高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

面向我国碳中和、碳达峰的大气甲烷观测卫星现状与发展趋势分析

姚璐 杨东旭 蔡兆男 朱思虹 刘毅 邓剑波 田龙飞 尹增山 卢乃锰

姚璐, 杨东旭, 蔡兆男, 等. 2022. 面向我国碳中和、碳达峰的大气甲烷观测卫星现状与发展趋势分析[J]. 大气科学, 46(6): 1469−1483 doi: 10.3878/j.issn.1006-9895.2207.22096
引用本文: 姚璐, 杨东旭, 蔡兆男, 等. 2022. 面向我国碳中和、碳达峰的大气甲烷观测卫星现状与发展趋势分析[J]. 大气科学, 46(6): 1469−1483 doi: 10.3878/j.issn.1006-9895.2207.22096
YAO Lu, YANG Dongxu, CAI Zhaonan, et al. 2022. Status and Trend Analysis of Atmospheric Methane Satellite Measurement for Carbon Neutrality and Carbon Peaking in China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1469−1483 doi: 10.3878/j.issn.1006-9895.2207.22096
Citation: YAO Lu, YANG Dongxu, CAI Zhaonan, et al. 2022. Status and Trend Analysis of Atmospheric Methane Satellite Measurement for Carbon Neutrality and Carbon Peaking in China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1469−1483 doi: 10.3878/j.issn.1006-9895.2207.22096

面向我国碳中和、碳达峰的大气甲烷观测卫星现状与发展趋势分析

doi: 10.3878/j.issn.1006-9895.2207.22096
基金项目: 国家重点研发计划项目2021YFB3901000,国家自然科学基金项目41905029、42105113
详细信息
    作者简介:

    姚璐,女,1989年出生,博士,主要从事短波红外温室气体卫星遥感反演算法研究。E-mail: yaolu@mail.iap.ac.cn

    通讯作者:

    杨东旭,E-mail: yangdx@mail.iap.ac.cn

  • 中图分类号: P412

Status and Trend Analysis of Atmospheric Methane Satellite Measurement for Carbon Neutrality and Carbon Peaking in China

Funds: National Key Research and Development Program of China (Grant 2021YFB3901000), National Natural Science Foundation of China (Grants 41905029, 42105113)
  • 摘要: 甲烷(CH4)是辐射强迫仅次于二氧化碳(CO2)的重要温室气体,减少CH4排放是控制全球增温,实现碳中和目标的必要手段。面对碳中和战略需求,快速定位排放源并定量监测CH4排放量,准确估算全球和区域CH4源汇分布,对减排措施的制定、实施和评价均具有重要的现实意义。此外,结合长期CH4观测数据和气候系统模型探索大气CH4浓度变化规律,是预测和积极应对气候变化的前提。IPCC 2006年国家温室气体清单指南2019修订版正式提出了利用“自上而下”方法计算通量、核验排放清单的方法,表明获取全球范围内的高精度高时空分辨率CH4观测数据势在必行。为了实现碳中和目标,本文首先从大气CH4研究需解决的几个关键科学问题入手,分析了CH4的星载探测需求,总结了CH4星载探测的现状和发展趋势,并简要介绍了中国第二代碳卫星的设计思路。同时,星载CH4探测还依赖于高精度的反演算法提供可靠的数据产品,以实现监测和实际应用的目的。因此,本文进一步阐述了卫星遥感CH4反演算法及相应数据产品在排放量监测和通量反演中的应用,论述了提升反演算法计算效率和精度,开发甲烷烟羽快速识别算法和建立通量反演算法的必要性。最后,本文从探测、数据获取和应用的角度进行总结,表明了CH4卫星观测在碳中和目标实践中的科学应用潜能。
  • 表  1  已结束及在轨运行的星载CH4探测卫星性能总结

    Table  1.   Instrument characteristics of ended and in-orbit CH4 satellites

    卫星SCIAMACHYGOSATSentinel-5pFY-3DGF-5GOSAT-2GHGSat
    (D/C1/C2)
    发射时间2002年2009年2017年2017年2018年2018年2016年
    主/被动被动被动被动被动被动被动被动
    轨道类型极轨极轨极轨极轨极轨极轨极轨
    当地时10:0012:45~13:1513:3014:0013:3012:45~13:1509:30
    轨道高度/km790666824836705613512
    倾角98.5°98.0°98.74°98.75°98.2°97.8°97.5°
    星下点分辨率30 km×60 km10.5 km (d)7 km×7 km10 km (d)10.3 km (d)9.7 km (d)约0.03 km×
    0.03 km
    幅宽/km96079026002250185090312
    回访周期/d3531662314
    搭载探测器8通道光栅光谱仪TANSO-FTS,
    TANSO-CAI
    TROPOMIGAS, FTSGMITANSO-FTS2,
    TANSO-CAI2
    WAF-P Imaging
    波长带宽/μm0.24~0.44
    0.4~1.0
    1.0~1.7
    1.94~2.04
    2.265~2.38
    0.76~0.78
    1.56~1.72
    1.92~2.08
    5.56~14.30
    0.27~0.30
    0.30~0.32
    0.31~0.41
    0.41~0.50
    0.68~0.73
    0.73~0.78
    2.31~2.39
    0.75~0.77
    1.56~1.72
    1.92~2.08
    2.20~2.38
    0.76~0.77
    1.57~1.58
    1.64~1.66
    2.04~2.06
    0.75~0.77
    1.56~1.69
    1.92~2.38
    5.6~14.30
    1.630~1.675
    信噪比<100@1.57300@0.75~0.77
    300@1.56~1.72
    300@1.92~2.08
    300@5.5~14.3
    500@0.71~0.775
    100@2.305~2.385
    320@0.76
    260~300@1.61
    160~300@2.0
    140~300@2.3
    300@0.76
    300@1.58
    250@1.65
    250@2.05
    400@0.75~0.77
    300@1.56~1.69
    300@1.92~2.33
    300@5.5~8.4
    300@8.4~14.3
    200
    观测方式临边,天底天底,耀斑,目标

    天底天底,耀斑,目标

    天底,耀斑,掩星

    天底,耀斑,目标

    目标
    气体观测目标O3, O4, N2O, NO2, CH4, CO, CO2, H2O, SO2, HCHOCO2, CH4, O3, H2ONO2, O3, SO2, HCHO, CH4, COCO2, CH4, CO, N2OCO2, CH4,
    NO2, CO2, SO2,
    大气气溶胶
    CO2, CH4, O2,
    O3, H2O, CO,
    黑碳, PM2.5
    CO2, CH4,
    注:d表示直径。
    下载: 导出CSV

    表  2  计划中的CH4探测卫星性能总结

    Table  2.   Instrument characteristics of scheduled CH4 satellites

    卫星MicroCarbSentinel -5GeoCarbMERLINMethaneSAT
    发射时间2023年2024年2025年2027年2023年
    主/被动被动被动被动主动被动
    轨道类型极轨极轨静止极轨极轨
    当地时10:3009:3006:00/18:00
    轨道高度/km64981735768500585
    倾角98°98.7°97.4°
    星下点分辨率4.5 km×9 km7 km×7 km2.7 km×5.4 km0.15 km×0.15 km0.13 km×0.4 km
    幅宽/km13.527150.1260
    回访周期/d21292~8 h28
    搭载探测器光栅光谱仪UVNS四通道狭缝成像光栅光谱仪IPDA雷达光栅光谱仪
    波长带宽/μm0.76~0.77
    1.26~1.28
    1.60~1.62
    2.04~2.08
    0.27~0.30
    0.30~0.37
    0.37~0.50
    0.69~0.71
    0.75~0.76
    0.76~0.77
    1.59~1.67
    2.31~2.39
    0.65~0.77
    1.59~1.62
    2.04~2.08
    2.20~2.38
    1.64555/
    1.64585
    1.249~1.305
    1.605~1.683
    观测方式天底,耀斑,目标

    天底天底,目标
    天底天底,目标
    气体观测目标CO2, CH4
    O3, NO2, SO2, HCHO, CO, CH4,气溶胶光学, 厚度CO, CH4CH4CO2, CH4
    下载: 导出CSV
  • [1] Alberti C, Hase F, Frey M, et al. 2022. Improved calibration procedures for the EM27/SUN spectrometers of the COllaborative Carbon Column Observing Network (COCCON) [J]. Atmos. Meas. Tech., 15(8): 2433−2463. doi: 10.5194/amt-15-2433-2022
    [2] Bergamaschi P, Frankenberg C, Meirink J F, et al. 2009. Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals [J]. J. Geophys. Res. Atmos., 114(22): 1−28. doi: 10.1029/2009JD012287
    [3] Bergamaschi P, Corazza M, Karstens U, et al. 2015. Top-down estimates of European CH4 and N2O emissions based on four different inverse models [J]. Atmos. Chem. Phys., 15(2): 715−736. doi: 10.5194/acp-15-715-2015
    [4] Bi Y M, Zhang P, Yang Z D, et al. 2022. Fast CO2 retrieval using a semi-physical statistical model for the high-resolution spectrometer on the Fengyun-3D satellite [J]. J. Meteor. Res., 36(2): 374−386. doi: 10.1007/s13351-022-1149-8
    [5] Boesch H, Baker D, Connor B, et al. 2011. Global characterization of CO2 column retrievals from shortwave-infrared satellite observations of the Orbiting Carbon Observatory-2 mission [J]. Remote Sens., 3(2): 270−304. doi: 10.3390/rs3020270
    [6] Buchwitz M, De Beek R, Bramstedt K, et al. 2004. Global carbon monoxide as retrieved from Sciamachy by WFM-DOAS [J]. Atmos. Chem. Phys., 4(7): 1945−1960. doi: 10.5194/acp-4-1945-2004
    [7] Buchwitz M, De Beek R, Noël S, et al. 2006. Atmospheric carbon gases retrieved from SCIAMACHY by WFM-DOAS: Version 0.5 CO and CH4 and impact of calibration improvements on CO2 retrieval [J]. Atmos. Chem. Phys., 6(9): 2727−2751. doi: 10.5194/acp-6-2727-2006
    [8] Buchwitz M, Reuter M, Schneising O, et al. 2015. The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets [J]. Remote Sens. Environ., 162: 344−362. doi: 10.1016/j.rse.2013.04.024
    [9] Buchwitz M, Schneising O, Reuter M, et al. 2017. Satellite-derived methane hotspot emission estimates using a fast data-driven method [J]. Atmos. Chem. Phys., 17(9): 5751−5774. doi: 10.5194/acp-17-5751-2017
    [10] Butz A, Hasekamp O P, Frankenberg C, et al. 2010. CH4 retrievals from space-based solar backscatter measurements: Performance evaluation against simulated aerosol and cirrus loaded scenes [J]. J. Geophys. Res. Atmos., 115(D24): D24302. doi: 10.1029/2010JD014514
    [11] Butz A, Guerlet S, Hasekamp O, et al. 2011. Toward accurate CO2 and CH4 observations from GOSAT [J]. Geophys. Res. Lett., 38(14): L14812. doi: 10.1029/2011GL047888
    [12] Cai Z N, Che K, Liu Y, et al. 2021. Decreased anthropogenic CO2 emissions during the COVID-19 pandemic estimated from FTS and MAX-DOAS measurements at urban Beijing [J]. Remote Sens., 13(3): 517. doi: 10.3390/rs13030517
    [13] 蔡兆男, 成里京, 李婷婷, 等. 2021. 碳中和目标下的若干地球系统科学和技术问题分析 [J]. 中国科学院院刊, 36(5): 602−613. doi: 10.16418/j.issn.1000-3045.20210402002

    Cai Zhaonan, Cheng Lijing, Li Tingting, et al. 2021. Key scientific and technical issues in earth system science towards achieving carbon neutrality in China [J]. Bull. Chinese Acad. Sci. (in Chinese), 36(5): 602−613. doi: 10.16418/j.issn.1000-3045.20210402002
    [14] Checa-Garcia R, Landgraf J, Galli A, et al. 2015. Mapping spectroscopic uncertainties into prospective methane retrieval errors from Sentinel-5 and its precursor [J]. Atmos. Meas. Tech., 8(9): 3617−3629. doi: 10.5194/amt-8-3617-2015
    [15] Chen J, Dietrich F, Maazallahi H, et al. 2020. Methane emissions from the Munich Oktoberfest [J]. Atmos. Chem. Phys., 20(6): 3683−3696. doi: 10.5194/acp-20-3683-2020
    [16] 陈良富, 尚华哲, 范萌, 等. 2021. 高分五号卫星大气参数探测综述 [J]. 遥感学报, 25(9): 1917−1931. doi: 10.11834/jrs.20210582

    Chen Liangfu, Shang Huazhe, Fan Meng, et al. 2021. Mission overview of the GF-5 satellite for atmospheric parameter monitoring [J]. Natl. Remote Sens. Bull. (in Chinese), 25(9): 1917−1931. doi: 10.11834/jrs.20210582
    [17] Crevoisier C, Nobileau D, Fiore A M, et al. 2009. A new insight on tropospheric methane in the Tropics-first year from IASI hyperspectral infrared observations [J]. Atmos. Chem. Phys., 9(2): 6855−6887. doi: 10.5194/acpd-9-6855-2009
    [18] Crisp D, Fisher B M, O’Dell C, et al. 2012. The ACOS CO2 retrieval algorithm. Part II: Global $ {\rm{X}}_{{{\rm{CO}}}_2}$ data characterization [J]. Atmos. Meas. Tech., 5(4): 687−707. doi: 10.5194/amt-5-687-2012
    [19] Crisp D, Meijer Y, Munro R, et al. 2018. A constellation architecture for monitoring carbon dioxide and methane from space [R]. Committee on Earth Observation Satellites.
    [20] De Vrese P, Stacke T, Kleinen T, et al. 2021. Diverging responses of high-latitude CO2 and CH4 emissions in idealized climate change scenarios [J]. Cryosphere, 15(2): 1097−1130. doi: 10.5194/tc-15-1097-2021
    [21] 邓剑波. 2015. 短波红外卫星遥感甲烷大气柱平均干空气混合比反演算法研究 [D]. 中国科学院大学博士学位论文. Deng Jianbo. 2015. Retrieval algorithm for XCH4 with shortwave infrared satellite observations [D]. Ph. D. dissertation (in Chinese), University of Chinese Academy of Sciences.
    [22] Deng J B, Liu Y, Yang D X, et al. 2014. CH4 retrieval from hyperspectral satellite measurements in short-wave infrared: Sensitivity study and preliminary test with GOSAT data [J]. Chinese Sci. Bull., 59(14): 1499−1507. doi: 10.1007/s11434-014-0245-2
    [23] Dupuy E, Morino I, Deutscher N M, et al. 2016. Comparison of XH2O retrieved from GOSAT short-wavelength infrared spectra with observations from the TCCON network [J]. Remote Sens., 8(5): 414. doi: 10.3390/rs8050414
    [24] Ehret G, Bousquet P, Pierangelo C, et al. 2017. MERLIN: A French-German space lidar mission dedicated to atmospheric methane [J]. Remote Sens., 9(10): 1052. doi: 10.3390/rs9101052
    [25] Feng L, Palmer P I, Zhu S H, et al. 2022. Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate [J]. Nat. Commun., 13(1): 1378. doi: 10.1038/s41467-022-28989-z
    [26] Forster P, Ramaswamy P, Artaxo P, et al. 2007. Changes in atmospheric constituents and in radiative forcing [M]//IPCC. Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (in Chinese). Cambridge: Cambridge University Press, 204.
    [27] GCOS-200. 2016. The Global Observing System for Climate: Implementation Needs [R]. WMO.
    [28] GHG-CCI. 2020. User Requirements Document for the GHG-CCI+ project of ESA’ s Climate Change Initiative, pp. 42, version 3.0.
    [29] Han G, Xu H, Gong W, et al. 2018. Feasibility study on measuring atmospheric CO2 in urban areas using spaceborne CO2-IPDA LIDAR [J]. Remote Sens., 10(7): 985. doi: 10.3390/rs10070985
    [30] Hase F, Frey M, Blumenstock T, et al. 2015. Application of portable FTIR spectrometers for detecting greenhouse gas emissions of the major city Berlin [J]. Atmos. Meas. Tech., 8(7): 3059−3068. doi: 10.5194/amt-8-3059-2015
    [31] Hu H L, Hasekamp O, Butz A, et al. 2016. The operational methane retrieval algorithm for TROPOMI [J]. Atmos. Meas. Tech., 9(11): 5423−5440. doi: 10.5194/amt-9-5423-2016
    [32] IPCC, 2018. Annex I: Glossary [Matthews, J. B. R. (ed.)]. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V. , P. Zhai, H. -O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds. )]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 541−562, doi:10.1017/9781009157940.008.
    [33] Jervis D, McKeever J, Durak B O A, et al. 2021. The GHGSat-D imaging spectrometer [J]. Atmos. Meas. Tech., 14(3): 2127−2140. doi: 10.5194/amt-14-2127-2021
    [34] Jones T S, Franklin J E, Chen J, et al. 2021. Assessing urban methane emissions using column observing portable Fourier transform infrared (FTIR) spectrometers and a novel Bayesian inversion framework [J]. Atmos. Chem. Phys., 21(17): 13131−13147. doi: 10.5194/acp-21-13131-2021
    [35] Lan X, Thoning K W, and Dlugokencky E J. 2022. Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements. Version 2022-10, https://doi.org/10.15138/P8XG-AA10
    [36] 刘良云, 陈良富, 刘毅, 等. 2022. 全球碳盘点卫星遥感监测方法、进展与挑战 [J]. 遥感学报, 26(2): 243−267. doi: 10.11834/jrs.20221806

    Liu Liangyun, Chen Liangfu, Liu Yi, et al. 2022. Satellite remote sensing for global stocktaking: Methods, progress and perspectives [J]. Natl. Remote Sens. Bull. (in Chinese), 26(2): 243−267. doi: 10.11834/jrs.20221806
    [37] 刘毅, 杨东旭, 蔡兆男. 2013. 中国碳卫星大气CO2反演方法: GOSAT数据初步应用 [J]. 科学通报, 58(11): 996–999. Liu Yi, Yang Dongxu, Cai Zhaonan. 2013. A retrieval algorithm for TanSat XCO2 observation: Retrieval experiments using GOSAT data [J]. Chinese Sci. Bull. , 58(13): 1520–1523. doi: 10.1007/s11434-013-5680-y
    [38] 刘毅, 王婧, 车轲, 等. 2021. 温室气体的卫星遥感—进展与趋势 [J]. 遥感学报, 25(1): 53−64. doi: 10.11834/jrs.20210081

    Liu Yi, Wang Jing, Che Ke, et al. 2021. Satellite remote sensing of greenhouse gases: Progress and trends [J]. Natl. Remote Sens. Bull. (in Chinese), 25(1): 53−64. doi: 10.11834/jrs.20210081
    [39] Lorente A, Borsdorff T, Butz A, et al. 2021. Methane retrieved from TROPOMI: Improvement of the data product and validation of the first 2 years of measurements [J]. Atmos. Meas. Tech., 14(1): 665−684. doi: 10.5194/amt-14-665-2021
    [40] Lunt M F, Palmer P I, Feng L, et al. 2019. An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data [J]. Atmos. Chem. Phys., 19(23): 14721−14740. doi: 10.5194/acp-19-14721-2019
    [41] Maasakkers J D, Jacob D J, Sulprizio M P, et al. 2019. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 [J]. Atmos. Chem. Phys., 19(11): 7859−7881. doi: 10.5194/acp-19-7859-2019
    [42] Maasakkers J D, Jacob D J, Sulprizio M P, et al. 2021. 2010–2015 North American methane emissions, sectoral contributions, and trends: A high-resolution inversion of GOSAT observations of atmospheric methane [J]. Atmos. Chem. Phys., 21(6): 4339−4356. doi: 10.5194/acp-21-4339-2021
    [43] Meijer Y, Boesch H, Bombelli A, et al. 2020. Copernicus CO2 Monitoring Mission Requirements Document (MRD)[R]. European Space Agency, Earth and Mission Science Division revision 3. https://esamultimedia.esa.int/docs/EarthObservation/CO2M_MRD_v3.0_20201001_Issued.pdf.
    [44] Miller S M, Michalak A M, Detmers R G, et al. 2019. China’s coal mine methane regulations have not curbed growing emissions [J]. Nat. Commun., 10(1): 303. doi: 10.1038/s41467-018-07891-7
    [45] Moore III B, Crowell S M R, Rayner P J, et al. 2018. The Potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas [J]. Front. Environ. Sci., 6: 109. doi: 10.3389/fenvs.2018.00109
    [46] Murray L T, Leibensperger E M, Orbe C, et al. 2021. GCAP 2.0: A global 3-D chemical-transport model framework for past, present, and future climate scenarios [J]. Geosci. Model Dev., 14(9): 5789−5823. doi: 10.5194/gmd-14-5789-2021
    [47] Myhre G, Shindell D, Bréon F M, et al. 2013. Anthropogenic and natural radiative forcing [M]//IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the 5th Assessment Report of the IPCC. Cambridge: Cambridge University Press, 659–740.
    [48] O’ Dell C W, Connor B, Bösch H, et al. 2012. The ACOS CO2 retrieval algorithm-Part 1: Description and validation against synthetic observations [J]. Atmos. Meas. Tech., 5(1): 99−121. doi: 10.5194/amt-5-99-2012
    [49] Palmer P I, Feng L, Lunt M F, et al. 2021. The added value of satellite observations of methane for understanding the contemporary methane budget [J]. Philos. Trans. Roy. Soc. A Mathemat. Phys. Eng. Sci., 379(2210): 20210106. doi: 10.1098/rsta.2021.0106
    [50] Pandey S, Gautam R, Houweling S, et al. 2019. Satellite observations reveal extreme methane leakage from a natural gas well blowout [J]. Proc. Natl. Acad. Sci. USA, 116(52): 26376−26381. doi: 10.1073/pnas.1908712116
    [51] Parker R J, Webb A, Boesch H, et al. 2020. A decade of GOSAT Proxy satellite CH4 observations [J]. Earth Syst. Sci. Data, 12(4): 3383−3412. doi: 10.5194/essd-12-3383-2020
    [52] Payne V H, Clough S A, Shephard M W, et al. 2009. Information-centered representation of retrievals with limited degrees of freedom for signal: Application to methane from the tropospheric emission spectrometer [J]. J. Geophys. Res. Atmos., 114(D10): D10307. doi: 10.1029/2008JD010155
    [53] 朴世龙, 何悦, 王旭辉, 等. 2022. 中国陆地生态系统碳汇估算: 方法、进展、展望 [J]. 中国科学: 地球科学, 52(6): 1010–1020.

    Piao Shilong, He Yue, Wang Xuhui, et al. 2022. Estimation of China’ s Terrestrial ecosystem carbon sink: Methods, progress and prospects [J]. Sci. China Earth Sci. , 65(4): 641-651. doi:10.1007/s11430-021-9892-6
    [54] Plant G, Kort E A, Murray L T, et al. 2022. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations [J]. Remote Sens. Environ., 268: 112756. doi: 10.1016/j.rse.2021.112756
    [55] Qu Z, Jacob D J, Shen L, et al. 2021. Global distribution of methane emissions: A comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments [J]. Atmos. Chem. Phys., 21(18): 14159−14175. doi: 10.5194/acp-21-14159-2021
    [56] Rohrschneider R R, Wofsy S, Franklin J E, et al. 2021. The MethaneSAT Mission [C]//35th Annual Small Satellite Conference.
    [57] Saito R, Patra P K, Sweeney C, et al. 2013. TransCom model simulations of methane: Comparison of vertical profiles with aircraft measurements [J]. J. Geophys. Res. Atmos., 118(9): 3891−3904. doi: 10.1002/jgrd.50380
    [58] Saunois M, Stavert A R, Poulter B, et al. 2020. The Global Methane Budget 2000–2017 [J]. Earth Syst. Sci. Data, 12(3): 1561−1623. doi: 10.5194/essd-12-1561-2020
    [59] Schneising O, Burrows J P, Dickerson R R, et al. 2014. Remote sensing of fugitive methane emissions from oil and gas production in North American tight geologic formations [J]. Earth’ s Fut., 2(10): 548–558. doi:10.1002/2014EF000265
    [60] Schneising O, Buchwitz M, Reuter M, et al. 2019. A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor [J]. Atmos. Meas. Tech., 12(12): 6771−6802. doi: 10.5194/amt-12-6771-2019
    [61] Sierk B, Fernandez V, Bézy J L, et al. 2021. The Copernicus CO2M mission for monitoring anthropogenic carbon dioxide emissions from space [C]//Proceedings Volume 11852, International Conference on Space Optics — ICSO 2020. ICSO, 118523M. doi:10.1117/12.2599613
    [62] Somkuti P, O’ Dell C W, Crowell S, et al. 2021. Solar-induced chlorophyll fluorescence from the Geostationary Carbon Cycle Observatory (GeoCarb): An extensive simulation study [J]. Remote Sens. Environ., 263: 112565. doi: 10.1016/j.rse.2021.112565
    [63] Stanevich I, Jones D B A, Strong K, et al. 2020. Characterizing model errors in chemical transport modeling of methane: Impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model [J]. Geosci. Model Dev., 13(9): 3839−3862. doi: 10.5194/gmd-13-3839-2020
    [64] 孙允珠, 蒋光伟, 李云端, 等. 2018. “高分五号”卫星概况及应用前景展望 [J]. 航天返回与遥感, 39(3): 1−13. doi: 10.3969/j.issn.1009-8518.2018.03.001

    Sun Yunzhu, Jiang Guangwei, Li Yunduan, et al. 2018. GF-5 satellite: Overview and application prospects [J]. Spacecr. Recov. Remote Sens. (in Chinese), 39(3): 1−13. doi: 10.3969/j.issn.1009-8518.2018.03.001
    [65] Suto H, Kataoka F, Kikuchi N, et al. 2021. Thermal and near-infrared sensor for carbon observation Fourier transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse gases Observing SATellite-2 (GOSAT-2) during its first year in orbit [J]. Atmos. Meas. Tech., 14(3): 2013−2039. doi: 10.5194/amt-14-2013-2021
    [66] Tenkanen M, Tsuruta A, Rautiainen K, et al. 2021. Utilizing earth observations of soil freeze/thaw data and atmospheric concentrations to estimate cold season methane emissions in the northern high latitudes [J]. Remote Sens., 13(24): 5059. doi: 10.3390/rs13245059
    [67] Tollefson J. 2022. Scientists raise alarm over ‘dangerously fast’ growth in atmospheric methane [J/OL]. Nature. https://www.nature.com/articles/d41586-022-00312-2
    [68] Trishchenko A P, Trichtchenko L D, Garand L. 2019. Highly elliptical orbits for polar regions with reduced total ionizing dose [J]. Adv. Space Res., 63(12): 3761−3767. doi: 10.1016/j.asr.2019.04.005
    [69] Turner A J, Frankenberg C, Kort E A. 2019. Interpreting contemporary trends in atmospheric methane [J]. Proc. Natl. Acad. Sci. USA, 116(8): 2805−2813. doi: 10.1073/pnas.1814297116
    [70] Turner A J, Jacob D J, Wecht K J, et al. 2015. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data [J]. Atmos. Chem. Phys., 15(12): 7049−7069. doi: 10.5194/acp-15-7049-2015
    [71] Varon D J, Jacob D J, Jervis D, et al. 2020. Quantifying time-averaged methane emissions from individual coal mine vents with GHGSat-D satellite observations [J]. Environ. Sci. Technol., 54(16): 10246−10253. doi: 10.1021/acs.est.0c01213
    [72] Varon D J, Jervis D, McKeever J, et al. 2021. High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations [J]. Atmos. Meas. Tech., 14(4): 2771−2785. doi: 10.5194/amt-14-2771-2021
    [73] Von Clarmann T, Degenstein D A, Livesey N J, et al. 2020. Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature [J]. Atmos. Meas. Tech., 13(8): 4393−4436. doi: 10.5194/amt-13-4393-2020
    [74] Wang S B, Ke J, Chen S J, et al. 2020. Performance evaluation of spaceborne integrated path differential absorption lidar for carbon dioxide detection at 1572 nm [J]. Remote Sens., 12(16): 2570. doi: 10.3390/rs12162570
    [75] Wang Y L, Wang X H, Wang K, et al. 2022. The size of the land carbon sink in China [J]. Nature, 603(7901): E7−E9. doi: 10.1038/s41586-021-04255-y
    [76] Weber T, Wiseman N A, Kock A. 2019. Global ocean methane emissions dominated by shallow coastal waters [J]. Nat. Commun., 10(1): 4584. doi: 10.1038/s41467-019-12541-7
    [77] Worden J R, Turner A J, Bloom A, et al. 2015. Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements [J]. Atmos. Meas. Tech., 8(8): 3433−3445. doi: 10.5194/amt-8-3433-2015
    [78] Wunch D, Toon G C, Blavier J F L, et al. 2011. The total carbon column observing network [J]. Philos. Trans. Roy. Soc. A, 369(1943): 2087−2112. doi: 10.1098/rsta.2010.0240
    [79] Xiong X, Barnet C, Maddy E S, et al. 2013. Mid-upper tropospheric methane retrieval from IASI and its validation [J]. Atmos. Meas. Tech., 6(9): 2255−2265. doi: 10.5194/amt-6-2255-2013
    [80] Yang D, Boesch H, Liu Y, et al. 2020a. Toward high precision XCO2 retrievals from TanSat observations: Retrieval improvement and validation against TCCON measurements [J]. J. Geophys. Res. Atmos., 125(22): e2020JD032794. doi: 10.1029/2020JD032794
    [81] Yang D X, Liu Y, Cai Z N, et al. 2015. An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations [J]. Sci. Bull., 60(23): 2063−2066. doi: 10.1007/s11434-015-0953-2
    [82] Yang D X, Zhang H F, Liu Y, et al. 2017. Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China [J]. Adv. Atmos. Sci., 34(8): 965−976. doi: 10.1007/s00376-017-6221-4
    [83] Yang Y, Zhou M Q, Langerock B, et al. 2020b. New ground-based Fourier-transform near-infrared solar absorption measurements of XCO2, XCH4 and XCO at Xianghe, China [J]. Earth Syst. Sci. Data, 12(3): 1679−1696. doi: 10.5194/essd-12-1679-2020
    [84] Yoshida Y, Ota Y, Eguchi N, et al. 2011. Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite [J]. Atmos. Meas. Tech., 4(4): 717−734. doi: 10.5194/amt-4-717-2011
    [85] Yoshida Y, Kikuchi N, Morino I, et al. 2013. Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data [J]. Atmos. Meas. Tech., 6(6): 1533−1547. doi: 10.5194/amt-6-1533-2013
    [86] Zhang Y Z, Jacob D J, Maasakkers J D, et al. 2018. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane [J]. Atmos. Chem. Phys., 18(21): 15959−15973. doi: 10.5194/acp-18-15959-2018
    [87] Zhang Y Z, Gautam R, Pandey S, et al. 2020. Quantifying methane emissions from the largest oil-producing basin in the United States from space [J]. Sci. Adv., 6(17): eaaz5120. doi: 10.1126/sciadv.aaz5120
    [88] Zhang Y Z, Jacob D J, Lu X, et al. 2021. Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations [J]. Atmos. Chem. Phys., 21(5): 3643−3666. doi: 10.5194/acp-21-3643-2021
  • 加载中
表(2)
计量
  • 文章访问数:  72
  • HTML全文浏览量:  15
  • PDF下载量:  21
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-06-08
  • 录用日期:  2022-08-12
  • 网络出版日期:  2022-08-24
  • 刊出日期:  2022-11-24

目录

    /

    返回文章
    返回