Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3633-2021
© 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/gmd-14-3633-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jun 2021
Model description paper |
| 17 Jun 2021
| 17 Jun 2021
A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
now at: Division of Geological and Planetary Sciences, California
Institute of Technology, Pasadena, CA, USA
John C. Lin
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
Henrique F. Duarte
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
now at: Earth System Science Center, National Institute for
Space Research, São José dos Campos, Brazil
Vineet Yadav
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Nicholas C. Parazoo
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Tomohiro Oda
Goddard Earth Sciences Technology and Research, Universities Space
Research Association, Columbia, MD, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD, USA
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD, USA
Eric A. Kort
Climate and Space Sciences and Engineering, University of Michigan,
Ann Arbor, MI, USA
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- Constraining Sector‐Specific CO2 Fluxes Using Space‐Based XCO2 Observations Over the Los Angeles Basin D. Roten et al. 10.1029/2023GL104376
- Machine learning for predicting urban greenhouse gas emissions: A systematic literature review Y. Jin & A. Sharifi 10.1016/j.rser.2025.115625
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27 citations as recorded by crossref.
- Research on Carbon Sequestration Capacity of Urban Park Green Space in the Central Urban Area of Beijing and Driving Factors Thereof X. LIU et al. 10.3724/j.fjyl.202408270485
- Evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence R. Cheng et al. 10.1088/1748-9326/ac9dae
- Efficacy of the CO Tracer Technique in Partitioning Biogenic and Anthropogenic Atmospheric CO2 Signals in the Humid Subtropical Eastern Highland Rim City of Cookeville, Tennessee W. Gichuhi & L. Gamage 10.3390/atmos14020208
- Radiocarbon Measurements Reveal Underestimated Fossil CH4 and CO2 Emissions in London G. Zazzeri et al. 10.1029/2023GL103834
- Diverse biosphere influence on carbon and heat in mixed urban Mediterranean landscape revealed by high resolution thermal and optical remote sensing N. Parazoo et al. 10.1016/j.scitotenv.2021.151335
- Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO D. Wu et al. 10.5194/acp-22-14547-2022
- Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich S. Stagakis et al. 10.5194/bg-22-2133-2025
- A Method for Estimating the Background Column Concentration of CO2 Using the Lagrangian Approach Z. Pei et al. 10.1109/TGRS.2022.3176134
- Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data S. Wang et al. 10.1016/j.agrformet.2023.109622
- CO2 fluxes of vegetation in the Greenbelt of Ontario and increased net ecosystem emissions associated with its removal S. Madsen et al. 10.1525/elementa.2023.00102
- 基于多源数据的城市CO2源汇高空间分辨网格的构建方法研究 孙. Sun Erchang et al. 10.3788/AOS241741
- Observing System Choice Can Minimize Interference of the Biosphere in Studies of Urban CO2 Emissions R. Lal & E. Kort 10.1029/2022JD037452
- Toward a satellite-based monitoring system for urban CO2 emissions in support of global collective climate mitigation actions T. Wilmot et al. 10.1088/1748-9326/ad6017
- A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet) J. Gensheimer et al. 10.5194/bg-19-1777-2022
- Source decomposition of eddy-covariance CO2 flux measurements for evaluating a high-resolution urban CO2 emissions inventory K. Wu et al. 10.1088/1748-9326/ac7c29
- Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities? D. Mallia et al. 10.1029/2023JD038686
- Gross primary productivity of terrestrial ecosystems: a review of observations, remote sensing, and modelling studies over South Asia V. Pandey et al. 10.1007/s00704-024-05158-4
- Observing Anthropogenic and Biogenic CO2 Emissions in Los Angeles Using a Dense Sensor Network J. Kim et al. 10.1021/acs.est.4c11392
- Using urban-suburban difference of atmospheric CO2 to evaluate carbon neutrality capacity in Hangzhou, China B. Qi et al. 10.1016/j.jes.2024.10.027
- Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities Q. Zhuang et al. 10.1016/j.jag.2022.103058
- Quantification of fossil fuel CO2 from combined CO, δ13CO2 and Δ14CO2 observations J. Kim et al. 10.5194/acp-23-14425-2023
- Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions K. Wu et al. 10.5194/amt-16-581-2023
- Constraining Sector‐Specific CO2 Fluxes Using Space‐Based XCO2 Observations Over the Los Angeles Basin D. Roten et al. 10.1029/2023GL104376
- Machine learning for predicting urban greenhouse gas emissions: A systematic literature review Y. Jin & A. Sharifi 10.1016/j.rser.2025.115625
- Exploring CO2 anomalies in Brazilian biomes combining OCO-2 & 3 data: Linkages to wildfires patterns L. da Costa et al. 10.1016/j.asr.2024.01.016
- Assessing Impacts of Environmental Perturbations on Urban Biogenic Carbon Exchange in the Chicago Region P. Li et al. 10.1029/2023MS003867
- The potential of urban irrigation for counteracting carbon-climate feedback P. Li et al. 10.1038/s41467-024-46826-3
Latest update: 03 Jul 2025
Short summary
A model (SMUrF) is presented that estimates biogenic CO2 fluxes over cities around the globe to separate out biogenic fluxes from anthropogenic emissions. The model leverages satellite-based solar-induced fluorescence data and a machine-learning technique. We evaluate the biogenic fluxes against flux observations and show contrasts between biogenic and anthropogenic fluxes over cities, revealing urban–rural flux gradients, diurnal cycles, and the resulting imprints on atmospheric-column CO2.
A model (SMUrF) is presented that estimates biogenic CO2 fluxes over cities around the globe to...
