurements to validate the co-located TROPOMI satellite observations within 50 km at Xianghe. The mean difference between TCCON and TROPOMI

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690312&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690297&type=

5.41866684

2.87866688

measurements from June 2018 to May 2021 is 0.109% (nearly 2 ppb)

which is within the retrieval uncertainty of the TROPOMI measurement. Moreover

a high correlation (

= 0.92) is found between TCCON and TROPOMI

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690335&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690358&type=

5.41866684

2.87866688

measurements at Xianghe. However

the annual growth of

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690312&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690297&type=

5.41866684

2.87866688

derived from the TROPOMI satellite measurements is 0.263% ± 0.172%/year larger than that derived from the TCCON measurements. Besides

seasonal variation is observed in the differences between TCCON and TROPOMI

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690312&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=99690297&type=

5.41866684

2.87866688

measurements

and the differences are obvious when the surface albedo is less than 0.1. Therefore

further investigations are needed to improve the TROPOMI CH

retrievals in North China.

关键词

Keywords

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