浏览全部资源
扫码关注微信
纸质出版日期： 2022-05-07 ，
扫 描 看 全 文
李正强，谢一凇，石玉胜，厉青，Cohen Jason，张羽中，韩颖慧，熊伟，刘毅.2022.大气环境卫星温室气体和气溶胶协同观测综述.遥感学报，26（5）： 795-816
Li Z Q，Xie Y S，Shi Y S，Li Q， COHEN J.， Zhang Y Z， Han Y H， Xiong W and Liu Y. 2022. A review of collaborative remote sensing observation of greenhouse gases and aerosol with atmospheric environment satellites. National Remote Sensing Bulletin， 26（5）：795-816
人类排放的温室气体和气溶胶是造成全球气候变暖和大气环境恶化的主要因素，也是大气环境卫星遥感的核心探测目标。与传统的单一探测目标卫星相比，实现同平台的大气温室气体和气溶胶协同监测，对于提高温室气体卫星反演精度、改善“自上而下”碳源汇估算、提升温室气体和气溶胶的人为/自然源区分能力具有重要意义，也是各国航天机构积极发展的空间探测手段。本文对欧、日、中、美等具备温室气体和气溶胶协同监测能力的卫星进行系统的介绍，包括卫星平台、传感器、处理算法和质控验证。按照卫星监测任务和传感器用途，将其分为大气综合探测卫星和温室气体监测卫星两大类，并从碳中和行动和大气环境综合治理等需求出发，提出温室气体和气溶胶协同观测星座（GACOC）的概念及其发展方向，包括主被动卫星组网观测、温室气体和气溶胶高精度联合反演算法、人为排放源识别和定量监测等应用。
Climate change is the most critical issue related to human survival and economic development currently being faced by the whole world. Greenhouse gases (GHGs) and aerosol are the main factors contributing to global warming and atmospheric environmental degradation caused by anthropogenic emissions; thus
they are the core detection targets of satellite remote sensing platforms. Compared with traditional single-target satellites
the collaborative monitoring of GHGs and aerosol on the same airborne platform
“Greenhouse gases and Aerosol Collaborative Observation Constellation” (GACOC)
could significantly improve the accuracy of CO
2
and CH
4
retrieval. This way could improve the ability to estimate the carbon source and sink via the “top-down” method
as well as the ability to distinguish anthropogenic/natural sources of CO
2
CH
4
and atmospheric particulate matters. The GACOC has become an important spatial detection approach actively developed by aerospace agencies of various countries.
This study introduces the satellites launched by the European Union
Japan
China
and the United States that can monitor GHGs and aerosol in one space-borne platform. These satellites are further divided into two categories according to their missions. The first one is the comprehensive atmospheric sounding satellites that independently detect GHGs and aerosol. These satellites can provide the temporal and spatial distribution of columnar CO
2
or CH
4
concentration and aerosol properties in the global context. The representative satellites of this category include ENVISAT
Sentinel-5P
FY-3D
and GF-5
as well as GF-5(02)
DQ-1
DQ-2
and MetOp-SG-A that are about to launch in 1-3 years. The second category is the GHG monitoring satellites. Synchronous aerosol and cloud observations on the same platform provide necessary information for high-precision inversion of GHGs. The typical GHG satellites include GOSAT
GOSAT-2
OCO-2
OCO-3
TanSat
and the ESA-planned CO2M series.
Focusing on the significant national demands such as assessment of carbon neutrality pathways and atmospheric environmental governance
this study also discusses the development tendencies of monitoring GHGs and aerosol within the framework of a collaborative observation constellation.
(1) Identification and quantitative monitoring of large anthropogenic emission sources. The anthropogenic CO
2
/CH
4
and aerosol particles (and other tracers such as NO
2
) emitted from large-scale industrial areas or cities have some similarities in source
environment
and meteorological condition. Therefore
the high-resolution GHGs and aerosol observation by collaborative satellites can be employed to improve the ability to identify
track
and monitor large-scale
fixed
anthropogenic sources more efficiently.
(2) High-precision joint inversion of atmospheric GHGs and aerosol. The scattering of aerosol and cloud greatly impact the inversion accuracy of CO
2
/CH
4
satellite products. The advanced spaceborne technology that combines multi-angle
multi-band
and polarimetric measurements obtain high-precision aerosol optical and microphysical parameters. These parameters can be used to generate observation-based aerosol models when dealing with aerosol scattering during GHG’s inversion
and these models are more appropriate than the traditional models from modeling data.
(3) Active–passive satellite networking. No single satellite can acquire a daily
global-coverage GHG or aerosol product due to the issues such as limited swath width
large number of cloudy pixels
and strict data quality criteria. Therefore
active–passive satellite networking is an essential approach to satisfy the demands of operationally observing the earth. The GACOC could fill in the data gap effectively and generate a spatially–temporally continuous global dataset of GHGs and aerosol. These data can provide a solid foundation for scientific research such as accurate assessments of climate change and dynamic monitoring of the atmospheric environment.
温室气体气溶胶卫星遥感二氧化碳协同观测星座
greenhouse gasesaerosolsatellite remote sensingcarbon dioxidecollaborative observation constellation
Abshire J B, Ramanathan A K, Riris H, Allan G R, Sun X L, Hasselbrack W E, Mao J P, Wu S, Chen J, Numata K, Kawa S R, Yang M Y M and DiGangi J. 2018. Airborne measurements of CO2 column concentrations made with a pulsed IPDA lidar using a multiple-wavelength-locked laser and HgCdTe APD detector. Atmospheric Measurement Techniques, 11(4): 2001-2025 [DOI: 10.5194/amt-11-2001-2018http://dx.doi.org/10.5194/amt-11-2001-2018]
Bertaux J L, Hauchecorne A, Lefèvre F, Bréon F M, Blanot L, Jouglet D, Lafrique P and Akaev P. 2020. The use of the 1.27 µm O2 absorption band for greenhouse gas monitoring from space and application to MicroCarb. Atmospheric Measurement Techniques, 13(6): 3329-3374 [DOI: 10.5194/amt-13-3329-2020http://dx.doi.org/10.5194/amt-13-3329-2020]
Bilal M, Nichol J E and Chan P W. 2014. Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms. Remote Sensing of Environment, 153: 50-60 [DOI: 10.1016/j.rse.2014.07.015http://dx.doi.org/10.1016/j.rse.2014.07.015]
Buchwitz M, de Beek R, Burrows J P, Bovensmann H, Warneke T, Notholt J, Meirink J F, Goede A P H, Bergamaschi P, Körner S, Heimann M and Schulz A. 2005. Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models. Atmospheric Chemistry and Physics, 5(4): 941-962 [DOI: 10.5194/acp-5-941-2005http://dx.doi.org/10.5194/acp-5-941-2005]
Buchwitz M, Rozanov V V and Burrows J P. 2000. A near-infrared optimized DOAS method for the fast global retrieval of atmospheric CH4, CO, CO2, H2O, and N2O total column amounts from SCIAMACHY Envisat-1 nadir radiances. Journal of Geophysical Research: Atmospheres, 105(D12): 15231-15245 [DOI: 10.1029/2000JD900191http://dx.doi.org/10.1029/2000JD900191]
Chen H, Li Q, Wang Z T, Ma P F, Li Y and Zhao A M. 2020. Retrieval of aerosol optical depth using FY3D MERSI2 data. Journal of Geo-Information Science, 22(9): 1887-1896
陈辉, 厉青, 王中挺, 马鹏飞, 李营, 赵爱梅. 2020. 一种基于FY3D/MERSI2的AOD遥感反演方法. 地球信息科学学报, 22(9): 1887-1896 [DOI: 10.12082/dqxxkx.2020.190206http://dx.doi.org/10.12082/dqxxkx.2020.190206]
Che H, Zhang X Y, Xia X, Goloub P, Holben B, Zhao H, Wang Y, Zhang X C, Wang H, Blarel L, Damiri B, Zhang R, Deng X, Ma Y, Wang T, Geng F, Qi B, Zhu J, Yu J, Chen Q and Shi G. 2015. Ground-based aerosol climatology of China: aerosol optical depths from the China Aerosol Remote Sensing Network (CARSNET) 2002-2013. Atmospheric Chemistry and Physics, 15(13): 7619-7652 [DOI: 10.5194/acp-15-7619-2015http://dx.doi.org/10.5194/acp-15-7619-2015]
Chen X, Wang J, Liu Y, Xu X G, Cai Z N, Yang D X, Yan C X and Feng L. 2017. Angular dependence of aerosol information content in CAPI/TanSat observation over land: effect of polarization and synergy with A-train satellites. Remote Sensing of Environment, 196: 163-177 [DOI: 10.1016/j.rse.2017.05.007http://dx.doi.org/10.1016/j.rse.2017.05.007]
Choi M, Kim J, Lee J, Kim M, Park Y J, Holben B, Eck T F, Li Z Q and Song C H. 2018. GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia. Atmospheric Measurement Techniques, 11(1): 385-408 [DOI: 10.5194/amt-11-385-2018http://dx.doi.org/10.5194/amt-11-385-2018]
Choi M, Lim H, Kim J, Lee S, Eck T F, Holben B N, Garay M J, Hyer E J, Saide P E and Liu H Q. 2019. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign. Atmospheric Measurement Techniques, 12(8): 4619-4641 [DOI: 10.5194/amt-12-4619-2019http://dx.doi.org/10.5194/amt-12-4619-2019]
Cohen J B and Wang C E. 2014. Estimating global black carbon emissions using a top-down Kalman Filter approach. Journal of Geophysical Research: Atmospheres, 119(1): 307-323 [DOI: 10.1002/2013JD019912http://dx.doi.org/10.1002/2013JD019912]
Connor B, Bösch H, McDuffie J, Taylor T, Fu D J, Frankenberg C, O’Dell C, Payne V H, Gunson M, Pollock R, Hobbs J, Oyafuso F and Jiang Y B. 2016. Quantification of uncertainties in OCO-2 measurements of XCO2: simulations and linear error analysis. Atmospheric Measurement Techniques, 9(10): 5227-5238 [DOI: 10.5194/amt-9-5227-2016http://dx.doi.org/10.5194/amt-9-5227-2016]
Crisp D, O’Dell C, Eldering A, Fisher B, Oyafuso F, Payne V, Drouin B, Toon G, Laughner J, Somkuti P, McGarragh G, Merrelli A, Nelson R, Gunson M, Frankenberg C, Osterman G, Boesch H, Brown L, Castano R, Christi M, Connor B, McDuffie J, Miller C, Natraj V, O’Brien D, Polonski I, Smyth M, Thompson D and Granat R. 2021. Orbiting Carbon Observatory (OCO) -2: Level 2 Full Physics Algorithm Theoretical Basis Document [OCO D-55207]
de Graaf M, de Haan J and Sanders A. 2019. TROPOMI ATBD of the Aerosol Layer Height [S5P-KNMI-L2-0006-RP]
de Leeuw G, Holzer-Popp T, Bevan S, Davies W H, Descloitres J, Grainger R G, Griesfeller J, Heckel A, Kinne S, Klüser L, Kolmonen P, Litvinov P, Martynenko D, North P, Ovigneur B, Pascal N, Poulsen C, Ramon D, Schulz M, Siddans R, Sogacheva L, Tanré D, Thomas G E, Virtanen T H, von Hoyningen Huene W, Vountas M and Pinnock S. 2015. Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis. Remote Sensing of Environment, 162: 295-315 [DOI: 10.1016/j.rse.2013.04.023http://dx.doi.org/10.1016/j.rse.2013.04.023]
Diner D J, Abdou W A, Ackerman T P, Crean K, Gordon H R, Kahn R A, Martonchik J V, McMuldroch S, Paradise S R, Pinty B, Verstraete M M, Wang M H and West R A. 2008. MISR: Level 2 Aerosol Retrieval Algorithm Theoretical Basis [JPL D-11400]
Diner D J, Martonchik J V, Kahn R A, Pinty B, Gobron N, Nelson D L and Holben B N. 2005. Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land. Remote Sensing of Environment, 94(2): 155-171 [DOI: 10.1016/j.rse.2004.09.009http://dx.doi.org/10.1016/j.rse.2004.09.009]
Dubovik O, Herman M, Holdak A, Lapyonok T, Tanré D, Deuzé J L, Ducos F, Sinyuk A and Lopatin A. 2011. Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations. Atmospheric Measurement Techniques, 4(5): 975-1018 [DOI: 10.5194/amt-4-975-2011http://dx.doi.org/10.5194/amt-4-975-2011]
Dubovik O, Lapyonok T, Litvinov P, Herman M, Fuertes D, Ducos F, Torres B, Derimian Y, Huang X, Lopatin A, Chaikovsky A, Aspetsberger M and Federspiel C. 2014. GRASP: a versatile algorithm for characterizing the atmosphere. SPIE Newsroom, 25 [DOI 10.1117/2.1201408.005558http://dx.doi.org/10.1117/2.1201408.005558]
Dubovik O, Li Z Q, Mishchenko M I, Tanré D, Karol Y, Bojkov B, Cairns B, Diner D J, Espinosa W R, Goloub P, Gu X F, Hasekamp O, Hong J, Hou W Z, Knobelspiesse K D, Landgraf J, Li L, Litvinov P, Liu Y, Lopatin A, Marbach T, Maring H, Martins V, Meijer Y, Milinevsky G, Mukai S, Parol F, Qiao Y L, Remer L, Rietjens J, Sano I, Stammes P, Stamnes S, Sun X B, Tabary P, Travis L D, Waquet F, Xu F, Yan C X and Yin D K. 2019. Polarimetric remote sensing of atmospheric aerosols: instruments, methodologies, results, and perspectives. Journal of Quantitative Spectroscopy and Radiative Transfer, 224: 474-511 [DOI: 10.1016/j.jqsrt.2018.11.024http://dx.doi.org/10.1016/j.jqsrt.2018.11.024]
Dubovik O, Litvinov P, Poustomis F, Vinuesa J, Fuertes D and Ducos F. 2018. ERA - Enhanced Retrieval of Aerosol properties: reference and NRT algorithm prototype for 3MI mission
Ehret G, Bousquet P, Pierangelo C, Alpers M, Millet B, Abshire J B, Bovensmann H, Burrows J P, Chevallier F, Ciais P, Crevoisier C, Fix A, Flamant P, Frankenberg C, Gibert F, Heim B, Heimann M, Houweling S, Hubberten H W, Jöckel P, Law K, Löw A, Marshall J, Agusti-Panareda A, Payan S, Prigent C, Rairoux P, Sachs T, Scholze M and Wirth M. 2017. MERLIN: a French-German space lidar mission dedicated to atmospheric methane. Remote Sensing, 9(10): 1052 [DOI: 10.3390/rs9101052http://dx.doi.org/10.3390/rs9101052]
Eldering A, O'Dell C W, Wennberg P O, Crisp D, Gunson M R, Viatte C, Avis C, Braverman A, Castano R, Chang A, Chapsky L, Cheng C, Connor B, Dang L, Doran G, Fisher B, Frankenberg C, Fu D J, Granat R, Hobbs J, Lee R A M, Mandrake L, McDuffie J, Miller C E, Myers V, Natraj V, O'Brien D, Osterman G B, Oyafuso F, Payne V H, Pollock H R, Polonsky I, Roehl C M, Rosenberg R, Schwandner F, Smyth M, Tang V, Taylor T E, To C, Wunch D and Yoshimizu J. 2017. The Orbiting Carbon Observatory-2: first 18 months of science data products. Atmospheric Measurement Techniques, 10(2): 549-563 [DOI: 10.5194/amt-10-549-2017http://dx.doi.org/10.5194/amt-10-549-2017]
Eldering A, Taylor T E, O’Dell C W and Pavlick R. 2019. The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data. Atmospheric Measurement Techniques, 12(4): 2341-2370 [DOI: 10.5194/amt-12-2341-2019http://dx.doi.org/10.5194/amt-12-2341-2019]
Ge B Y. 2020. Retrieval of Aerosol Optical Parameters Based on Directional Polarimetric Camera (DPC) Onboard the GF-5 Satellite. University of Chinese Academy of Sciences
葛邦宇. 2020. 高分卫星多角度偏振相机(DPC)气溶胶参数反演算法研究. 北京，中国科学院大学
Ge B Y, Mei X D, Li Z Q, Hou W Z, Xie Y, Zhang Y, Xu H, Li K T and Wei Y Y. 2020. An improved algorithm for retrieving high resolution fine-mode aerosol based on polarized satellite data: application and validation for POLDER-3. Remote Sensing of Environment, 247: 111894 [DOI: 10.1016/j.rse.2020.111894http://dx.doi.org/10.1016/j.rse.2020.111894]
Ge B Y, Yang L K, Chen X F, Li Z Q, Mei X D and Liu L. 2018. Study on aerosol optical depth retrieval over land from Himawari-8 data based on dark target method. Journal of Remote Sensing, 22(1): 38-50
葛邦宇, 杨磊库, 陈兴峰, 李正强, 梅笑冬, 刘李. 2018. 暗目标法的Himawari-8静止卫星数据气溶胶反演. 遥感学报, 22(1): 38-50 [DOI: 10.11834/jrs.20187033http://dx.doi.org/10.11834/jrs.20187033]
GHG-CCI Project Team. 2020. GHG-CCI: User Requirements Document for the GHG-CCI+ project of ESA’s Climate Change Initiative, Version 3.0
Giles D M, Sinyuk A, Sorokin M G, Schafer J S, Smirnov A, Slutsker I, Eck T F, Holben B N, Lewis J R, Campbell J R, Welton E J, Korkin S V and Lyapustin A I. 2019. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques, 12(1): 169-209 [DOI: 10.5194/amt-12-169-2019http://dx.doi.org/10.5194/amt-12-169-2019]
Gloudemans A M S, Schrijver H, Kleipool Q, van den Broek M M P, Straume A G, Lichtenberg G, van Hees R M, Aben I and Meirink J F. 2005. The impact of SCIAMACHY near-infrared instrument calibration on CH4 and CO total columns. Atmospheric Chemistry and Physics, 5(9): 2369-2383 [DOI: 10.5194/acp-5-2369-2005http://dx.doi.org/10.5194/acp-5-2369-2005]
Gong S Y and Shi Y S. 2021. Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China. Science of the Total Environment, 784: 147116 [DOI: 10.1016/j.scitotenv.2021.147116http://dx.doi.org/10.1016/j.scitotenv.2021.147116]
Guerlet S, Butz A, Schepers D, Basu S, Hasekamp O P, Kuze A, Yokota T, Blavier J F, Deutscher N M, Griffith D W T, Hase F, Kyro E, Morino I, Sherlock V, Sussmann R, Galli A and Aben I. 2013. Impact of aerosol and thin cirrus on retrieving and validating XCO2 from GOSAT shortwave infrared measurements. Journal of Geophysical Research: Atmospheres, 118(10): 4887-4905 [DOI: 10.1002/jgrd.50332http://dx.doi.org/10.1002/jgrd.50332]
Hagolle O, Dedieu G, Mougenot B, Debaecker V, Duchemin B and Meygret A. 2008. Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: application to Formosat-2 images. Remote Sensing of Environment, 112(4): 1689-1701 [DOI: 10.1016/j.rse.2007.08.016http://dx.doi.org/10.1016/j.rse.2007.08.016]
Han G, Xu H, Gong W, Liu J Q, Du J, Ma X and Liang A L. 2018. Feasibility study on measuring atmospheric CO2 in urban areas using Spaceborne CO2-IPDA LIDAR. Remote Sensing, 10(7): 985 [DOI: 10.3390/rs10070985http://dx.doi.org/10.3390/rs10070985]
Hasekamp O, Lorente A, Hu H L, Butz A, de Brugh J and Landgraf J. 2019. Algorithm Theoretical Baseline Document for Sentinel-5 Precursor Methane Retrieval [SRON-S5P-LEV2-RP-001]
He L J, Wang L C, Li Z Q, Jiang D Y, Sun L, Liu D, Liu L, Yao R, Zhou Z G and Wei J. 2021. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China. Atmospheric Environment, 250: 118265 [DOI: 10.1016/j.atmosenv.2021.118265http://dx.doi.org/10.1016/j.atmosenv.2021.118265]
Heymann J, Reuter M, Hilker M, Buchwitz M, Schneising O, Bovensmann H, Burrows J P, Kuze A, Suto H, Deutscher N M, Dubey M K, Griffith D W T, Hase F, Kawakami S, Kivi R, Morino I, Petri C, Roehl C, Schneider M, Sherlock V, Sussmann R, Velazco V A, Warneke T and Wunch D. 2015. Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm. Atmospheric Measurement Techniques, 8(7): 2961-2980 [DOI: 10.5194/amt-8-2961-2015http://dx.doi.org/10.5194/amt-8-2961-2015]
Holben B N, Eck T F, Slutsker I, Tanré D, Buis J P, Setzer A, Vermote E, Reagan J A, Kaufman Y J, Nakajima T, Lavenu F, Jankowiak I and Smirnov A. 1998. AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1): 1-16 [DOI: 10.1016/S0034-4257(98)00031-5http://dx.doi.org/10.1016/S0034-4257(98)00031-5]
Hsu N C, Jeong M J, Bettenhausen C, Sayer A M, Hansell R, Seftor C S, Huang J and Tsay S C. 2013. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. Journal of Geophysical Research: Atmospheres, 118(16): 9296-9315 [DOI: 10.1002/jgrd.50712http://dx.doi.org/10.1002/jgrd.50712]
Hsu N C, Tsay S C, King M D, Herman J R. 2004. Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569 [DOI: 10.1109/TGRS.2004.824067http://dx.doi.org/10.1109/TGRS.2004.824067]
Huang H L, Ti R F, Zhang D Y, Fang W, Sun X B and Yi W N. 2020. Inversion of aerosol optical depth over land from directional polarimetric camera onboard Chinese Gaofen-5 satellite. Journal of Infrared and Millimeter Waves, 39(4): 454-461
黄红莲, 提汝芳, 张冬英, 方薇, 孙晓兵, 易维宁. 2020. 高分五号卫星偏振遥感陆地上空气溶胶光学厚度. 红外与毫米波学报, 39(4): 454-461 [DOI: 10.11972/j.issn.1001-9014.2020.04.010http://dx.doi.org/10.11972/j.issn.1001-9014.2020.04.010]
Jiang F, Wang H M, Chen J M, Ju W M, Tian X J, Feng S Z, Li G C, Chen Z Q, Zhang S P, Lu X H, Liu J, Wang H K, Wang J, He W and Wu M S. 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmospheric Chemistry and Physics, 21(3): 1963-1985 [DOI: 10.5194/acp-21-1963-2021http://dx.doi.org/10.5194/acp-21-1963-2021]
Kaufman Y J, Tanré D, Remer L A, Vermote E F, Chu A and Holben B N. 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067 [DOI: 10.1029/96JD03988http://dx.doi.org/10.1029/96JD03988]
Kuhlmann G, Broquet G, Marshall J, Clément V, Löscher A, Meijer Y and Brunner D. 2019. Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission. Atmospheric Measurement Techniques, 12(12): 6695-6719 [DOI: 10.5194/amt-12-6695-2019http://dx.doi.org/10.5194/amt-12-6695-2019]
Kuze A, Suto H, Nakajima M and Hamazaki T. 2009. Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. Applied Optics, 48(35): 6716-6733 [DOI: 10.1364/AO.48.006716http://dx.doi.org/10.1364/AO.48.006716]
Levy R C, Remer L A, Mattoo S, Vermote E F and Kaufman Y J. 2007. Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of Geophysical Research: Atmospheres, 112: D13211 [DOI: 10.1029/2006JD007811http://dx.doi.org/10.1029/2006JD007811]
Li B G, Gasser T, Ciais P, Piao S, Tao S, Balkanski Y, Hauglustaine D, Boisier J P, Chen Z, Huang M T, Li L Z, Li Y, Liu H Y, Liu J F, Peng S S, Shen Z H, Sun Z Z, Wang R, Wang T, Yin G D, Yin Y, Zeng H, Zeng Z Z and Zhou F. 2016. The contribution of China’s emissions to global climate forcing. Nature, 531(7594): 357-361 [DOI: 10.1038/nature17165http://dx.doi.org/10.1038/nature17165]
Li Z Q, Hou W Z, Hong J, Zheng F X, Luo D G, Wang J, Gu X F and Qiao Y L. 2018a. Directional Polarimetric Camera (DPC): monitoring aerosol spectral optical properties over land from satellite observation. Journal of Quantitative Spectroscopy and Radiative Transfer, 218: 21-37 [DOI: 10.1016/j.jqsrt.2018.07.003http://dx.doi.org/10.1016/j.jqsrt.2018.07.003]
Li Z Q, Li D H, Li K T, Xu H, Chen X F, Chen C, Xie Y S, Li L, Li L, Li W, Lv Y, Qie L L, Zhang Y and Gu X F. 2015. Sun-sky radiometer observation network with the extension of multi-wavelength polarization measurements. Journal of Remote Sensing, 19(3): 495-519
李正强, 李东辉, 李凯涛, 许华, 陈兴峰, 陈澄, 谢一凇, 李莉, 李雷, 李伟, 吕阳, 伽丽丽, 张莹, 顾行发. 2015. 扩展多波长偏振测量的太阳—天空辐射计观测网. 遥感学报, 19(3): 495-519 [DOI: 10.11834/jrs.20154129http://dx.doi.org/10.11834/jrs.20154129]
Li Z Q, Xu H, Li K T, Li D H, Xie Y S, Li L, Zhang Y, Gu X F, Zhao W, Tian Q J, Deng R R, Su X L, Huang B, Qiao Y L, Cui W Y, Hu Y, Gong C L, Wang Y Q, Wang X F, Wang J P, Du W B, Pan Z Q, Li Z Z and Bu D. 2018b. Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: an overview of sun–sky radiometer observation network (SONET) measurements. Bulletin of the American Meteorological Society, 99(4): 739-755 [DOI: 10.1175/BAMS-D-17-0133.1http://dx.doi.org/10.1175/BAMS-D-17-0133.1]
Liang A L, Gong W, Han G and Xiang C Z. 2017. Comparison of satellite-observed XCO2 from GOSAT, OCO-2, and ground-based TCCON. Remote Sensing, 9(10): 1033 [DOI: 10.3390/rs9101033http://dx.doi.org/10.3390/rs9101033]
Liu Y, Wang J, Che K, Cai Z N, Yang D X and Wu L. 2021. Satellite remote sensing of greenhouse gases: progress and trends. National Remote Sensing Bulletin, 25(1): 53-64
刘毅, 王婧, 车轲, 蔡兆男, 杨东旭, 吴林. 2021. 温室气体的卫星遥感——进展与趋势. 遥感学报, 25(1): 53-64 [DOI: 10.11834/jrs.20210081http://dx.doi.org/10.11834/jrs.20210081]
Liu Y, Wang J, Yao L, Chen X, Cai Z N, Yang D X, Yin Z S, Gu S Y, Tian L F, Lu N M and Lyu D. 2018. The TanSat mission: preliminary global observations. Science Bulletin, 63(18): 1200-1207 [DOI: 10.1016/j.scib.2018.08.004http://dx.doi.org/10.1016/j.scib.2018.08.004]
Lorente A, Borsdorff T, Butz A, Hasekamp O, aan de Brugh J, Schneider A, Wu L H, Hase F, Kivi R, Wunch D, Pollard D F, Shiomi K, Deutscher N M, Velazco V A, Roehl C M, Wennberg P O, Warneke T and Landgraf J. 2021. Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements. Atmospheric Measurement Techniques, 14(1): 665-684 [DOI: 10.5194/amt-14-665-2021http://dx.doi.org/10.5194/amt-14-665-2021]
Lu Q F, Zhou F, Qi C L, Hu X Q, Xu H L and Wu C Q. 2019. Spectral performance evaluation of high-spectral resolution infrared atmospheric sounder onboard FY-3D. Optics and Precision Engineering, 27(10): 2105-2115
陆其峰, 周方, 漆成莉, 胡秀清, 徐寒列, 吴春强. 2019. FY-3D星红外高光谱大气探测仪的在轨光谱精度评估. 光学精密工程, 27(10): 2105-2115 [DOI: 10.3788/OPE.20192710.2105http://dx.doi.org/10.3788/OPE.20192710.2105]
Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS Collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765 [DOI: 10.5194/amt-11-5741-2018http://dx.doi.org/10.5194/amt-11-5741-2018]
Martonchik J V, Diner D J, Kahn R A, Ackerman T P, Verstraete M M, Pinty B and Gordon H R. 1998. Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1212-1227 [DOI: 10.1109/36.701027http://dx.doi.org/10.1109/36.701027]
Meijer Y, Boesch H, Bombelli A, Brunner D, Buchwitz M, Ciais P, Crisp D, Engelen R, Holmlund K, Houweling S, Janssens-Maenhout G, Marshall J, Nakajima M, Pinty B, Scholze M, Bezy J, Drinkwater M, Fehr T, Fernandez V, Loescher A, Nett H and Sierk B. 2020. Copernicus CO2 Monitoring Mission Requirements Document [EOP-SM/3088/YM-ym]
Moore B III, Crowell S M R, Rayner P J, Kumer J, O'Dell C W, O'Brien D, Utembe S, Polonsky I, Schimel D and Lemen J. 2018. The potential of the geostationary carbon cycle observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas. Frontiers in Environmental Science, 6: 109 [DOI: 10.3389/fenvs.2018.00109http://dx.doi.org/10.3389/fenvs.2018.00109]
O'Dell C W, Connor B, Bösch H, O'Brien D, Frankenberg C, Castano R, Christi M, Eldering D, Fisher B, Gunson M, McDuffie J, Miller C E, Natraj V, Oyafuso F, Polonsky I, Smyth M, Taylor T, Toon G C, Wennberg P O and Wunch D. 2012. The ACOS CO2 retrieval algorithm – Part 1: description and validation against synthetic observations. Atmospheric Measurement Techniques, 5(1): 99-121 [DOI: 10.5194/amt-5-99-2012http://dx.doi.org/10.5194/amt-5-99-2012]
O'Dell C W, Eldering A, Wennberg P O, Crisp D, Gunson M R, Fisher B, Frankenberg C, Kiel M, Lindqvist H, Mandrake L, Merrelli A, Natraj V, Nelson R R, Osterman G B, Payne V H, Taylor T E, Wunch D, Drouin B J, Oyafuso F, Chang A, McDuffie J, Smyth M, Baker D F, Basu S, Chevallier F, Crowell S M R, Feng L, Palmer P I, Dubey M, García O E, Griffith D W T, Hase F, Iraci L T, Kivi R, Morino I, Notholt J, Ohyama H, Petri C, Roehl C M, Sha M K, Strong K, Sussmann R, Te Y, Uchino O and Velazco V A. 2018. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm. Atmospheric Measurement Techniques, 11(12): 6539-6576 [DOI: 10.5194/amt-11-6539-2018http://dx.doi.org/10.5194/amt-11-6539-2018]
Olson M R, Wang Y Q, de Foy B, Li Z Q, Bergin M H, Zhang Y X and Schauer J J. 2022. Source attribution of black and Brown carbon near-UV light absorption in Beijing, China and the impact of regional air-mass transport. Science of the Total Environment, 807: 150871 [DOI: 10.1016/j.scitotenv.2021.150871http://dx.doi.org/10.1016/j.scitotenv.2021.150871]
Qie L, Li D H, Li Z Q, Zhang Y, Hou W Z and Chen X F. 2015. A sensitivity study of atmospheric reflectance to aerosol layer height based on multi-angular polarimetric measurements//Proceedings Volume 9678, AOPC 2015: Telescope and Space Optical Instrumentation. Beijing: SPIE [DOI: 10.1117/12.2199671http://dx.doi.org/10.1117/12.2199671]
Reuter M, Buchwitz M, Schneising O, Noël S, Bovensmann H and Burrows J P. 2017. A fast atmospheric trace gas retrieval for hyperspectral instruments approximating multiple scattering—Part 2: application to XCO2 retrievals from OCO-2. Remote Sensing, 9(11): 1102 [DOI: 10.3390/rs9111102http://dx.doi.org/10.3390/rs9111102]
Sanghavi S, Nelson R, Frankenberg C and Gunson M. 2020. Aerosols in OCO-2/GOSAT retrievals of XCO2: an information content and error analysis. Remote Sensing of Environment, 251: 112053 [DOI: 10.1016/j.rse.2020.112053http://dx.doi.org/10.1016/j.rse.2020.112053]
Santer R, Ramon D, Vidot J and Dilligeard E. 2007. A surface reflectance model for aerosol remote sensing over land. International Journal of Remote Sensing, 28(3/4): 737-760 [DOI: 10.1080/01431160600821028http://dx.doi.org/10.1080/01431160600821028]
Schepers D, Guerlet S, Butz A, Landgraf J, Frankenberg C, Hasekamp O, Blavier J F, Deutscher N M, Griffith D W T, Hase F, Kyro E, Morino I, Sherlock V, Sussmann R and Aben I. 2012. Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: performance comparison of proxy and physics retrieval algorithms. Journal of Geophysical Research: Atmospheres, 117(D10): D10307 [DOI: 10.1029/2012JD017549http://dx.doi.org/10.1029/2012JD017549]
Schneising O, Buchwitz M, Burrows J P, Bovensmann H, Bergamaschi P and Peters W. 2009. Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite - Part 2: methane. Atmospheric Chemistry and Physics, 9(2): 443-465 [DOI: 10.5194/acp-9-443-2009http://dx.doi.org/10.5194/acp-9-443-2009]
Schneising O, Buchwitz M, Burrows J P, Bovensmann H, Reuter M, Notholt J, Macatangay R and Warneke T. 2008. Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 1: carbon dioxide. Atmospheric Chemistry and Physics, 8(14): 3827-3853 [DOI: 10.5194/acp-8-3827-2008http://dx.doi.org/10.5194/acp-8-3827-2008]
Schuster G L, Dubovik O and Arola A. 2016. Remote sensing of soot carbon – Part 1: distinguishing different absorbing aerosol species. Atmospheric Chemistry and Physics, 16(3): 1565-1585 [DOI: 10.5194/acp-16-1565-2016http://dx.doi.org/10.5194/acp-16-1565-2016]
Shi G M, Li C C and Ren T. 2014. Sensitivity analysis of single-angle polarization reflectance observed by satellite. Chinese Science Bulletin, 59(14): 1519-1528 [DOI: 10.1007/s11434-014-0213-xhttp://dx.doi.org/10.1007/s11434-014-0213-x]
Shi H L, Li Z W, Ye H H, Luo H Y, Xiong W and Wang X H. 2021. First level 1 product results of the greenhouse gas monitoring instrument on the GaoFen-5 satellite. IEEE Transactions on Geoscience and Remote Sensing, 59(2): 899-914 [DOI: 10.1109/TGRS.2020.2998729http://dx.doi.org/10.1109/TGRS.2020.2998729]
Sinyuk A, Holben B N, Eck T F, Giles D M, Slutsker I, Korkin S, Schafer J S, Smirnov A, Sorokin M and Lyapustin A. 2020. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2. Atmospheric Measurement Techniques, 13(6): 3375-3411 [DOI: 10.5194/amt-13-3375-2020http://dx.doi.org/10.5194/amt-13-3375-2020]
Suto H, Kataoka F, Kikuchi N, Knuteson R O, Butz A, Haun M, Buijs H, Shiomi K, Imai H and Kuze A. 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. Atmospheric Measurement Techniques, 14(3): 2013-2039 [DOI: 10.5194/amt-14-2013-2021http://dx.doi.org/10.5194/amt-14-2013-2021]
Tang F Y, Zhou H J, Wang W H, Yang T P and Si F Q. 2021. Absorbing aerosol index inversion algorithm of TROPOMI and its application. Acta Optica Sinica, 41(16): 1601001
汤付颖, 周海金, 王维和, 杨太平, 司福祺. 2021. TROPOMI吸收性气溶胶指数反演算法及其应用. 光学学报, 41(16): 1601001 [DOI: 10.3788/AOS202141.1601001http://dx.doi.org/10.3788/AOS202141.1601001]
Tanré D, Remer L A, Kaufman Y J, Mattoo S, Hobbs P V, Livingston J M, Russell P B and Smirnov A. 1999. Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS airborne simulator during TARFOX. Journal of Geophysical Research: Atmospheres, 104(D2): 2261-2278 [DOI: 10.1029/1998jd200077http://dx.doi.org/10.1029/1998jd200077]
Taylor T E, O'Dell C W, Frankenberg C, Partain P T, Cronk H Q, Savtchenko A, Nelson R R, Rosenthal E J, Chang A Y, Fisher B, Osterman G B, Pollock R H, Crisp D, Eldering A and Gunson M R. 2016. Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data. Atmospheric Measurement Techniques, 9(3): 973-989 [DOI: 10.5194/amt-9-973-2016http://dx.doi.org/10.5194/amt-9-973-2016]
Wu H. 2019. Influence of Cloud and Aerosol in Atmospheric CO2 Inversion and its Correlation Method. Hefei: University of Science and Technology of China
吴浩. 2019. 大气CO2反演中云和气溶胶的影响及其校正方法. 合肥: 中国科学技术大学
Wu K Y, Hou W Z, Shi Z, Xu H and Wen Y N. 2021. Research progress of aerosol remote sensing retrieval algorithm based on satellite multi-angle observation. Journal of Atmospheric and Environmental Optics, 16(4): 283-298
吴孔逸, 侯伟真, 史正, 许华, 温亚南. 2021. 基于卫星多角度观测的气溶胶遥感反演算法研究进展. 大气与环境光学学报, 16(4): 283-298 [DOI: 10.3969/j.issn.1673-6141.2021.04.001http://dx.doi.org/10.3969/j.issn.1673-6141.2021.04.001]
Wu S C, Wang X H, Ye H H, Li C, An Y and Wang X D. 2021. Atmospheric CO2 cooperative inversion algorithm applied to GF-5 satellite. Acta Optica Sinica, 41(15): 1501002
吴时超, 王先华, 叶函函, 李超, 安源, 王晓迪. 2021. 应用于GF-5卫星的大气CO2协同反演算法. 光学学报, 41(15): 1501002 [DOI: 10.3788/AOS202141.1501002http://dx.doi.org/10.3788/AOS202141.1501002]
Wunch D, Toon G C, Sherlock V, Deutscher N M, Liu C, Feist D G and Wennberg P O. 2015. Documentation for the 2014 TCCON Data Release, CaltechDATA [DOI: 10.14291/tccon.ggg2014.documentation.R0/1221662http://dx.doi.org/10.14291/tccon.ggg2014.documentation.R0/1221662]
Wunch D, Wennberg P O, Osterman G, Fisher B, Naylor B, Roehl C M, O'Dell C, Mandrake L, Viatte C, Kiel M, Griffith D W T, Deutscher N M, Velazco V A, Notholt J, Warneke T, Petri C, De Maziere M, Sha M K, Sussmann R, Rettinger M, Pollard D, Robinson J, Morino I, Uchino O, Hase F, Blumenstock T, Feist D G, Arnold S G, Strong K, Mendonca J, Kivi R, Heikkinen P, Iraci L, Podolske J, Hillyard P W, Kawakami S, Dubey M K, Parker H A, Sepulveda E, García O E, Te Y, Jeseck P, Gunson M R, Crisp D and Eldering A. 2017. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmospheric Measurement Techniques, 10(6): 2209-2238 [DOI: 10.5194/amt-10-2209-2017http://dx.doi.org/10.5194/amt-10-2209-2017]
Xie X D. 2020. Study on the Interactions of Air Pollution, Vegetation and Carbon Dioxide in China. Nanjing: Nanjing University
谢晓栋. 2020. 中国地区大气污染—植被—二氧化碳的相互影响研究. 南京: 南京大学
Xie Y S, Li Z Q, Hou W Z, Zhang Y, Qie L L, Li L, Li K T and Xu H. 2019. Retrieval of fine-mode aerosol optical depth based on remote sensing measurements of Directional Polarimetric Camera onboard GF-5 satellite. Aerospace Shanghai, 36(S2): 219-226
谢一凇, 李正强, 侯伟真, 张洋, 伽丽丽, 李莉, 李凯涛, 许华. 2019. 高分五号卫星多角度偏振成像仪细粒子气溶胶光学厚度遥感反演. 上海航天, 36(S2): 219-226 [DOI: 10.19328/j.cnki.1006-1630.2019.S.033http://dx.doi.org/10.19328/j.cnki.1006-1630.2019.S.033]
Xie Y S, Li Z Q, Zhang X Y, Xu H, Li D H and Li K T. 2015. A new ground-based differential absorption sunphotometer for measuring atmospheric columnar CO2 and preliminary applications//Proceedings Volume 9678, AOPC 2015: Telescope and Space Optical Instrumentation. Beijing: SPIE [DOI: 10.1117/12.2199379http://dx.doi.org/10.1117/12.2199379]
Xiong W. 2019. Greenhouse gases Monitoring Instrument(GMI) on GF-5 satellite (invited). Infrared and Laser Engineering, 48(3): 0303002
熊伟. 2019. “高分五号”卫星大气主要温室气体监测仪(特邀). 红外与激光工程, 48(3): 0303002 [DOI: 10.3788/IRLA201948.0303002http://dx.doi.org/10.3788/IRLA201948.0303002]
Yang D, Boesch H, Liu Y, Somkuti P, Cai Z, Chen X, Di Noia A, Lin C, Lu N, Lyu D, Parker R J, Tian L, Wang M, Webb A, Yao L, Yin Z, Zheng Y, Deutscher N M, Griffith D W T, Hase F, Kivi R, Morino I, Notholt J, Ohyama H, Pollard D F, Shiomi K, Sussmann R, Té Y, Velazco V A, Warneke T and Wunch D. 2020. Toward high precision XCO2 retrievals from TanSat observations: retrieval improvement and validation against TCCON measurements. Journal of Geophysical Research: Atmospheres, 125(22): e2020JD032794 [DOI: 10.1029/2020JD032794http://dx.doi.org/10.1029/2020JD032794]
Ye H H, Wang X H, Wu S C, Li C, Li Z W, Shi H L and Xiong W. 2021. Atmospheric CO2 retrieval method for satellite observations of Greenhouse gases Monitoring Instrument on GF-5. Journal of Atmospheric and Environmental Optics, 16(3): 231-238
叶函函, 王先华, 吴时超, 李超, 李志伟, 施海亮, 熊伟. 2021. 高分五号卫星GMI大气CO2反演方法. 大气与环境光学学报, 16(3): 231-238 [DOI: 10.3969/j.issn.1673-6141.2021.03.008http://dx.doi.org/10.3969/j.issn.1673-6141.2021.03.008]
Zhang X Y, Meng X Y, Zhou M Q, Bai W G, Zhou L H, Hu Y M and Yu X. 2018. Review of the validation of atmospheric CO2 from satellite hyper spectral remote sensing. Climate Change Research, 14(6): 602-612
张兴赢, 孟晓阳, 周敏强, 白文广, 周丽花, 胡玥明, 余骁. 2018. 卫星高光谱大气CO2探测精度验证研究进展. 气候变化研究进展, 14(6): 602-612 [DOI: 10.12006/j.issn.1673-1719.2018.070http://dx.doi.org/10.12006/j.issn.1673-1719.2018.070]
Zhang Y and Li Z Q. 2015. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160: 252-262 [DOI: 10.1016/j.rse.2015.02.005http://dx.doi.org/10.1016/j.rse.2015.02.005]
Zhang Y, Li Z Q, Qie L L, Hou W Z, Liu Z H, Zhang Y, Xie Y S, Chen X F and Xu H. 2017. Retrieval of aerosol optical depth using the empirical orthogonal functions (EOFs) based on PARASOL multi-angle intensity data. Remote Sensing, 9(6): 578 [DOI: 10.3390/rs9060578http://dx.doi.org/10.3390/rs9060578]
Zhao L. 2017. Remote Retrieval of Atmospheric CO2 and CH4 using GOSAT. Changchun: Jilin University
赵靓. 2017. 基于GOSAT卫星的大气CO2和CH4遥感反演研究. 长春: 吉林大学
Zheng F X, Zhu J Y, Hou W Z and Li Z Q. 2021. Effect analysis of using different polarization quantities in aerosol retrieval from satellite observation. Spectroscopy and Spectral Analysis, 41(7): 2212-2218
郑逢勋, 朱家乙, 侯伟真, 李正强. 2021. 卫星遥感中不同偏振量对气溶胶反演的影响分析. 光谱学与光谱分析, 41(7): 2212-2218 [DOI: 10.3964/j.issn.1000-0593http://dx.doi.org/10.3964/j.issn.1000-0593(202107-2212-07]
Zweers D C S. 2018. TROPOMI ATBD of the UV aerosol index [S5P-KNMI-L2-0008-RP]
相关作者
相关机构
京公网安备11010802024621