XCO2 distribution in China based on multi-source satellite data fusion Article Swipe
Xiaolin Wang
,
Jingjing Ai
,
Peng Gao
,
Yujie Cui
,
Mingkun Wang
,
Zhen Huang
,
Hongxia Wang
,
Tingting Fan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7072225/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7072225/v1
The spatiotemporal distribution of the carbon dioxide column concentration (XCO 2 ) in China is generated by utilizing the high accuracy surface modeling (HASM) data fusion method, where the simulation output of the global earth observing system chemistry model (GEOS-Chem) is used as the driving field while the observation data from the greenhouse gases observing satellite (GOSAT) and orbiting carbon observatory-2 (OCO-2) as the optimal control constraints. The simulation results of the GEOS-Chem model with its grid resolutions of 2°×2.5° and 0.25°×0.3125° are compared with the total carbon column observing network (TCCON) station data, and the mean error (ME) together with the root mean square error (RMSE) at Hefei station decrease by 0.7248 ppm and 0.6901 ppm, respectively, while the ME and RMSE decreasing by 0.0371 ppm and 0.2426 ppm at Xianghe station. Besides, the sensitivity of the sampling weight \(\lambda\) of observation data and the sampling radius to the HASM data fusion accuracy is analyzed, and the optimal values \(\lambda =0.6\) and \(r=3\) corresponding to the highest fusion accuracy are obtained through the cross validation. The validation results show that the MAE and RMSE decrease from 1.6282 ppm and 2.3808 ppm to 1.1890 ppm and 1.9377 ppm before and after the HASM data fusion, respectively. The spatial distribution of the monthly XCO 2 after the HASM data fusion is similar to the simulation result from 2017 to 2018, indicating that the HASM data fusion method not only simulates the XCO 2 with high precision, but also ameliorates effectively the spatial distribution of the XCO 2 .
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- article
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- https://doi.org/10.21203/rs.3.rs-7072225/v1
- https://www.researchsquare.com/article/rs-7072225/latest.pdf
- OA Status
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- 16
- OpenAlex ID
- https://openalex.org/W4415928292
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https://openalex.org/W4415928292Canonical identifier for this work in OpenAlex
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https://doi.org/10.21203/rs.3.rs-7072225/v1Digital Object Identifier
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XCO2 distribution in China based on multi-source satellite data fusionWork title
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articleOpenAlex work type
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2025Year of publication
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2025-11-05Full publication date if available
- Authors
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Xiaolin Wang, Jingjing Ai, Peng Gao, Yujie Cui, Mingkun Wang, Zhen Huang, Hongxia Wang, Tingting FanList of authors in order
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https://doi.org/10.21203/rs.3.rs-7072225/v1Publisher landing page
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https://www.researchsquare.com/article/rs-7072225/latest.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-7072225/latest.pdfDirect OA link when available
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0Total citation count in OpenAlex
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16Number of works referenced by this work
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