Articles | Volume 14, issue 9
https://doi.org/10.5194/bg-14-2343-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-2343-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Reviews and syntheses
| 
08 May 2017
Reviews and syntheses |
| 08 May 2017
Reviews and syntheses: Flying the satellite into your model: on the role of observation operators in constraining models of the Earth system and the carbon cycle
Thomas Kaminski
CORRESPONDING AUTHOR
The Inversion Lab, Tewessteg 4, 20249 Hamburg, Germany
Pierre-Philippe Mathieu
European Space Agency ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (Rome), Italy
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Cited
22 citations as recorded by crossref.
- Optimal design of surface CO2 observation network to constrain China’s land carbon sink Y. Wang et al. 10.1016/j.scib.2023.07.010
- Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion X. Ren et al. 10.1016/j.ecolmodel.2018.03.013
- Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends D. Pasetto et al. 10.1111/2041-210X.13018
- Progress and challenges in remotely sensed terrestrial carbon fluxes T. Wang et al. 10.1080/10095020.2024.2336599
- Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates T. Kaminski et al. 10.5194/bg-14-2527-2017
- Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
- Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories T. Kaminski et al. 10.1088/1748-9326/ac3cea
- Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design T. Kaminski & P. Rayner 10.5194/bg-14-4755-2017
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research Y. Kong et al. 10.3390/atmos10070354
- Mean European Carbon Sink Over 2010–2015 Estimated by Simultaneous Assimilation of Atmospheric CO2, Soil Moisture, and Vegetation Optical Depth M. Scholze et al. 10.1029/2019GL085725
- Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records T. Lavergne et al. 10.5194/tc-13-49-2019
- Synergistic retrievals of leaf area index and soil moisture from Sentinel-1 and Sentinel-2 T. Quaife et al. 10.1080/19479832.2022.2149629
- A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0 W. Knorr et al. 10.5194/gmd-18-2137-2025
- Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide T. Kaminski et al. 10.1016/j.rse.2017.08.017
- Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations K. Castro-Morales et al. 10.5194/bg-16-3009-2019
- Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine P. Reyes-Muñoz et al. 10.3390/rs14061347
- Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. 10.3389/fmars.2019.00429
- Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS) M. Chernetskiy et al. 10.3390/rs9070656
- Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System T. Kaminski et al. 10.3389/frsen.2022.887456
- Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance T. Kaminski et al. 10.5194/tc-12-2569-2018
- Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0 G. Schürmann et al. 10.5194/gmd-9-2999-2016
21 citations as recorded by crossref.
- Optimal design of surface CO2 observation network to constrain China’s land carbon sink Y. Wang et al. 10.1016/j.scib.2023.07.010
- Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion X. Ren et al. 10.1016/j.ecolmodel.2018.03.013
- Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends D. Pasetto et al. 10.1111/2041-210X.13018
- Progress and challenges in remotely sensed terrestrial carbon fluxes T. Wang et al. 10.1080/10095020.2024.2336599
- Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates T. Kaminski et al. 10.5194/bg-14-2527-2017
- Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
- Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories T. Kaminski et al. 10.1088/1748-9326/ac3cea
- Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design T. Kaminski & P. Rayner 10.5194/bg-14-4755-2017
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research Y. Kong et al. 10.3390/atmos10070354
- Mean European Carbon Sink Over 2010–2015 Estimated by Simultaneous Assimilation of Atmospheric CO2, Soil Moisture, and Vegetation Optical Depth M. Scholze et al. 10.1029/2019GL085725
- Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records T. Lavergne et al. 10.5194/tc-13-49-2019
- Synergistic retrievals of leaf area index and soil moisture from Sentinel-1 and Sentinel-2 T. Quaife et al. 10.1080/19479832.2022.2149629
- A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0 W. Knorr et al. 10.5194/gmd-18-2137-2025
- Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide T. Kaminski et al. 10.1016/j.rse.2017.08.017
- Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations K. Castro-Morales et al. 10.5194/bg-16-3009-2019
- Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine P. Reyes-Muñoz et al. 10.3390/rs14061347
- Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. 10.3389/fmars.2019.00429
- Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS) M. Chernetskiy et al. 10.3390/rs9070656
- Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System T. Kaminski et al. 10.3389/frsen.2022.887456
- Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance T. Kaminski et al. 10.5194/tc-12-2569-2018
Latest update: 17 Jul 2025
Short summary
This paper provides the formalism and examples of how observation operators can be used, in combination with data assimilation or retrieval techniques, to better ingest satellite products in a manner consistent with the dynamics of the Earth system expressed by models.
This paper provides the formalism and examples of how observation operators can be used, in...
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