Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3597-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-3597-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Aug 2023
Data description paper |
| 10 Aug 2023
| 10 Aug 2023
Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method
Yuan Wang
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
Tongwen Li
School of Geospatial Engineering and Science, Sun Yat-sen University,
Guangzhou, Guangdong, 519082, China
Yuanjian Yang
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China
Siqin Zhou
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
Liangpei Zhang
The State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430079, China
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Qiang Zhang, Qiangqiang Yuan, Jie Li, Yuan Wang, Fujun Sun, and Liangpei Zhang
Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, https://doi.org/10.5194/essd-13-1385-2021, 2021
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Acquired daily soil moisture products are always incomplete globally (just about 30 %–80 % coverage ratio) due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we generate long-term seamless global daily (SGD) AMSR2 soil moisture productions from 2013 to 2019. These productions are significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Siyu Tan, and Liangpei Zhang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1004, https://doi.org/10.5194/acp-2020-1004, 2020
Revised manuscript not accepted
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Estimating ambient PM2.5 and PM10 considering their precursors and chemical compositions instead of AOD products; Both remote sensing (Sentinel-5P) and assimilated data (GEOS-FP) are adopted; Sample-based Cross-Validation R2s and RMSEs are 0.93 (0.9) and 8.982 (17.604) μg/m3 for PM2.5 (PM10), respectively; Achieving better performance compared to the baseline (AOD-based) in different cases (e.g., overall and seasonal).
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Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily...
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