Articles | Volume 17, issue 13
https://doi.org/10.5194/amt-17-3949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jul 2024
Research article |
| 03 Jul 2024
| 03 Jul 2024
Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements
Fengxin Xie
CORRESPONDING AUTHOR
China–UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, China
currently at: Atmosphere and Ocean Research Institute, the University of Tokyo, Chiba, Japan
Tao Ren
CORRESPONDING AUTHOR
China–UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, China
Changying Zhao
China–UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, China
Shanghai Institute of Satellite Engineering, Shanghai, China
Yilei Gu
Shanghai Institute of Satellite Engineering, Shanghai, China
Minqiang Zhou
Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Pucai Wang
Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Kei Shiomi
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan
Isamu Morino
Satellite Remote Sensing Section and Satellite Observation Center, Earth System Division, National Institute for Environmental Studies, Tsukuba, Japan
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Cited
13 citations as recorded by crossref.
- Atmospheric Remote Sensing Based on Satellite Oxygen-Band Observations: A Review X. Wu et al. https://doi.org/10.3390/rs18162808
- XCO2 Data Full-Coverage Mapping in China Based on Random Forest Models R. Chen et al. https://doi.org/10.3390/rs17010048
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Spatiotemporal Characteristic of XCO2 and Its Changing Contribution Rate from Different Influencing Indicators in Mongolian Plateau of Central Asia Y. A et al. https://doi.org/10.3390/atmos16050560
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
- From Deterministic to Probabilistic: A Lightweight Framework for Probabilistic Machine Learning in Trace Gas Remote Sensing W. Chen et al. https://doi.org/10.34133/remotesensing.0881
- Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. https://doi.org/10.5194/amt-18-241-2025
- Implementation of Near-Real-Time Satellite Data Retrieval for CO₂ Concentration Using an Enhanced Transformer Network J. Yu et al. https://doi.org/10.1109/ACCESS.2026.3659201
- Transformer-Based Fast Mole Fraction of CO 2 Retrievals from Satellite-Measured Spectra W. Chen et al. https://doi.org/10.34133/remotesensing.0470
- A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2 L. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2026.01.039
- Deep Neural Emulator of Radiative Transfer Model for Near-Infrared Hyperspectral Simulation: Implications for Fast Satellite Retrieval of XCO2 R. Hu et al. https://doi.org/10.1109/TGRS.2025.3644156
- Research on a fast XCO2 retrieval method based on one-dimensional convolutional neural networks Y. Jiang et al. https://doi.org/10.1016/j.jqsrt.2026.110074
- Random Forest-Based Retrieval of XCO2 Concentration from Satellite-Borne Shortwave Infrared Hyperspectral W. Zhang et al. https://doi.org/10.3390/atmos16030238
13 citations as recorded by crossref.
- Atmospheric Remote Sensing Based on Satellite Oxygen-Band Observations: A Review X. Wu et al. https://doi.org/10.3390/rs18162808
- XCO2 Data Full-Coverage Mapping in China Based on Random Forest Models R. Chen et al. https://doi.org/10.3390/rs17010048
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Spatiotemporal Characteristic of XCO2 and Its Changing Contribution Rate from Different Influencing Indicators in Mongolian Plateau of Central Asia Y. A et al. https://doi.org/10.3390/atmos16050560
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
- From Deterministic to Probabilistic: A Lightweight Framework for Probabilistic Machine Learning in Trace Gas Remote Sensing W. Chen et al. https://doi.org/10.34133/remotesensing.0881
- Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. https://doi.org/10.5194/amt-18-241-2025
- Implementation of Near-Real-Time Satellite Data Retrieval for CO₂ Concentration Using an Enhanced Transformer Network J. Yu et al. https://doi.org/10.1109/ACCESS.2026.3659201
- Transformer-Based Fast Mole Fraction of CO 2 Retrievals from Satellite-Measured Spectra W. Chen et al. https://doi.org/10.34133/remotesensing.0470
- A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2 L. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2026.01.039
- Deep Neural Emulator of Radiative Transfer Model for Near-Infrared Hyperspectral Simulation: Implications for Fast Satellite Retrieval of XCO2 R. Hu et al. https://doi.org/10.1109/TGRS.2025.3644156
- Research on a fast XCO2 retrieval method based on one-dimensional convolutional neural networks Y. Jiang et al. https://doi.org/10.1016/j.jqsrt.2026.110074
- Random Forest-Based Retrieval of XCO2 Concentration from Satellite-Borne Shortwave Infrared Hyperspectral W. Zhang et al. https://doi.org/10.3390/atmos16030238
Saved (final revised paper)
Latest update: 02 Sep 2026
Short summary
This study demonstrates a new machine learning approach to efficiently and accurately estimate atmospheric carbon dioxide levels from satellite data. Rather than using traditional complex physics-based retrieval methods, neural network models are trained on simulated data to rapidly predict CO2 concentrations directly from satellite spectral measurements.
This study demonstrates a new machine learning approach to efficiently and accurately estimate...
