Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5219-2022
© Author(s) 2022. 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-15-5219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Sep 2022
Research article |
| 15 Sep 2022
| 15 Sep 2022
On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements
Laboratoire des Sciences du Climat et de l'Environnement/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Leslie David
Laboratoire des Sciences du Climat et de l'Environnement/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Pierre Chatelanaz
Laboratoire des Sciences du Climat et de l'Environnement/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement/IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
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Cited
16 citations as recorded by crossref.
- Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements F. Xie et al. https://doi.org/10.5194/amt-17-3949-2024
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. https://doi.org/10.3390/rs16183394
- Dynamic estimation of a priori CO2 vertical profiles from satellite SWIR spectra for improved XCO2 retrieval S. Xu et al. https://doi.org/10.1016/j.atmosenv.2026.122177
- 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
- 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
- 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
- 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
- Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation O. Lamminpää et al. https://doi.org/10.5194/amt-18-673-2025
- Machine-learning reconstruction of high-resolution XCO2 over Thailand (2015–2024) using integrated satellite and environmental data U. Nacksriphod et al. https://doi.org/10.1016/j.atmosenv.2026.122214
- Global XCH4 retrievals from TROPOMI shortwave-infrared radiances through cross-satellite transfer learning Y. Luo et al. https://doi.org/10.1016/j.jqsrt.2026.110158
- 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
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
- A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data K. Hu et al. https://doi.org/10.1109/JSTARS.2024.3355549
- 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
- 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
16 citations as recorded by crossref.
- Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements F. Xie et al. https://doi.org/10.5194/amt-17-3949-2024
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. https://doi.org/10.3390/rs16183394
- Dynamic estimation of a priori CO2 vertical profiles from satellite SWIR spectra for improved XCO2 retrieval S. Xu et al. https://doi.org/10.1016/j.atmosenv.2026.122177
- 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
- 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
- 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
- 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
- Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation O. Lamminpää et al. https://doi.org/10.5194/amt-18-673-2025
- Machine-learning reconstruction of high-resolution XCO2 over Thailand (2015–2024) using integrated satellite and environmental data U. Nacksriphod et al. https://doi.org/10.1016/j.atmosenv.2026.122214
- Global XCH4 retrievals from TROPOMI shortwave-infrared radiances through cross-satellite transfer learning Y. Luo et al. https://doi.org/10.1016/j.jqsrt.2026.110158
- 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
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
- A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data K. Hu et al. https://doi.org/10.1109/JSTARS.2024.3355549
- 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
- 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
Saved (final revised paper)
Latest update: 27 Oct 2026
Short summary
The estimate of atmospheric CO2 from space measurement is difficult. Current methods are based on a detailed description of the atmospheric radiative transfer. These are affected by significant biases and errors and are very computer intensive. Instead we have proposed using a neural network approach. A first attempt led to confusing results. Here we provide an interpretation for these results and describe a new version that leads to high-quality estimates.
The estimate of atmospheric CO2 from space measurement is difficult. Current methods are based...
