Articles | Volume 17, issue 5
https://doi.org/10.5194/amt-17-1375-2024
© 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-17-1375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Mar 2024
Research article |
| 06 Mar 2024
| 06 Mar 2024
The importance of digital elevation model accuracy in XCO2 retrievals: improving the Orbiting Carbon Observatory 2 Atmospheric Carbon Observations from Space version 11 retrieval product
Nicole Jacobs
CORRESPONDING AUTHOR
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
Department of Physics, University of Toronto, Toronto, ON, Canada
Christopher W. O'Dell
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
Thomas E. Taylor
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
Thomas L. Logan
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Brendan Byrne
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Matthäus Kiel
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Rigel Kivi
Space and Earth Observation Centre, Finnish Meteorological Institute, Sodankylä, Finland
Pauli Heikkinen
Space and Earth Observation Centre, Finnish Meteorological Institute, Sodankylä, Finland
Aronne Merrelli
Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
Vivienne H. Payne
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Abhishek Chatterjee
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
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Cited
23 citations as recorded by crossref.
- Spatiotemporal bias in OCO-2 XCO2 retrievals relative to TCCON observations T. Nacson et al. https://doi.org/10.1016/j.jag.2026.105466
- Does the Satellite Sensing Column-Averaged Dry-Air Mole Fraction CO2 (XCO2) Require Oxygen Concentration From O2–A Band for Normalizing? L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3575603
- A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system Z. Jin et al. https://doi.org/10.5194/essd-16-2857-2024
- Research on Synchronous Inversion Algorithm Based on Tri-Wavelength DIAL Z. Xu et al. https://doi.org/10.3390/opt6040048
- Improving Forest Carbon Sink Accounting Using Integrated Satellite-Ground Observations, Machine Learning, and Ecological Process Modeling X. Hong et al. https://doi.org/10.1021/acs.est.6c03931
- Substantial Discrepancies Across Global Satellite XCO2 Products: A Systematic Evaluation J. Yang et al. https://doi.org/10.3390/rs18020371
- 基于国产碳卫星的在轨光谱性能评估及大气二氧化碳的高精度反演（特邀） 洪. Hong Xinhua et al. https://doi.org/10.3788/AOS241153
- Validation and assessment of satellite-based columnar CO2 and CH4 mixing ratios from GOSAT and OCO-2 satellites over India H. Gadhavi et al. https://doi.org/10.5194/amt-18-4497-2025
- Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
- All-Fiber Multimodal Sensor for Dual-Gas and Spectral Co-Detection Based on Hollow-Core Photonic Bandgap Fiber J. Lin et al. https://doi.org/10.1109/JSEN.2026.3675640
- Evaluating the performance of carbon dioxide and methane observations from carbon-monitoring satellite products over China X. Hong et al. https://doi.org/10.1016/j.scitotenv.2024.176896
- Seasonal and interannual variability in CO2 fluxes in southern Africa seen by GOSAT E. Metz et al. https://doi.org/10.5194/bg-22-555-2025
- Quantitative analysis of spatiotemporal coverage and uncertainty decomposition in OCO-2/3 XCO2 across China L. Zhang et al. https://doi.org/10.1016/j.atmosenv.2024.120636
- CO2 variability over a tropical coastal station in India: Synergy of observation and model K. Uma et al. https://doi.org/10.1016/j.scitotenv.2024.177371
- Fiducial Reference Measurements for Greenhouse Gases (FRM4GHG): Validation of Satellite (Sentinel-5 Precursor, OCO-2, and GOSAT) Missions Using the COllaborative Carbon Column Observing Network (COCCON) M. Sha et al. https://doi.org/10.3390/rs17050734
- Ai-driven carbon Monitoring: Transformer-Based reconstruction of atmospheric CO2 in Canadian poultry regions P. Prajesh et al. https://doi.org/10.1016/j.jag.2026.105248
- Analysis of the Current Situation of CO2 Satellite Observation Y. Li et al. https://doi.org/10.3390/rs17213635
- Synergy of Multiple‐Satellite Measurements to Fill the Gap of Global XCO2 J. Lee et al. https://doi.org/10.1029/2024JD042809
- La surveillance des émissions anthropiques de CO2 depuis l’espace : un enjeu géopolitique émergent G. Broquet & F. Chevallier https://doi.org/10.4000/12de3
- Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023 P. Ke et al. https://doi.org/10.1093/nsr/nwae367
- Satellite-Based Assessment of Spatially Heterogeneous XCO2 and Marine pCO2 Trends (2015–2020) S. Zhang et al. https://doi.org/10.3390/rs18040630
- 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
- 大气环境监测卫星二氧化碳探测激光雷达研究进展及应用展望（特邀） 卜. Bu Lingbing et al. https://doi.org/10.3788/AOS251157
23 citations as recorded by crossref.
- Spatiotemporal bias in OCO-2 XCO2 retrievals relative to TCCON observations T. Nacson et al. https://doi.org/10.1016/j.jag.2026.105466
- Does the Satellite Sensing Column-Averaged Dry-Air Mole Fraction CO2 (XCO2) Require Oxygen Concentration From O2–A Band for Normalizing? L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3575603
- A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system Z. Jin et al. https://doi.org/10.5194/essd-16-2857-2024
- Research on Synchronous Inversion Algorithm Based on Tri-Wavelength DIAL Z. Xu et al. https://doi.org/10.3390/opt6040048
- Improving Forest Carbon Sink Accounting Using Integrated Satellite-Ground Observations, Machine Learning, and Ecological Process Modeling X. Hong et al. https://doi.org/10.1021/acs.est.6c03931
- Substantial Discrepancies Across Global Satellite XCO2 Products: A Systematic Evaluation J. Yang et al. https://doi.org/10.3390/rs18020371
- 基于国产碳卫星的在轨光谱性能评估及大气二氧化碳的高精度反演（特邀） 洪. Hong Xinhua et al. https://doi.org/10.3788/AOS241153
- Validation and assessment of satellite-based columnar CO2 and CH4 mixing ratios from GOSAT and OCO-2 satellites over India H. Gadhavi et al. https://doi.org/10.5194/amt-18-4497-2025
- Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
- All-Fiber Multimodal Sensor for Dual-Gas and Spectral Co-Detection Based on Hollow-Core Photonic Bandgap Fiber J. Lin et al. https://doi.org/10.1109/JSEN.2026.3675640
- Evaluating the performance of carbon dioxide and methane observations from carbon-monitoring satellite products over China X. Hong et al. https://doi.org/10.1016/j.scitotenv.2024.176896
- Seasonal and interannual variability in CO2 fluxes in southern Africa seen by GOSAT E. Metz et al. https://doi.org/10.5194/bg-22-555-2025
- Quantitative analysis of spatiotemporal coverage and uncertainty decomposition in OCO-2/3 XCO2 across China L. Zhang et al. https://doi.org/10.1016/j.atmosenv.2024.120636
- CO2 variability over a tropical coastal station in India: Synergy of observation and model K. Uma et al. https://doi.org/10.1016/j.scitotenv.2024.177371
- Fiducial Reference Measurements for Greenhouse Gases (FRM4GHG): Validation of Satellite (Sentinel-5 Precursor, OCO-2, and GOSAT) Missions Using the COllaborative Carbon Column Observing Network (COCCON) M. Sha et al. https://doi.org/10.3390/rs17050734
- Ai-driven carbon Monitoring: Transformer-Based reconstruction of atmospheric CO2 in Canadian poultry regions P. Prajesh et al. https://doi.org/10.1016/j.jag.2026.105248
- Analysis of the Current Situation of CO2 Satellite Observation Y. Li et al. https://doi.org/10.3390/rs17213635
- Synergy of Multiple‐Satellite Measurements to Fill the Gap of Global XCO2 J. Lee et al. https://doi.org/10.1029/2024JD042809
- La surveillance des émissions anthropiques de CO2 depuis l’espace : un enjeu géopolitique émergent G. Broquet & F. Chevallier https://doi.org/10.4000/12de3
- Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023 P. Ke et al. https://doi.org/10.1093/nsr/nwae367
- Satellite-Based Assessment of Spatially Heterogeneous XCO2 and Marine pCO2 Trends (2015–2020) S. Zhang et al. https://doi.org/10.3390/rs18040630
- 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
- 大气环境监测卫星二氧化碳探测激光雷达研究进展及应用展望（特邀） 卜. Bu Lingbing et al. https://doi.org/10.3788/AOS251157
Saved (final revised paper)
Latest update: 06 Aug 2026
Short summary
The accuracy of trace gas retrievals from spaceborne observations, like those from the Orbiting Carbon Observatory 2 (OCO-2), are sensitive to the referenced digital elevation model (DEM). Therefore, we evaluate several global DEMs, used in versions 10 and 11 of the OCO-2 retrieval along with the Copernicus DEM. We explore the impacts of changing the DEM on biases in OCO-2-retrieved XCO2 and inferred CO2 fluxes. Our findings led to an update to OCO-2 v11.1 using the Copernicus DEM globally.
The accuracy of trace gas retrievals from spaceborne observations, like those from the Orbiting...
