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.
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
Viewed
Total article views: 3,646 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Mar 2023)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,705 | 831 | 110 | 3,646 | 68 | 92 |
- HTML: 2,705
- PDF: 831
- XML: 110
- Total: 3,646
- BibTeX: 68
- EndNote: 92
Total article views: 2,595 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Aug 2023)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,043 | 470 | 82 | 2,595 | 56 | 77 |
- HTML: 2,043
- PDF: 470
- XML: 82
- Total: 2,595
- BibTeX: 56
- EndNote: 77
Total article views: 1,051 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Mar 2023)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
662 | 361 | 28 | 1,051 | 88 | 12 | 15 |
- HTML: 662
- PDF: 361
- XML: 28
- Total: 1,051
- Supplement: 88
- BibTeX: 12
- EndNote: 15
Viewed (geographical distribution)
Total article views: 3,646 (including HTML, PDF, and XML)
Thereof 3,534 with geography defined
and 112 with unknown origin.
Total article views: 2,595 (including HTML, PDF, and XML)
Thereof 2,497 with geography defined
and 98 with unknown origin.
Total article views: 1,051 (including HTML, PDF, and XML)
Thereof 1,037 with geography defined
and 14 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
13 citations as recorded by crossref.
- Full-coverage estimation of CO2 concentrations in China via multisource satellite data and Deep Forest model K. Cai et al. 10.1038/s41597-024-04063-9
- Local-Global Temporal Difference Learning for Satellite Video Super-Resolution Y. Xiao et al. 10.1109/TCSVT.2023.3312321
- Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model L. Cui et al. 10.1016/j.atmosres.2024.107542
- Integrated Analysis of Solar-Induced Chlorophyll Fluorescence, Normalized Difference Vegetation Index, and Column-Average CO2 Concentration in South-Central Brazilian Sugarcane Regions K. de Meneses et al. 10.3390/agronomy14102345
- NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection K. Dashdondov et al. 10.1109/ACCESS.2023.3340240
- Reconstructing global daily XCO2 at 1° × 1° spatial resolution from 2016 to 2019 with multisource satellite observation data Y. Huang et al. 10.1117/1.JRS.18.028502
- Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model C. Wu et al. 10.1016/j.scitotenv.2024.176171
- Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model W. Liu et al. 10.1016/j.ecoinf.2024.102806
- Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas L. Fan et al. 10.3390/app142311100
- Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia M. Khaliq et al. 10.1016/j.scitotenv.2024.171311
- Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020 J. He et al. 10.1016/j.asr.2024.07.007
- Using Multisource Data and Time Series Features to Construct a Global Terrestrial CO₂ Coverage by Deep Learning W. Tian et al. 10.1109/TGRS.2024.3462589
- Seasonal and Diurnal Variations in XCO2 Characteristics in China as Observed by OCO‐2/3 Satellites: Effects of Land Cover and Local Meteorology H. Zhao et al. 10.1029/2023JD038841
12 citations as recorded by crossref.
- Full-coverage estimation of CO2 concentrations in China via multisource satellite data and Deep Forest model K. Cai et al. 10.1038/s41597-024-04063-9
- Local-Global Temporal Difference Learning for Satellite Video Super-Resolution Y. Xiao et al. 10.1109/TCSVT.2023.3312321
- Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model L. Cui et al. 10.1016/j.atmosres.2024.107542
- Integrated Analysis of Solar-Induced Chlorophyll Fluorescence, Normalized Difference Vegetation Index, and Column-Average CO2 Concentration in South-Central Brazilian Sugarcane Regions K. de Meneses et al. 10.3390/agronomy14102345
- NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection K. Dashdondov et al. 10.1109/ACCESS.2023.3340240
- Reconstructing global daily XCO2 at 1° × 1° spatial resolution from 2016 to 2019 with multisource satellite observation data Y. Huang et al. 10.1117/1.JRS.18.028502
- Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model C. Wu et al. 10.1016/j.scitotenv.2024.176171
- Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model W. Liu et al. 10.1016/j.ecoinf.2024.102806
- Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas L. Fan et al. 10.3390/app142311100
- Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia M. Khaliq et al. 10.1016/j.scitotenv.2024.171311
- Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020 J. He et al. 10.1016/j.asr.2024.07.007
- Using Multisource Data and Time Series Features to Construct a Global Terrestrial CO₂ Coverage by Deep Learning W. Tian et al. 10.1109/TGRS.2024.3462589
Latest update: 06 Dec 2024
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...
Altmetrics
Final-revised paper
Preprint