基于卫星遥感的中国地区XCO2 和XCH4
时空分布研究

大气与环境光学学报 ›› 2022, Vol. 17 ›› Issue (6): 679-692.

• “新型卫星载荷大气遥感及应用” 专辑 • 上一篇

基于卫星遥感的中国地区XCO2 和XCH4 时空分布研究

加亦瑱1, 陶明辉1∗, 丁思佳1, 刘航语1, 曾铭裕1, 陈良富2

1 中国地质大学(武汉) 地理与信息工程学院, 湖北武汉430074; 2 中国科学院空天信息创新研究院遥感科学国家重点实验室, 北京100101

收稿日期:2022-09-30 修回日期:2022-11-03 出版日期:2022-11-28 发布日期:2022-12-14
通讯作者: E-mail: taomh@cug.edu.cn E-mail:taomh@cug.edu.cn
作者简介:加亦瑱(2000 - ), 女, 河南三门峡人, 硕士研究生, 主要从事大气环境遥感方面的研究。E-mail: jiayz@cug.edu.cn
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 41871262, 42271382)

Spatial and temporal distribution of XCO2 and XCH4 in China based on satellite remote sensing

JIA Yizhen1, TAO Minghui1∗, DING Sijia1, LIU Hangyu1, ZENG Mingyu1, CHEN Liangfu2

1 School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; 2 State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

Received:2022-09-30 Revised:2022-11-03 Published:2022-11-28 Online:2022-12-14
Contact: Ming HuiTAO E-mail:taomh@cug.edu.cn

摘要/Abstract

摘要： 人为活动排放的以CO2 和CH4 为主的大量温室气体是造成全球增温的主要因素。由于地面观测站点稀少, 卫星遥感为监测CO2 和CH4 的时空分布及变化趋势提供了新的技术手段。本文验证了GOSAT、OCO-2 卫星的大气 CO2 和CH4 柱浓度遥感产品XCO2 和XCH4 的精度, 并分析了我国XCO2 和XCH4 的时空分布和变化趋势, 主要结论如下: (1) 在所用XCO2 遥感产品中, OCO-2 ACOS 与地面观测的相关性最高(达0.93); 而XCH4 产品中GOSAT OCPR 的相关性最高(达0.78)。(2) 在研究的时间跨度内, XCO2 浓度呈逐年上升趋势, 如我国OCO-2 XCO2 年均浓度由2014 年的396.92 × 10−6 增长到2021 年的414.72 × 10−6; CO2 浓度高值主要分布在城市和工业集中的中国东部地区, 西北地区塔克拉玛干沙漠的高值与气溶胶散射影响有关; 同时, 受人为源和自然源的季节变化影响, XCO2 具有冬春高、夏秋低的时间特征。(3) XCH4 浓度同样呈逐年上升趋势, 但与XCO2 不同, XCH4 浓度高值主要分布在天然气和煤炭开采集中的四川东部、重庆西部、陕西与山西的中部地区, 以及工业集中的华北地区, 季浓度呈现夏秋高、春冬低的特征。(4) 2020 年XCO2 高值区发生偏移; 相对于2020 年, 2021 年CO2 增速有所回升, 但增幅相对于2019 年之前仍有所减小。

关键词: 卫星遥感, XCO2, XCH4, 时空分布, 反演精度

Abstract: The large amount of greenhouse gases, mainly CO2 and CH4, emitted by human activities is a major contributor to global warming. Due to the scarcity of ground-based observation sites, satellite remote sensing provides a new technical means to monitor the spatial and temporal distribution and trends of CO2 and CH4. In this paper, we verify the accuracy of XCO2 and XCH4, the remote sensing products of atmospheric CO2 and CH4 column concentrations from GOSAT and OCO-2 satellites, and analyze the spatial and temporal distributions and trends of XCO2 and XCH4 in China. The main conclusions are as follows. (1) Among XCO2 remote sensing products, OCO-2 ACOS has the highest correlation with ground observation (up to 0.93); while GOSAT OCPR has the highest correlation with XCH4 products (up to 0.78). (2) In the years of study, XCO2 concentration increases year by year, for example, the annual average OCO-2 XCO2 concentration in China increases from 396.92 × 10−6 in 2014 to 414.72 × 10−6 in 2021. The high values of CO2 concentration are mainly distributed in urban and industrial concentrated East China, and the high values in the Taklamakan Desert in Northwest China are related to the influence of aerosol scattering. And in the other hand, influenced by the seasonality of anthropogenic and natural sources, XCO2 has the temporal characteristics of high in winter and spring and low in summer and autumn. (3) The concentration of XCH4 also increases year by year. Unlike XCO2, the high concentration of XCH4 is distributed in central areas of China such as eastern Sichuan, western Chongqing, central Shaanxi and Shanxi where natural gas and coal mining are concentrated, and the northern China where industry is concentrated. And the seasonal concentration of XCH4 is high in summer and autumn and low in spring and winter. (4) In 2020, the XCO2 high value area shifts. Compared with 2020, the CO2 growth rate of 2021 increases a little, but the increase is still reduced compared with that before 2019.

Key words: satellite remote sensing, XCO2, XCH4, spatial and temporal distribution, inversion accuracy

中图分类号:

P407.4

加亦瑱, 陶明辉∗, 丁思佳, 刘航语, 曾铭裕, 陈良富. 基于卫星遥感的中国地区XCO2 和XCH4 时空分布研究[J]. 大气与环境光学学报, 2022, 17(6): 679-692.

JIA Yizhen, TAO Minghui∗, DING Sijia, LIU Hangyu, ZENG Mingyu, CHEN Liangfu. Spatial and temporal distribution of XCO2 and XCH4 in China based on satellite remote sensing[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(6): 679-692.

参考文献 25

[1]	IPCC. Climate change 2021: The physical science basis [R]. Contribution of Working Group I to the Sixth Assessment Report
	of the Intergovernmental Panel on Climate Change, 2021.
[2]	Etheridge D M, Steele L P, Langenfelds R L, et al. Natural and anthropogenic changes in atmospheric CO2 over the last 1000
	years from air in Antarctic ice and firn [J]. Journal of Geophysical Research, 1996, 101: 4115-4128.
[3]	Etminan M, Myhre G, Highwood E J, et al. Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant
	revision of the methane radiative forcing [J]. Geophysical Research Letters, 2016, 43(24): 12614-12623.
[4]	Prather M J, Holmes C D, Hsu J. Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of
	atmospheric chemistry [J]. Geophysical Research Letters, 2012, 39(9): L09803.
[5]	Liu D. A Study on the Influencing Factors of Individual’s Willingness to Pay for Carbon Neutrality [D]. Wuhan: Wuhan
	University, 2019.
	柳典. 个人“碳中和” 支付意愿影响因素研究[D]. 武汉: 武汉大学, 2019.
[6]	O′dell CW, Eldering A,Wennberg P O, et al. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with
	the version 8 ACOS algorithm [J]. Atmospheric Measurement Techniques, 2018, 11(12): 6539-6576.
[7]	Chevallier F, Palmer P I, Feng L, et al. Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne
	measurements of atmospheric CO2 [J]. Geophysical Research Letters, 2014, 41(3): 1065-1070.
[8]	Wunch D, Toon G C, Blavier J F L, et al. The total carbon column observing network [J]. Philosophical Transactions of the
	Royal Society A: Mathematical, Physical and Engineering Sciences, 2011, 369(1943): 2087-2112.
[9]	Mo L, Wu Z C, Zhang Y. Spatial and temporal variations of XCO2 in China and its influencing factors analysis [J]. China
	Environmental Science, 2021, 41(6): 2562-2570.
	莫露, 巫兆聪, 张熠. 中国XCO2 时空分布与影响因素分析[J]. 中国环境科学, 2021, 41(6): 2562-2570.
[10]	Xia L J, Liu L X, Li B Z, et al. Spatial and temporal distribution characteristics of atmospheric CO2 in central China [J]. China
	Environmental Science, 2018, 38(8): 2811-2819.
	夏玲君, 刘立新, 李柏贞, 等. 我国中部地区大气CO2 柱浓度时空分布[J]. 中国环境科学, 2018, 38(8): 2811-2819.
[11]	Wu C J, Lei, L P, ZengZ C. Spatio-temporal analysis of differences among atmospheric CO2 concentrations retrieved from
	different satellite observations [J]. Journal of the University of Chinese Academy of Sciences, 2019, 36(3): 331-337.
	吴长江, 雷莉萍, 曾招城. 不同卫星反演的大气CO2 浓度差异时空特征分析[J]. 中国科学院大学学报, 2019, 36(3):
33	1-337.
[12]	Liu D D, Huang Y B, Cao Z S, et al. Analysis of total columns of greenhouse gas based on direct observation and comparison
	with satellite data in Hefei [J]. Acta Photonica Sinica, 2020, 49(3): 165-174.
	刘丹丹, 黄印博, 曹振松, 等. 合肥地区温室气体柱浓度直接观测与卫星数据对比分析[J]. 光子学报, 2020, 49(3):
16	5-174.
[13]	Bie N. Spatio-temporal Patterns and Uncertainty Analysis of Satellite Retrieved Atmospheric CO2 Columns in China [D].
	Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 2018.
	别念. 卫星遥感中国区域大气CO2 柱浓度时空特征与不确定性分析[D]. 北京: 中国科学院大学(中国科学院遥感与数
	字地球研究所), 2018.
[14]	Taylor T E, O′dell C W, Crisp D, et al. An 11-year record of XCO2 estimates derived from GOSAT measurements using the
	NASA ACOS version 9 retrieval algorithm [J]. Earth System Science Data, 2022, 14(1): 325-360.
[15]	Butz A, Guerlet S, Hasekamp O, et al. Toward accurate CO2 and CH4 observations from GOSAT [J]. Geophysical Research
	Letters, 2011, 38(14) : L14812.
[16]	ODell C, Eldering A, Gunson M, et al. Improvements in XCO2 accuracy from OCO-2 with the latest ACOS v10 product [C].
	EGU General Assembly Conference Abstracts, April 30, 2021, Online. 2021: EGU21-10484.
[17]	OCO-2/OCO-3 Science Team, Abhishek Chatterjee, Vivienne Payne. OCO-3 level 2 bias-corrected XCO2 and other select
	fields from the full-physics retrieval aggregated as daily files, retrospective processing v10.4r [DS]. Greenbelt, MD, USA,
	Goddard Earth Sciences Data and Information Services Center (GES DISC), 2022.
[18]	Parker R, Boesch H, Cogan A, et al. Methane observations from the Greenhouse Gases Observing SATellite: Comparison to
	ground-based TCCON data and model calculations [J]. Geophysical Research Letters, 2011, 38(15) : L15807.
[19]	Butz A, Hasekamp O P, Frankenberg C, et al. CH4 retrievals from space-based solar backscatter measurements: Performance
	evaluation against simulated aerosol and cirrus loaded scenes [J]. Journal of Geophysical Research: Atmospheres, 2010, 115:
	D24302.
[20]	Wu H. Influence of Cloud and Aerosol in Atmospheric CO2 Inversion and Its Correction Method [D]. Hefei: University of
	Science and Technology of China, 2019.
	吴浩. 大气CO2 反演中云和气溶胶的影响及其校正方法[D]. 合肥: 中国科学技术大学, 2019.
[21]	Chen L F, Zhang Y, Zou M M, et al. Overview of atmospheric CO2 remote sensing from space [J]. Journal of Remote Sensing,
20	15, 19(1): 1-11.
	陈良富, 张莹, 邹铭敏, 等. 大气CO2 浓度卫星遥感进展[J]. 遥感学报, 2015, 19(1): 1-11.
[22]	Wang Z. Analysis of Atmospheric CO2 Characteristics and Source and Sink in China [D]. Xi′an: Chang′an University, 2019.
	王振. 中国区域大气CO2 特征及源汇分析研究[D]. 西安: 长安大学, 2019.
[23]	Tao M, Chen L, Wang J, et al. Characterization of dust activation and their prevailing transport over East Asia based on
	multi-satellite observations [J]. Atmospheric research, 2022, 265: 105886.
[24]	Yang D X, Liu Y, Cai Z N, et al. The spatial and temporal distribution of carbon dioxide over China based on GOSAT
	observations [J]. Chinese Journal of Atmospheric Sciences, 2016, 40(3): 541-550.
	杨东旭, 刘毅, 蔡兆男, 等. 基于GOSAT 反演的中国地区二氧化碳浓度时空分布研究[J]. 大气科学, 2016, 40(3):
54	1-550.
[25]	Zhang S H, Xie B, Zhang H, et al. The spatial-temporal distribution of CH4 over globe and East Asia [J]. China Environmental
	Science, 2018, 38(12): 4401-4408.
	张绍会, 谢冰, 张华, 等. 全球和东亚地区CH4 浓度时空分布特征分析[J]. 中国环境科学, 2018, 38(12): 4401-4408.

基于卫星遥感的中国地区XCO2 和XCH4 时空分布研究

Spatial and temporal distribution of XCO2 and XCH4 in China based on satellite remote sensing

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献 25

相关文章 14

编辑推荐

Metrics

本文评价

[1]	戴刘新, 张莹∗, 李正强, 漏嗣佳. 中国近地面PM2.5 质量浓度卫星遥感数据集比较及历史趋势分析[J]. 大气与环境光学学报, 2022, 17(6): 613-629.
[2]	吴辉∗, 曹久才, 缪明榕, 李超. 京西生态涵养区日照时数变化特征及影响因素[J]. 大气与环境光学学报, 2022, 17(2): 195-204.
[3]	孙睿, 张红, 汪水兵∗, 卫尤文. 长三角区域典型城市臭氧时空分布及其与气象因素相关性研究[J]. 大气与环境光学学报, 2021, 16(6): 483-494.
[4]	苏静明, 洪炎∗, 唐超礼, 严加琪. 基于OMI 的中国区域2005–2020 年间臭氧柱总量随经纬度变化特性研究[J]. 大气与环境光学学报, 2021, 16(6): 495-503.
[5]	汪水兵, 刘桂建, 杨鹏, 张红∗, 洪星园, 朱森, 包翔, 秦志勇. 合肥市臭氧时空分布特征与气象因子影响研究[J]. 大气与环境光学学报, 2021, 16(4): 339-348.
[6]	蔡新立. 基于高分一号遥感卫星的渤海湾悬浮泥沙时空分布反演[J]. 大气与环境光学学报, 2020, 15(2): 134-142.
[7]	张绍会, 谢冰, 张华, 周喜讯. 基于AIRS卫星的全球和东亚地区CO₂时空特征分析[J]. 大气与环境光学学报, 2019, 14(6): 442-454.
[8]	张洪海李超高一博方雪静熊伟麻金继. 中高层OH自由基时空分布特征及其机理研究[J]. 大气与环境光学学报, 2017, 12(4): 292-304.
[9]	毛慧琴张玉环厉青张丽娟. 基于卫星遥感的生物质燃烧排放估算研究进展[J]. 大气与环境光学学报, 2016, 11(6): 402-411.
[10]	秦玮范广强张天舒吕立慧项衍盛世杰. 基于激光雷达和地面监测数据对南京一次沙尘和细粒子污染时空演变特征的分析[J]. 大气与环境光学学报, 2016, 11(4): 270-280.
[11]	梅世玉麻金继张鑫. APEC期间京津冀地区NO2浓度的时空变化特征研究[J]. 大气与环境光学学报, 2016, 11(4): 281-287.
[12]	高一博梅世玉麻金继吴时超张洪海许丹丹. 基于OMI数据2005~2012年中国区域SO2时空变化特征研究[J]. 大气与环境光学学报, 2016, 11(4): 299-312.
[13]	金岭徐亮高闽光童晶晶程巳阳李相贤. 利用SOF-FTIR技术监测化工厂区VOCs排放[J]. 大气与环境光学学报, 2013, 8(6): 416-421.
[14]	吴永红何秀李成才刘晓阳王美华刘启汉毛节泰. 卫星遥感气溶胶光学厚度在北京2008年地面空气质量监测上的应用[J]. 大气与环境光学学报, 2009, 4(4): 266-273.