Specific patterns of XCO2 observed by GOSAT during 2009–2016 and assessed with model simulations over China-Reference-Cited by-同舟云学术

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Specific patterns of XCO2 observed by GOSAT during 2009–2016 and assessed with model simulations over China

Published:2020-01-13 Issue:3 Volume:63 Page:384-394
ISSN:1674-7313
Container-title:Science China Earth Sciences
language:en
Short-container-title:Sci. China Earth Sci.

Author:

Bie Nian,Lei Liping,He Zhonghua,Zeng Zhaocheng,Liu Liangyun,Zhang Bing,Cai Bofeng

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

http://link.springer.com/content/pdf/10.1007/s11430-018-9377-7.pdf

Reference33 articles.

1. Bie N, Lei L, He Z, Liu M. 2016. An analysis of atmospheric CO2 concentration around the takelamagan desert with five products retrieved from satellite observations. Beijing: International Geoscience and Remote Sensing Symposium. 4087–4089

2. Bie N, Lei L, Zeng Z C, Cai B, Yang S, He Z, Wu C, Nassar R. 2018. Regional uncertainty of GOSAT XCO2 retrievals in China: Quantification and attribution. Atmos Meas Tech, 11: 1251–1272

3. Bovensmann H, Buchwitz M, Burrows J P, Reuter M, Krings T, Gerilowski K, Schneising O, Heymann J, Tretner A, Erzinger J. 2010. A remote sensing technique for global monitoring of power plant CO2 emissions from space and related applications. Atmos Meas Tech, 3: 781–811

4. Buchwitz M, Reuter M, Schneising O, Boesch H, Guerlet S, Dils B, Aben I, Armante R, Bergamaschi P, Blumenstock T, Bovensmann H, Brunner D, Buchmann B, Burrows J P, Butz A, Chédin A, Chevallier F, Crevoisier C D, Deutscher N M, Frankenberg C, Hase F, Hasekamp O P, Heymann J, Kaminski T, Laeng A, Lichtenberg G, De Mazière M, Noël S, Notholt J, Orphal J, Popp C, Parker R, Scholze M, Sussmann R, Stiller G P, Warneke T, Zehner C, Bril A, Crisp D, Griffith D W T, Kuze A, O’Dell C, Oshchepkov S, Sherlock V, Suto H, Wennberg P, Wunch D, Yokota T, Yoshida Y. 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. Remote Sens Environ, 162: 344–362

5. Cai B, Zhang L. 2014. Urban CO2 emissions in China: Spatial boundary and performance comparison. Energy Policy, 66: 557–567

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1. Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model;Ecological Informatics;2024-11

2. Spatio-temporal variation of atmospheric CO2 and its association with anthropogenic, vegetation, and climate indices over the state of Bihar, India;Environmental Advances;2024-07

3. Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data;Science of The Total Environment;2023-10

4. Seasonal and Diurnal Variations in XCO₂ Characteristics in China as Observed by OCO‐2/3 Satellites: Effects of Land Cover and Local Meteorology;Journal of Geophysical Research: Atmospheres;2023-09-16

5. Machine Learning Model-Based Estimation of XCO2 with High Spatiotemporal Resolution in China;Atmosphere;2023-02-22

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