Articles | Volume 23, issue 2
https://doi.org/10.5194/acp-23-1545-2023
© 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/acp-23-1545-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2023
Research article |
| 26 Jan 2023
| 26 Jan 2023
Using Orbiting Carbon Observatory-2 (OCO-2) column CO2 retrievals to rapidly detect and estimate biospheric surface carbon flux anomalies
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, USA
NASA Postdoctoral Program, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, USA
Zhen Zhang
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Yasuko Yoshida
Science Systems and Applications, Inc. (SSAI), Lanham, MD 20706, USA
Abhishek Chatterjee
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Benjamin Poulter
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, USA
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Cited
18 citations as recorded by crossref.
- Spectroscopic techniques conceptualized with the remote sensing of atmospheric carbon dioxide and other greenhouse gases B. Raychaudhuri https://doi.org/10.1080/05704928.2024.2326527
- Estimates of Carbon Dioxide Flux into the Forest Ecosystem Based on Results of Ground-Based Hyperspectral Sounding of the Atmosphere and an Artificial Neural Network Model A. Rozanov et al. https://doi.org/10.1134/S1024856024700246
- Deep Learning Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes E. Sun et al. https://doi.org/10.3390/rs17071167
- Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
- Characteristic Analysis of Carbon Flux in Paddy Fields in Panjin Wetland X. Zou et al. https://doi.org/10.15244/pjoes/199613
- 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
- Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. https://doi.org/10.1021/acs.est.4c07415
- Spatio-temporal variation of atmospheric CO2 and its association with anthropogenic, vegetation, and climate indices over the state of Bihar, India A. Dass et al. https://doi.org/10.1016/j.envadv.2024.100513
- Enhancing carbon emission reduction strategies using OCO and ICOS data O. Åström et al. https://doi.org/10.1038/s41598-025-22022-1
- Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai Y. Wu et al. https://doi.org/10.3390/atmos15030246
- Spatio-temporal modeling of satellite-observed CO2 columns in China using deep learning Z. He et al. https://doi.org/10.1016/j.jag.2024.103859
- 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
- A multi‐satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave A. Feldman et al. https://doi.org/10.1111/gcb.16725
- Impact of net-zero emissions on atmospheric CO2 concentration in China: Ideal simulations based on the GEOS-Chem model J. Tan et al. https://doi.org/10.1007/s11430-024-1502-y
- 净零排放对中国大气二氧化碳浓度的影响——基于GEOS-Chem模型的理想模拟 景. 谭 et al. https://doi.org/10.1360/N072024-0163
- Atmospheric carbon dioxide over the Arabian Peninsula: Variability and associated sources H. Gandham et al. https://doi.org/10.1016/j.atmosenv.2025.121316
- Diurnal and seasonal dynamics of regional CO2 drawdown at Harvard Forest: Integrating remote sensing and modeling perspectives Y. Li et al. https://doi.org/10.1016/j.scitotenv.2025.179580
- The Arctic vegetation is more sensitive to heatwave-induced photosynthetic decline than other climate zones in Europe (2009–2017) Y. Hwang et al. https://doi.org/10.1038/s41598-026-41640-x
18 citations as recorded by crossref.
- Spectroscopic techniques conceptualized with the remote sensing of atmospheric carbon dioxide and other greenhouse gases B. Raychaudhuri https://doi.org/10.1080/05704928.2024.2326527
- Estimates of Carbon Dioxide Flux into the Forest Ecosystem Based on Results of Ground-Based Hyperspectral Sounding of the Atmosphere and an Artificial Neural Network Model A. Rozanov et al. https://doi.org/10.1134/S1024856024700246
- Deep Learning Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes E. Sun et al. https://doi.org/10.3390/rs17071167
- Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
- Characteristic Analysis of Carbon Flux in Paddy Fields in Panjin Wetland X. Zou et al. https://doi.org/10.15244/pjoes/199613
- 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
- Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. https://doi.org/10.1021/acs.est.4c07415
- Spatio-temporal variation of atmospheric CO2 and its association with anthropogenic, vegetation, and climate indices over the state of Bihar, India A. Dass et al. https://doi.org/10.1016/j.envadv.2024.100513
- Enhancing carbon emission reduction strategies using OCO and ICOS data O. Åström et al. https://doi.org/10.1038/s41598-025-22022-1
- Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai Y. Wu et al. https://doi.org/10.3390/atmos15030246
- Spatio-temporal modeling of satellite-observed CO2 columns in China using deep learning Z. He et al. https://doi.org/10.1016/j.jag.2024.103859
- 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
- A multi‐satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave A. Feldman et al. https://doi.org/10.1111/gcb.16725
- Impact of net-zero emissions on atmospheric CO2 concentration in China: Ideal simulations based on the GEOS-Chem model J. Tan et al. https://doi.org/10.1007/s11430-024-1502-y
- 净零排放对中国大气二氧化碳浓度的影响——基于GEOS-Chem模型的理想模拟 景. 谭 et al. https://doi.org/10.1360/N072024-0163
- Atmospheric carbon dioxide over the Arabian Peninsula: Variability and associated sources H. Gandham et al. https://doi.org/10.1016/j.atmosenv.2025.121316
- Diurnal and seasonal dynamics of regional CO2 drawdown at Harvard Forest: Integrating remote sensing and modeling perspectives Y. Li et al. https://doi.org/10.1016/j.scitotenv.2025.179580
- The Arctic vegetation is more sensitive to heatwave-induced photosynthetic decline than other climate zones in Europe (2009–2017) Y. Hwang et al. https://doi.org/10.1038/s41598-026-41640-x
Saved (final revised paper)
Latest update: 01 Jun 2026
Short summary
We investigate the conditions under which satellite-retrieved column carbon dioxide concentrations directly hold information about surface carbon dioxide fluxes, without the use of inversion models. We show that OCO-2 column carbon dioxide retrievals, available at 1–3 month latency, can be used to directly detect and roughly estimate extreme biospheric CO2 fluxes. As such, these OCO-2 retrievals have value for rapidly monitoring extreme conditions in the terrestrial biosphere.
We investigate the conditions under which satellite-retrieved column carbon dioxide...
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