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 article
| 
10 Aug 2023
Data description article |
| 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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- SERF-XCH4: A Stacked Ensemble Framework for Spatiotemporal Continuous Methane Monitoring and Driver Analysis H. Zhao et al. https://doi.org/10.3390/rs18071036
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- Comparison of model-derived carbon dioxide datasets with the Orbiting Carbon Observatory 3 (OCO-3) observations F. Mustafa & M. Xu https://doi.org/10.1016/j.atmosres.2025.108057
- Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model W. Liu et al. https://doi.org/10.1016/j.ecoinf.2024.102806
- Global monthly 0.1° land XCO2 data (2017–2024) estimated via multi-source fusion and spatiotemporal pattern integration H. Yu et al. https://doi.org/10.1088/2752-5295/ae6887
- A High Resolution Spatially Consistent Global Dataset for CO2 Monitoring A. Rakotoharisoa et al. https://doi.org/10.3390/rs17091617
- Estimating carbon emissions from thermal power plants using multimodal data fusion J. Bu et al. https://doi.org/10.1016/j.geoen.2025.213969
- Generating seamless hourly XCO2 maps over Asia: A CatBoost modeling approach integrating FY-4A GIIRS and OCO-2/3 (2021-2022) S. Borjigen et al. https://doi.org/10.1016/j.jclepro.2026.148493
- Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia M. Khaliq et al. https://doi.org/10.1016/j.scitotenv.2024.171311
- XCO2 over East Asia: Assessing consistency, growth, and seasonality from multiple satellites and models Y. Yang et al. https://doi.org/10.1016/j.atmosres.2026.108974
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- Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020 J. He et al. https://doi.org/10.1016/j.asr.2024.07.007
- Toward net-zero in space exploration: A review of technological and policy pathways for sustainable space activities D. Olawade et al. https://doi.org/10.1016/j.scitotenv.2025.179145
- Synergy of Multiple‐Satellite Measurements to Fill the Gap of Global XCO2 J. Lee et al. https://doi.org/10.1029/2024JD042809
- Space-ground integration system of methane emission monitoring and quantification: cases in Dongying, China H. He et al. https://doi.org/10.3389/feart.2025.1577961
- Local-Global Temporal Difference Learning for Satellite Video Super-Resolution Y. Xiao et al. https://doi.org/10.1109/TCSVT.2023.3312321
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- Reconstructing global daily XCO2 at 1° × 1° spatial resolution from 2016 to 2019 with multisource satellite observation data Y. Huang et al. https://doi.org/10.1117/1.JRS.18.028502
- Global Estimates of Daily Gapless Atmospheric XCH4 Concentrations From Satellite and Reanalysis Data During 2003–2020 Y. Qu et al. https://doi.org/10.1109/TGRS.2025.3593486
- Full-Coverage Mapping of Daily High-Resolution XCO2 Across China From 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion Y. Li et al. https://doi.org/10.1109/TGRS.2025.3540289
- Global Daily Column Average CO2 at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning F. Antezana Lopez et al. https://doi.org/10.1038/s41597-024-04135-w
- Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas L. Fan et al. https://doi.org/10.3390/app142311100
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- Substantial Discrepancies Across Global Satellite XCO2 Products: A Systematic Evaluation J. Yang et al. https://doi.org/10.3390/rs18020371
- Interannual Divergence of PM2.5 and O3 Pollution in East-Central China Revealed by Seamless 24-h Retrievals (2017–2023) Y. Su et al. https://doi.org/10.1109/TGRS.2026.3665614
- Estimation of High Spatial Resolution CO2 Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data S. Cai et al. https://doi.org/10.3390/atmos16050621
- Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. https://doi.org/10.1016/j.jenvman.2025.126309
- A full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021 for exploring global carbon dynamics Z. Wang et al. https://doi.org/10.5194/essd-17-5355-2025
- Reconstructing long-term (2003–2019) global high-resolution XCO 2 : bridging observational gaps with machine learning S. Hwang et al. https://doi.org/10.1080/15481603.2026.2627042
- Using Multisource Data and Time Series Features to Construct a Global Terrestrial CO₂ Coverage by Deep Learning W. Tian et al. https://doi.org/10.1109/TGRS.2024.3462589
- Comprehensive diurnal and nocturnal surface NO2 concentration retrieval with seamless temporal and spatial coverage Y. Zhang et al. https://doi.org/10.1016/j.atmosenv.2025.121764
- Improving Model-Based Carbon Dioxide Datasets Using Deep Learning and Satellite Observations F. Mustafa & M. Xu https://doi.org/10.1109/TGRS.2025.3556309
46 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. https://doi.org/10.1038/s41597-024-04063-9
- Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model L. Cui et al. https://doi.org/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. https://doi.org/10.3390/agronomy14102345
- How to Develop Comprehensive Explanatory and Confirmatory Techniques for the Characteristics of Global Climate Data? Insights From Six Stations of Humidity Data for the Period 1981 to 2022 Z. Al-Hemyari et al. https://doi.org/10.1177/21582440251368939
- NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection K. Dashdondov et al. https://doi.org/10.1109/ACCESS.2023.3340240
- Global daily 1 km gapless XCO₂ (2003−2023) derived from multi-satellite observations and a spatiotemporal deep learning framework J. Wang https://doi.org/10.1016/j.eiar.2025.108146
- A Multigranularity Spatiotemporal Attention Model Based on Multisource Satellite Data for Monthly XCO2 Reconstruction Over China R. Chen et al. https://doi.org/10.1109/TGRS.2025.3609734
- SERF-XCH4: A Stacked Ensemble Framework for Spatiotemporal Continuous Methane Monitoring and Driver Analysis H. Zhao et al. https://doi.org/10.3390/rs18071036
- Characteristics of atmospheric CH4, CO2 and CO at a suburban coastal site in Hong Kong W. Chan et al. https://doi.org/10.1016/j.atmosenv.2025.121630
- Seamless Global Mapping of HCHO from Chinese Satellite via Spectral Retrieval and Neural Operator J. Xue et al. https://doi.org/10.34133/remotesensing.1043
- A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 D. Hu et al. https://doi.org/10.5194/essd-17-2849-2025
- Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model C. Wu et al. https://doi.org/10.1016/j.scitotenv.2024.176171
- Comparison of model-derived carbon dioxide datasets with the Orbiting Carbon Observatory 3 (OCO-3) observations F. Mustafa & M. Xu https://doi.org/10.1016/j.atmosres.2025.108057
- Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model W. Liu et al. https://doi.org/10.1016/j.ecoinf.2024.102806
- Global monthly 0.1° land XCO2 data (2017–2024) estimated via multi-source fusion and spatiotemporal pattern integration H. Yu et al. https://doi.org/10.1088/2752-5295/ae6887
- A High Resolution Spatially Consistent Global Dataset for CO2 Monitoring A. Rakotoharisoa et al. https://doi.org/10.3390/rs17091617
- Estimating carbon emissions from thermal power plants using multimodal data fusion J. Bu et al. https://doi.org/10.1016/j.geoen.2025.213969
- Generating seamless hourly XCO2 maps over Asia: A CatBoost modeling approach integrating FY-4A GIIRS and OCO-2/3 (2021-2022) S. Borjigen et al. https://doi.org/10.1016/j.jclepro.2026.148493
- Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia M. Khaliq et al. https://doi.org/10.1016/j.scitotenv.2024.171311
- XCO2 over East Asia: Assessing consistency, growth, and seasonality from multiple satellites and models Y. Yang et al. https://doi.org/10.1016/j.atmosres.2026.108974
- Quantifying key drivers of atmospheric methane across Pakistan using a machine learning approach F. Altaf et al. https://doi.org/10.1007/s10661-025-14952-0
- Estimating global anthropogenic carbon dioxide emissions using satellite observations and machine learning methods F. Mustafa & M. Xu https://doi.org/10.1016/j.atmosenv.2025.121423
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- Space-ground integration system of methane emission monitoring and quantification: cases in Dongying, China H. He et al. https://doi.org/10.3389/feart.2025.1577961
- Local-Global Temporal Difference Learning for Satellite Video Super-Resolution Y. Xiao et al. https://doi.org/10.1109/TCSVT.2023.3312321
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- Reconstructing global daily XCO2 at 1° × 1° spatial resolution from 2016 to 2019 with multisource satellite observation data Y. Huang et al. https://doi.org/10.1117/1.JRS.18.028502
- Global Estimates of Daily Gapless Atmospheric XCH4 Concentrations From Satellite and Reanalysis Data During 2003–2020 Y. Qu et al. https://doi.org/10.1109/TGRS.2025.3593486
- Full-Coverage Mapping of Daily High-Resolution XCO2 Across China From 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion Y. Li et al. https://doi.org/10.1109/TGRS.2025.3540289
- Global Daily Column Average CO2 at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning F. Antezana Lopez et al. https://doi.org/10.1038/s41597-024-04135-w
- Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas L. Fan et al. https://doi.org/10.3390/app142311100
- Deep Learning Insights into Seamless Reconstruction of XCO2 in China: Spatiotemporal Patterns and Driving Mechanisms W. Wang et al. https://doi.org/10.3390/rs18091366
- Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al. https://doi.org/10.5194/acp-26-4453-2026
- A Multifactor Deep Forest Regression Stepwise Downscaling Framework for High-Resolution XCO2 in China Y. Huang et al. https://doi.org/10.1109/TGRS.2025.3633903
- Substantial Discrepancies Across Global Satellite XCO2 Products: A Systematic Evaluation J. Yang et al. https://doi.org/10.3390/rs18020371
- Interannual Divergence of PM2.5 and O3 Pollution in East-Central China Revealed by Seamless 24-h Retrievals (2017–2023) Y. Su et al. https://doi.org/10.1109/TGRS.2026.3665614
- Estimation of High Spatial Resolution CO2 Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data S. Cai et al. https://doi.org/10.3390/atmos16050621
- Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. https://doi.org/10.1016/j.jenvman.2025.126309
- A full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021 for exploring global carbon dynamics Z. Wang et al. https://doi.org/10.5194/essd-17-5355-2025
- Reconstructing long-term (2003–2019) global high-resolution XCO 2 : bridging observational gaps with machine learning S. Hwang et al. https://doi.org/10.1080/15481603.2026.2627042
- Using Multisource Data and Time Series Features to Construct a Global Terrestrial CO₂ Coverage by Deep Learning W. Tian et al. https://doi.org/10.1109/TGRS.2024.3462589
- Comprehensive diurnal and nocturnal surface NO2 concentration retrieval with seamless temporal and spatial coverage Y. Zhang et al. https://doi.org/10.1016/j.atmosenv.2025.121764
- Improving Model-Based Carbon Dioxide Datasets Using Deep Learning and Satellite Observations F. Mustafa & M. Xu https://doi.org/10.1109/TGRS.2025.3556309
Saved (final revised paper)
Latest update: 09 Jun 2026
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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