Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-117-2021
© Author(s) 2021. 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-14-117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2021
Research article |
| 07 Jan 2021
| 07 Jan 2021
XCO2 estimates from the OCO-2 measurements using a neural network approach
Leslie David
Laboratoire des Sciences du Climat et de l'Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
Laboratoire des Sciences du Climat et de l'Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
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Cited
17 citations as recorded by crossref.
- A high-precision retrieval method for methane vertical profiles based on dual-band spectral data from the GOSAT satellite L. Li et al. 10.1016/j.atmosenv.2023.120183
- Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. 10.5194/amt-18-241-2025
- Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation O. Lamminpää et al. 10.5194/amt-18-673-2025
- On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements F. Bréon et al. 10.5194/amt-15-5219-2022
- Global Evaluation and Intercomparison of XCO2 Retrievals from GOSAT, OCO-2, and TANSAT with TCCON J. Fang et al. 10.3390/rs15205073
- XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker A. Liang et al. 10.3390/atmos14091335
- Atmospheric CO2retrieval from satellite spectral measurements by a two-step machine learning approach Z. Zhao et al. 10.1016/j.jqsrt.2021.108006
- An improved band design framework for atmospheric pollutant detection and its application to the design of satellites for CO2 observation Z. Wu et al. 10.1016/j.jqsrt.2023.108712
- Evaluating national and subnational CO2 mitigation goals in China’s thirteenth five-year plan from satellite observations G. Pan et al. 10.1016/j.envint.2021.106771
- Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning X. Gong et al. 10.3390/atmos15010118
- Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model L. Cui et al. 10.1016/j.atmosres.2024.107542
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
- Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 M. Zhang & G. Liu 10.1016/j.scitotenv.2022.159588
- 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
- Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements F. Xie et al. 10.5194/amt-17-3949-2024
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Machine learning-based aerosol characterization using OCO-2 O2 A-band observations S. Chen et al. 10.1016/j.jqsrt.2021.108049
17 citations as recorded by crossref.
- A high-precision retrieval method for methane vertical profiles based on dual-band spectral data from the GOSAT satellite L. Li et al. 10.1016/j.atmosenv.2023.120183
- Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. 10.5194/amt-18-241-2025
- Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation O. Lamminpää et al. 10.5194/amt-18-673-2025
- On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements F. Bréon et al. 10.5194/amt-15-5219-2022
- Global Evaluation and Intercomparison of XCO2 Retrievals from GOSAT, OCO-2, and TANSAT with TCCON J. Fang et al. 10.3390/rs15205073
- XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker A. Liang et al. 10.3390/atmos14091335
- Atmospheric CO2retrieval from satellite spectral measurements by a two-step machine learning approach Z. Zhao et al. 10.1016/j.jqsrt.2021.108006
- An improved band design framework for atmospheric pollutant detection and its application to the design of satellites for CO2 observation Z. Wu et al. 10.1016/j.jqsrt.2023.108712
- Evaluating national and subnational CO2 mitigation goals in China’s thirteenth five-year plan from satellite observations G. Pan et al. 10.1016/j.envint.2021.106771
- Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning X. Gong et al. 10.3390/atmos15010118
- Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model L. Cui et al. 10.1016/j.atmosres.2024.107542
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
- Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 M. Zhang & G. Liu 10.1016/j.scitotenv.2022.159588
- 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
- Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements F. Xie et al. 10.5194/amt-17-3949-2024
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Machine learning-based aerosol characterization using OCO-2 O2 A-band observations S. Chen et al. 10.1016/j.jqsrt.2021.108049
Latest update: 22 Feb 2025
Short summary
This paper shows that a neural network (NN) approach can be used to process spaceborne observations from the OCO-2 satellite and retrieve both surface pressure and atmospheric CO2 content. The accuracy evaluation indicates that the retrievals have an accuracy that is at least as good as those of the operational approach, which relies on complex algorithms and is computer intensive. The NN approach is therefore a promising alternative for the processing of CO2-monitoring missions.
This paper shows that a neural network (NN) approach can be used to process spaceborne...
