Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data
The increase in the carbon dioxide (CO<sub>2</sub>) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO<sub>2&l...
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MDPI AG
2025-05-01
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| author | Shanzhao Cai Heng Dong Bo Zhang Huan Huang |
| author_facet | Shanzhao Cai Heng Dong Bo Zhang Huan Huang |
| author_sort | Shanzhao Cai |
| collection | DOAJ |
| description | The increase in the carbon dioxide (CO<sub>2</sub>) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO<sub>2</sub> column concentration (XCO<sub>2</sub>), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO<sub>2</sub> data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO<sub>2</sub> concentration in China from 2010 to 2022 (ChinaXCO<sub>2</sub>-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO<sub>2</sub> data significantly improves the spatiotemporal coverage. The overall R<sup>2</sup> of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO<sub>2</sub>-MLE shows good consistency with the observational records from two background stations in China, with R<sup>2</sup> values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO<sub>2</sub> concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO<sub>2</sub> concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO<sub>2</sub> data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals. |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-9aade5625e0545d5b5ff9dfa9143ae022025-08-20T01:56:14ZengMDPI AGAtmosphere2073-44332025-05-0116562110.3390/atmos16050621Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite DataShanzhao Cai0Heng Dong1Bo Zhang2Huan Huang3School of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, ChinaZhejiang Yongqiang Group Co., Ltd., Ningbo 317000, ChinaZhejiang Key Laboratory of Ecological and Environmental Big Data, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, ChinaThe increase in the carbon dioxide (CO<sub>2</sub>) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO<sub>2</sub> column concentration (XCO<sub>2</sub>), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO<sub>2</sub> data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO<sub>2</sub> concentration in China from 2010 to 2022 (ChinaXCO<sub>2</sub>-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO<sub>2</sub> data significantly improves the spatiotemporal coverage. The overall R<sup>2</sup> of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO<sub>2</sub>-MLE shows good consistency with the observational records from two background stations in China, with R<sup>2</sup> values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO<sub>2</sub> concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO<sub>2</sub> concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO<sub>2</sub> data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals.https://www.mdpi.com/2073-4433/16/5/621carbon dioxidehigh spatiotemporal resolutionmachine learning ensemble methodremote sensing downscaling |
| spellingShingle | Shanzhao Cai Heng Dong Bo Zhang Huan Huang Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data Atmosphere carbon dioxide high spatiotemporal resolution machine learning ensemble method remote sensing downscaling |
| title | Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data |
| title_full | Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data |
| title_fullStr | Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data |
| title_full_unstemmed | Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data |
| title_short | Estimation of High Spatial Resolution CO<sub>2</sub> Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data |
| title_sort | estimation of high spatial resolution co sub 2 sub concentration in china from 2010 to 2022 based on multi source carbon satellite data |
| topic | carbon dioxide high spatiotemporal resolution machine learning ensemble method remote sensing downscaling |
| url | https://www.mdpi.com/2073-4433/16/5/621 |
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