Articles | Volume 19, issue 6
https://doi.org/10.5194/bg-19-1777-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-1777-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2022
Research article |
| 31 Mar 2022
| 31 Mar 2022
A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)
Johannes Gensheimer
CORRESPONDING AUTHOR
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Philipp Köhler
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
Christian Frankenberg
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Related authors
No articles found.
Yitong Yao, Vincent Humphrey, Alexandra G. Konings, Jeffrey D. Wood, Yujie Wang, Ke Liu, Dien Wu, and Christian Frankenberg
EGUsphere, https://doi.org/10.5194/egusphere-2026-4858, https://doi.org/10.5194/egusphere-2026-4858, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study presents the first information-content analysis of mono-band and dual-band VOD observations for jointly retrieving vegetation water content, canopy temperature, and vegetation structure using an electromagnetic model, Bayesian inversion, and observing system simulation experiments. Our findings establish a new framework for interpreting VOD dynamics and provide practical guidance for the design of future multi-frequency microwave missions and retrieval algorithms.
Jacob B. H. Bushey, Michael Bertolacci, Joshua Benmergui, Julian Kostinek, Maryann Sargent, Marvin Knapp, Ethan Kyzivat, Sasha Ayvazov, Marcus Russi, Zhan Zhang, Apisada Chulakadabba, Jia Chen, Christopher Chan Miller, Sébastien Roche, Lucas A. Estrada, James D. East, Luna Yin, James P. Williams, Anthony Himmelberger, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-4693, https://doi.org/10.5194/egusphere-2026-4693, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
MethaneSAT was a designed to measure the concentration of methane in the atmosphere over oil and gas producing regions. Our modeling framework uses observations from MethaneSAT to estimate emissions from the Permian Basin. We show that the model accurately captures spatial patterns in emissions and compares well to other models that rely on different atmospheric transport. This data set is valuable for emissions monitoring and mitigation at high spatial resolution and with high precision.
Tobias Grasberger, Jia Chen, Benjamin Zanger, Alessandro Lupoli, Moritz Oliveira Makowski, Josef Stauber, and Felix Krahmer
EGUsphere, https://doi.org/10.5194/egusphere-2026-3161, https://doi.org/10.5194/egusphere-2026-3161, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As climate change makes reliable city-scale greenhouse gas monitoring more urgent, we developed a new mathematical method to better trace back emissions in cities. We combine approaches that detect point sources with methods that estimate widespread emissions for inferring urban emissions from air measurements. The result is a more accurate emission field reconstruction than traditional methods, adapted to the heterogeneous urban greenhouse gas emissions and supporting targeted climate action.
Yitong Yao, Yujie Wang, Yi Yin, Jeffrey D. Wood, and Christian Frankenberg
Biogeosciences, 23, 5461–5475, https://doi.org/10.5194/bg-23-5461-2026, https://doi.org/10.5194/bg-23-5461-2026, 2026
Short summary
Short summary
Droughts can leave lasting "scars" that slow down recovery long after the rain returns. In this study, we found that models assuming plants recover immediately after drought performed poorly, while those allowing only partial recovery matched real observations much better. Accounting for these delayed recovery processes will make land surface models more accurate in predicting carbon and water cycles under future droughts.
Sarah E. Hancock, Daniel J. Jacob, Rodrigo Jimenez, Andrés Ardila, Luis Morales-Rincon, Néstor Rojas, Lucas A. Estrada, Nicholas Balasus, James D. East, Melissa P. Sulprizio, Xiaolin Wang, James L. France, Lauren Potyk, Elise Penn, Zichong Chen, Daniel J. Varon, Christian Frankenberg, Marci Baranski, Andreea Calcan, and Robert J. Parker
Atmos. Chem. Phys., 26, 10455–10476, https://doi.org/10.5194/acp-26-10455-2026, https://doi.org/10.5194/acp-26-10455-2026, 2026
Short summary
Short summary
We combined satellite observations with detailed national data to improve estimates of Colombia’s methane emissions. Our results show that total methane emissions are higher than reported, with the biggest differences from fossil fuels. This approach demonstrates how satellite data can make national greenhouse gas reporting more accurate and transparent under the Paris Agreement.
Zhan Zhang, Maryann Sargent, Ethan Manninen, Jack D. Warren, Apisada Chulakadabba, Marcus Russi, Sasha Ayvazov, Joshua Benmergui, Marvin Knapp, Ethan Kyzivat, Christopher C. Miller, Sébastien Roche, Bingkun Luo, David J. Miller, Maya Nasr, Manuel Perez-Carrasco, Kang Sun, James P. Williams, Katlyn MacKay, Mark Omara, Jia Chen, Luis Guanter, Ritesh Gautam, Jonathan Franklin, Xiong Liu, and Steven C. Wofsy
Atmos. Meas. Tech., 19, 4637–4651, https://doi.org/10.5194/amt-19-4637-2026, https://doi.org/10.5194/amt-19-4637-2026, 2026
Short summary
Short summary
Methane released into the atmosphere is often difficult to detect in satellite imagery because the signals can be weak and hidden by background noise. We developed an automated method that improves the visibility of methane plumes while reducing false detections, decreasing the need for time-consuming manual inspection. The method identifies more small emission sources across different instruments, helping build a more complete understanding of methane emissions and their impacts.
Thomas Plewa, André Butz, Christian Frankenberg, Andrew K. Thorpe, and Julia Marshall
EGUsphere, https://doi.org/10.5194/egusphere-2026-2572, https://doi.org/10.5194/egusphere-2026-2572, 2026
Short summary
Short summary
In this paper, we present an improved deep learning method to estimate emissions of methane plumes from 2-D imagery. We present a new neural network architecture that provides insight into the wind speed used for the emission estimation and apply it to synthetic data that were used in previous studies. Our analysis illustrates that the performance increases by around 40% compared to the previous version, and that external wind speed data can be used to verify and improve the model's predictions.
Yanxia Li, Hengheng Zhang, Xuefeng Shi, Yaowei Li, Sophie Abou-Rizk, Jessica B. Smith, Zhaojin An, Adrian Wenzel, Junwei Song, Thomas Leisner, Frank Keutsch, Jia Chen, and Harald Saathoff
EGUsphere, https://doi.org/10.5194/egusphere-2026-2195, https://doi.org/10.5194/egusphere-2026-2195, 2026
Short summary
Short summary
Urban ultrafine particles (<100 nm) sources and growth mechanisms remain poorly constrained. Measurements in a Munich street canyon reveal that UFP number concentrations peak at night due to traffic and cooking emissions trapped under a shallow boundary layer. Particle growth is instead driven by condensation of organic vapours with strong seasonal shifts: biogenic and barbeque charcoal biomass-burning compounds dominate in summer, while combustion-related organics control winter growth.
Jacoline van Es, Juan Bettinelli, Jia Chen, Carina van der Veen, Stephan Henne, and Thomas Röckmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-1813, https://doi.org/10.5194/egusphere-2026-1813, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The increase in atmospheric methane needs to be reversed to mitigate ongoing global warming. To design effective mitigation strategies at the local/regional level, we need to know what the main sources in different regions. We show that in Hamburg, Germany, biogenic sources are the main contributor to local emissions, with additional contributions from waste and fossil CH4 sources. An unexpected source is the Elbe River, with spiking emissions at low tide when the river banks are exposed.
Yanxia Li, Hengheng Zhang, Xuefeng Shi, Yaowei Li, Sophie Abou-Rizk, Jessica B. Smith, Zhaojin An, Adrian Wenzel, Junwei Song, Thomas Leisner, Frank Keutsch, Jia Chen, and Harald Saathoff
Atmos. Chem. Phys., 26, 5813–5837, https://doi.org/10.5194/acp-26-5813-2026, https://doi.org/10.5194/acp-26-5813-2026, 2026
Short summary
Short summary
We analyzed Munich air samples across seasons to identify pollution sources. Traffic contributes less to particles than expected, while biomass burning dominates year-round. Summer barbecuing and winter heating release significant pollution. Monoterpene emissions from plants produce particles at night. Effective air quality improvement requires year-round strategies targeting biomass burning, not just vehicles. Understanding each city's pollution patterns is essential for public health.
Ann-Kristin Kunz, Samuel Hammer, Patrick Aigner, Laura Bignotti, Lars Borchardt, Jia Chen, Julian Della Coletta, Lukas Emmenegger, Markus Eritt, Xochilt Gutiérrez, Josh Hashemi, Rainer Hilland, Christopher Holst, Armin Jordan, Natascha Kljun, Richard Kneißl, Changxing Lan, Virgile Legendre, Ingeborg Levin, Benjamin Loubet, Matthias Mauder, Betty Molinier, Susanne Preunkert, Michel Ramonet, Stavros Stagakis, and Andreas Christen
Atmos. Chem. Phys., 26, 4967–5003, https://doi.org/10.5194/acp-26-4967-2026, https://doi.org/10.5194/acp-26-4967-2026, 2026
Short summary
Short summary
We present radiocarbon (14C)-based fossil fuel CO2 fluxes from relaxed eddy accumulation measurements on tall towers in the cities of Zurich, Paris, and Munich. By separating net CO2 fluxes into fossil and non-fossil components, these data reveal significant and variable contributions from human, plant, and soil respiration, as well as point-source emissions. These unique insights into CO2 flux composition offer crucial information for observation-based validation of urban emission estimates.
Nicholas Balasus, Daniel J. Jacob, A. Anthony Bloom, James D. East, Lucas A. Estrada, Sarah E. Hancock, Megan He, Todd A. Mooring, Alexander J. Turner, and John R. Worden
Atmos. Chem. Phys., 26, 4601–4617, https://doi.org/10.5194/acp-26-4601-2026, https://doi.org/10.5194/acp-26-4601-2026, 2026
Short summary
Short summary
We use satellite observations of methane to determine the amount of methane coming from Africa and relate this to the physical processes responsible, with relevance for climate change given methane's potency as a greenhouse gas. We find that the amount of methane coming from Africa has increased by a third across 2019–2024, driven primarily by steady increases in emissions from livestock, in addition to irregular surges from wetlands.
James Young Suk Yoon, Kelley C. Wells, Dylan B. Millet, Christian Frankenberg, Suniti Sanghavi, Abigail L. S. Swann, Joel A. Thornton, and Alexander J. Turner
Atmos. Chem. Phys., 26, 4509–4529, https://doi.org/10.5194/acp-26-4509-2026, https://doi.org/10.5194/acp-26-4509-2026, 2026
Short summary
Short summary
Isoprene is a molecule emitted by trees that is oxidized in the atmosphere within hours. Much of the isoprene globally is emitted in the remote tropics, where we have few direct observations of isoprene. Here, we use new satellite retrievals of isoprene to infer drivers of tropical isoprene variability. Across three regions, isoprene column variability is controlled by different factors, namely changes in isoprene emissions or changes in natural nitrogen oxide sources, like soils and fires.
Ethan Manninen, Apisada Chulakadabba, Maryann Sargent, Zhan Zhang, Harshil Kamdar, Jack Warren, Sébastien Roche, Christopher Chan Miller, Ethan Kyzivat, Joshua Benmergui, Jasna Pittman, Eleanor Walker, Jacob Bushey, Jenna Samra, Jacob Hawthorne, Bingkun Luo, Maya Nasr, Kang Sun, Jonathan Franklin, Xiong Liu, Jia Chen, and Steven Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-115, https://doi.org/10.5194/egusphere-2026-115, 2026
Short summary
Short summary
In this work, my coauthors and I interpret methane emissions observed by remote sensing systems as plumes, or point sources. We model the probability that a general imaging spectrometer will detect a plume, and apply this framework to multiple remote sensing systems. We show how two recent systems- MethanAIR and MethaneSAT- provide enough sensitivity to facility scale point sources to effectively characterize most plume emissions in the Permian Basin.
Patrick Aigner, Jia Chen, Felix Böhm, Mali Chariot, Lukas Emmenegger, Lars Frölich, Stuart Grange, Daniel Kühbacher, Klaus Kürzinger, Olivier Laurent, Moritz Makowski, Pascal Rubli, Adrian Schmitt, and Adrian Wenzel
Atmos. Meas. Tech., 19, 745–773, https://doi.org/10.5194/amt-19-745-2026, https://doi.org/10.5194/amt-19-745-2026, 2026
Short summary
Short summary
Dense urban CO2 monitoring is challenging due to cost and operational constraints. We developed a mid-cost sensor network for Munich, deployed on 17 rooftops. Temperature-stabilized enclosures and automated 2-point calibration ensured reliable performance, assessed by side-by-side comparison with a Picarro reference. In 1.5 years, the network collected 90 million measurements and resolved urban-rural gradients and seasonal diurnal patterns, capturing spatial CO2 variability at city scale.
Friedrich Klappenbach, Jia Chen, Moritz Makowski, Andreas Luther, Ronald C. Cohen, Jonathan E. Franklin, Steven Wofsy, and Taylor Jones
EGUsphere, https://doi.org/10.5194/egusphere-2026-204, https://doi.org/10.5194/egusphere-2026-204, 2026
Short summary
Short summary
This work presents a method to infer the upwind distance of emission sources from observed peak enhancements in atmospheric trace gas measurements. Using meteorological model output, the expected dilution of emissions from candidate source locations is quantified and compared with the observed concentration signals. The method enables the identification of plausible source locations and provides quantitative estimates of their emission strengths.
Nikhil Dadheech and Alexander J. Turner
Atmos. Chem. Phys., 26, 427–441, https://doi.org/10.5194/acp-26-427-2026, https://doi.org/10.5194/acp-26-427-2026, 2026
Short summary
Short summary
We developed a generalized emulator of atmospheric transport (FootNet v3) trained over the United States, enabling the emulation of both surface & column-averaged footprints at kilometer-scale resolution. We demonstrate that FootNet v3 generalizes to previously unseen regions and meteorological conditions, enabling accurate out-of-sample simulation of atmospheric transport. Flux inversion case studies show that FootNet matches or exceeds the performance of full-physics models in unseen regions.
Daniel Kühbacher, Jia Chen, Patrick Aigner, Mario Ilic, Ingrid Super, and Hugo Denier van der Gon
Geosci. Model Dev., 18, 9967–9990, https://doi.org/10.5194/gmd-18-9967-2025, https://doi.org/10.5194/gmd-18-9967-2025, 2025
Short summary
Short summary
We present DRIVE v1.0, a data-driven framework to estimate road transport emissions, their temporal profiles, and the associated uncertainties. The method was applied to the city of Munich, where we present bottom-up emission estimates for the years 2019 to 2022. The estimates are compared against official municipal reports as well as national and European downscaled inventories.
Eric J. Mei, Gregory J. Hakim, Max Taniguchi-King, Dominik Stiller, and Alexander J. Turner
Atmos. Chem. Phys., 25, 15033–15045, https://doi.org/10.5194/acp-25-15033-2025, https://doi.org/10.5194/acp-25-15033-2025, 2025
Short summary
Short summary
Chemistry-climate models are used to investigate how physical climate influences the composition of the atmosphere but are slow and expensive to run. We train a linear inverse model that can replicate the behavior of chemistry-climate models at low computational cost. It captures how large-scale climate features like El Niño affect atmospheric composition and can make accurate forecasts up to a year ahead. This model enables fast hypothesis testing and estimates of past atmospheric composition.
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
Atmos. Chem. Phys., 25, 14387–14410, https://doi.org/10.5194/acp-25-14387-2025, https://doi.org/10.5194/acp-25-14387-2025, 2025
Short summary
Short summary
To support the city of Zurich in tracking its path to net-zero greenhouse gas emissions planned to be reached by 2040, a CO2 emission monitoring system was established. The system combines a dense network of CO2 sensors with a high-resolution atmospheric transport model GRAMM/GRAL. This study presents the setup of the model together with its numerous inputs and evaluates its performance in comparison with observations from the CO2 sensor network.
Matthew S. Johnson, Sofia D. Hamilton, Seongeun Jeong, Yu Yan Cui, Dien Wu, Alex Turner, and Marc Fischer
Atmos. Chem. Phys., 25, 8475–8492, https://doi.org/10.5194/acp-25-8475-2025, https://doi.org/10.5194/acp-25-8475-2025, 2025
Short summary
Short summary
Satellites, such as NASA's Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3, respectively), retrieve carbon dioxide (CO2) concentrations, which provide vital information for estimating surface CO2 emissions. Here, we investigate the ability of OCO-2/3 retrievals to constrain CO2 emissions for the state of California for the major emission sectors (i.e., fossil fuels, net ecosystem exchange, and wildfire).
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025, https://doi.org/10.5194/acp-25-5159-2025, 2025
Short summary
Short summary
We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025, https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Short summary
The balance between CO2 uptake and emissions from urban green areas is still not well understood. This study evaluated for the first time the urban park CO2 exchange simulations with four different types of biosphere model by comparing them with observations. Even though some advantages and disadvantages of the different model types were identified, there was no strong evidence that more complex models performed better than simple ones.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Neil Humpage, Hartmut Boesch, William Okello, Jia Chen, Florian Dietrich, Mark F. Lunt, Liang Feng, Paul I. Palmer, and Frank Hase
Atmos. Meas. Tech., 17, 5679–5707, https://doi.org/10.5194/amt-17-5679-2024, https://doi.org/10.5194/amt-17-5679-2024, 2024
Short summary
Short summary
We used a Bruker EM27/SUN spectrometer within an automated weatherproof enclosure to measure greenhouse gas column concentrations over a 3-month period in Jinja, Uganda. The portability of the EM27/SUN allows us to evaluate satellite and model data in locations not covered by traditional validation networks. This is of particular value in tropical Africa, where extensive terrestrial ecosystems are a significant store of carbon and play a key role in the atmospheric budgets of CO2 and CH4.
Ayah Abu-Hani, Jia Chen, Vigneshkumar Balamurugan, Adrian Wenzel, and Alessandro Bigi
Atmos. Meas. Tech., 17, 3917–3931, https://doi.org/10.5194/amt-17-3917-2024, https://doi.org/10.5194/amt-17-3917-2024, 2024
Short summary
Short summary
This study examined the transferability of machine learning calibration models among low-cost sensor units targeting NO2 and NO. The global models were evaluated under similar and different emission conditions. To counter cross-sensitivity, the study proposed integrating O3 measurements from nearby reference stations, in Switzerland. The models show substantial improvement when O3 measurements are incorporated, which is more pronounced when in regions with elevated O3 concentrations.
Yitong Yao, Philippe Ciais, Emilie Joetzjer, Wei Li, Lei Zhu, Yujie Wang, Christian Frankenberg, and Nicolas Viovy
Earth Syst. Dynam., 15, 763–778, https://doi.org/10.5194/esd-15-763-2024, https://doi.org/10.5194/esd-15-763-2024, 2024
Short summary
Short summary
Elevated CO2 concentration (eCO2) is critical for shaping the future path of forest carbon uptake, while uncertainties remain about concurrent carbon loss. Here, we found that eCO2 might amplify competition-induced carbon loss, while the extent of drought-induced carbon loss hinges on the balance between heightened biomass density and water-saving benefits. This is the first time that such carbon loss responses to ongoing climate change have been quantified separately over the Amazon rainforest.
Benedikt Herkommer, Carlos Alberti, Paolo Castracane, Jia Chen, Angelika Dehn, Florian Dietrich, Nicholas M. Deutscher, Matthias Max Frey, Jochen Groß, Lawson Gillespie, Frank Hase, Isamu Morino, Nasrin Mostafavi Pak, Brittany Walker, and Debra Wunch
Atmos. Meas. Tech., 17, 3467–3494, https://doi.org/10.5194/amt-17-3467-2024, https://doi.org/10.5194/amt-17-3467-2024, 2024
Short summary
Short summary
The Total Carbon Column Observing Network is a network of ground-based Fourier transform infrared (FTIR) spectrometers used mainly for satellite validation. To ensure the highest-quality validation data, the network needs to be highly consistent. This is a major challenge, which so far is solved by site comparisons with airborne in situ measurements. In this work, we describe the use of a portable FTIR spectrometer as a travel standard for evaluating the consistency of TCCON sites.
Juliëtte C. S. Anema, Klaas Folkert Boersma, Piet Stammes, Gerbrand Koren, William Woodgate, Philipp Köhler, Christian Frankenberg, and Jacqui Stol
Biogeosciences, 21, 2297–2311, https://doi.org/10.5194/bg-21-2297-2024, https://doi.org/10.5194/bg-21-2297-2024, 2024
Short summary
Short summary
To keep the Paris agreement goals within reach, negative emissions are necessary. They can be achieved with mitigation techniques, such as reforestation, which remove CO2 from the atmosphere. While governments have pinned their hopes on them, there is not yet a good set of tools to objectively determine whether negative emissions do what they promise. Here we show how satellite measurements of plant fluorescence are useful in detecting carbon uptake due to reforestation and vegetation regrowth.
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1, https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
Preprint archived
Short summary
Short summary
Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation absorbed by chlorophyll. Potential uses of our model are to advance our understanding of the timing and magnitude of photosynthesis, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.
Ke Liu, Yujie Wang, Troy S. Magney, and Christian Frankenberg
Biogeosciences, 21, 1501–1516, https://doi.org/10.5194/bg-21-1501-2024, https://doi.org/10.5194/bg-21-1501-2024, 2024
Short summary
Short summary
Stomata are pores on leaves that regulate gas exchange between plants and the atmosphere. Existing land models unrealistically assume stomata can jump between steady states when the environment changes. We implemented dynamic modeling to predict gradual stomatal responses at different scales. Results suggested that considering this effect on plant behavior patterns in diurnal cycles was important. Our framework also simplified simulations and can contribute to further efficiency improvements.
Xinxu Zhao, Jia Chen, Julia Marshall, Michal Gałkowski, Stephan Hachinger, Florian Dietrich, Ankit Shekhar, Johannes Gensheimer, Adrian Wenzel, and Christoph Gerbig
Atmos. Chem. Phys., 23, 14325–14347, https://doi.org/10.5194/acp-23-14325-2023, https://doi.org/10.5194/acp-23-14325-2023, 2023
Short summary
Short summary
We develop a modeling framework using the Weather Research and Forecasting model at a high spatial resolution (up to 400 m) to simulate atmospheric transport of greenhouse gases and interpret column observations. Output is validated against weather stations and column measurements in August 2018. The differential column method is applied, aided by air-mass transport tracing with the Stochastic Time-Inverted Lagrangian Transport (STILT) model, also for an exploratory measurement interpretation.
Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 10267–10285, https://doi.org/10.5194/acp-23-10267-2023, https://doi.org/10.5194/acp-23-10267-2023, 2023
Short summary
Short summary
In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
Andreas Forstmaier, Jia Chen, Florian Dietrich, Juan Bettinelli, Hossein Maazallahi, Carsten Schneider, Dominik Winkler, Xinxu Zhao, Taylor Jones, Carina van der Veen, Norman Wildmann, Moritz Makowski, Aydin Uzun, Friedrich Klappenbach, Hugo Denier van der Gon, Stefan Schwietzke, and Thomas Röckmann
Atmos. Chem. Phys., 23, 6897–6922, https://doi.org/10.5194/acp-23-6897-2023, https://doi.org/10.5194/acp-23-6897-2023, 2023
Short summary
Short summary
Large cities emit greenhouse gases which contribute to global warming. In this study, we measured the release of one important green house gas, methane, in Hamburg. Multiple sources that contribute to methane emissions were located and quantified. Methane sources were found to be mainly caused by human activity (e.g., by release from oil and gas refineries). Moreover, potential natural sources have been located, such as the Elbe River and lakes.
Vincent Humphrey and Christian Frankenberg
Biogeosciences, 20, 1789–1811, https://doi.org/10.5194/bg-20-1789-2023, https://doi.org/10.5194/bg-20-1789-2023, 2023
Short summary
Short summary
Microwave satellites can be used to monitor how vegetation biomass changes over time or how droughts affect the world's forests. However, such satellite data are still difficult to validate and interpret because of a lack of comparable field observations. Here, we present a remote sensing technique that uses the Global Navigation Satellite System (GNSS) as a makeshift radar, making it possible to observe canopy transmissivity at any existing environmental research site in a cost-efficient way.
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023, https://doi.org/10.5194/acp-23-3325-2023, 2023
Short summary
Short summary
We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
Maximilian Rißmann, Jia Chen, Gregory Osterman, Xinxu Zhao, Florian Dietrich, Moritz Makowski, Frank Hase, and Matthäus Kiel
Atmos. Meas. Tech., 15, 6605–6623, https://doi.org/10.5194/amt-15-6605-2022, https://doi.org/10.5194/amt-15-6605-2022, 2022
Short summary
Short summary
The Orbiting Carbon Observatory 2 (OCO-2) measures atmospheric concentrations of the most potent greenhouse gas, CO2, globally. By comparing its measurements to a ground-based monitoring network in Munich (MUCCnet), we find that the satellite is able to reliably detect urban CO2 concentrations. Furthermore, spatial CO2 differences captured by OCO-2 and MUCCnet are strongly correlated, which indicates that OCO-2 could be helpful in determining urban CO2 emissions from space.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
Short summary
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Yujie Wang and Christian Frankenberg
Biogeosciences, 19, 4705–4714, https://doi.org/10.5194/bg-19-4705-2022, https://doi.org/10.5194/bg-19-4705-2022, 2022
Short summary
Short summary
Plant hydraulics is often misrepresented in topical research. We highlight the commonly seen ambiguities and/or mistakes, with equations and figures to help visualize the potential biases. We recommend careful thinking when using or modifying existing plant hydraulic terms, methods, and models.
Yujie Wang and Christian Frankenberg
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-172, https://doi.org/10.5194/bg-2022-172, 2022
Revised manuscript not accepted
Short summary
Short summary
Leaf light absorption coefficient is often not measured along with leaf gas exchange, but assumed to be constant. This potentially causes biases in estimated photosynthetic capacity and modeled photosynthetic rates. We explored how leaf light absorption features and light source may impact the photosynthesis modeling, and found that the biases are dependent of model assumptions. Researchers need to be more cautious with these inaccurate assumptions in photosynthesis models.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
Short summary
Short summary
We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Vigneshkumar Balamurugan, Jia Chen, Zhen Qu, Xiao Bi, and Frank N. Keutsch
Atmos. Chem. Phys., 22, 7105–7129, https://doi.org/10.5194/acp-22-7105-2022, https://doi.org/10.5194/acp-22-7105-2022, 2022
Short summary
Short summary
In this study, we investigated the response of secondary pollutants to changes in precursor emissions, focusing on the formation of secondary PM, during the COVID-19 lockdown period. We show that, due to the decrease in primary NOx emissions, atmospheric oxidizing capacity is increased. The nighttime increase in ozone, caused by less NO titration, results in higher NO3 radicals, which contribute significantly to the formation of PM nitrates. O3 should be limited in order to control PM pollution.
Andreas Luther, Julian Kostinek, Ralph Kleinschek, Sara Defratyka, Mila Stanisavljević, Andreas Forstmaier, Alexandru Dandocsi, Leon Scheidweiler, Darko Dubravica, Norman Wildmann, Frank Hase, Matthias M. Frey, Jia Chen, Florian Dietrich, Jarosław Nȩcki, Justyna Swolkień, Christoph Knote, Sanam N. Vardag, Anke Roiger, and André Butz
Atmos. Chem. Phys., 22, 5859–5876, https://doi.org/10.5194/acp-22-5859-2022, https://doi.org/10.5194/acp-22-5859-2022, 2022
Short summary
Short summary
Coal mining is an extensive source of anthropogenic methane emissions. In order to reduce and mitigate methane emissions, it is important to know how much and where the methane is emitted. We estimated coal mining methane emissions in Poland based on atmospheric methane measurements and particle dispersion modeling. In general, our emission estimates suggest higher emissions than expected by previous annual emission reports.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
Short summary
Short summary
Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Russell Doughty, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun, and Christian Frankenberg
Earth Syst. Sci. Data, 14, 1513–1529, https://doi.org/10.5194/essd-14-1513-2022, https://doi.org/10.5194/essd-14-1513-2022, 2022
Short summary
Short summary
We describe and compare solar-induced chlorophyll fluorescence data produced by NASA from the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 platforms.
Helen L. Fitzmaurice, Alexander J. Turner, Jinsol Kim, Katherine Chan, Erin R. Delaria, Catherine Newman, Paul Wooldridge, and Ronald C. Cohen
Atmos. Chem. Phys., 22, 3891–3900, https://doi.org/10.5194/acp-22-3891-2022, https://doi.org/10.5194/acp-22-3891-2022, 2022
Short summary
Short summary
On-road emissions are thought to vary widely from existing predictions, as the effects of the age of the vehicle fleet, the performance of emission control systems, and variations in speed are difficult to assess under ambient driving conditions. We present an observational approach to characterize on-road emissions and show that the method is consistent with other approaches to within ~ 3 %.
Gerrit Kuhlmann, Ka Lok Chan, Sebastian Donner, Ying Zhu, Marc Schwaerzel, Steffen Dörner, Jia Chen, Andreas Hueni, Duc Hai Nguyen, Alexander Damm, Annette Schütt, Florian Dietrich, Dominik Brunner, Cheng Liu, Brigitte Buchmann, Thomas Wagner, and Mark Wenig
Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, https://doi.org/10.5194/amt-15-1609-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO2 in Munich, we conducted the Munich NO2 Imaging Campaign (MuNIC), where NO2 was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO2 and its sources.
Yujie Wang and Christian Frankenberg
Biogeosciences, 19, 29–45, https://doi.org/10.5194/bg-19-29-2022, https://doi.org/10.5194/bg-19-29-2022, 2022
Short summary
Short summary
Modeling vegetation canopy is important in predicting whether the land remains a carbon sink to mitigate climate change in the near future. Vegetation canopy model complexity, however, impacts the model-predicted carbon and water fluxes as well as canopy fluorescence, even if the same suite of model inputs is used. Given the biases caused by canopy model complexity, we recommend not misusing parameters inverted using different models or assumptions.
Alexander J. Turner, Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung, and Ronald C. Cohen
Biogeosciences, 18, 6579–6588, https://doi.org/10.5194/bg-18-6579-2021, https://doi.org/10.5194/bg-18-6579-2021, 2021
Short summary
Short summary
This work builds a high-resolution estimate (500 m) of gross primary productivity (GPP) over the US using satellite measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) between 2018 and 2020. We identify ecosystem-specific scaling factors for estimating gross primary productivity (GPP) from TROPOMI SIF. Extreme precipitation events drive four regional GPP anomalies that account for 28 % of year-to-year GPP differences across the US.
Siraput Jongaramrungruang, Georgios Matheou, Andrew K. Thorpe, Zhao-Cheng Zeng, and Christian Frankenberg
Atmos. Meas. Tech., 14, 7999–8017, https://doi.org/10.5194/amt-14-7999-2021, https://doi.org/10.5194/amt-14-7999-2021, 2021
Short summary
Short summary
This study shows how precision error and bias in column methane retrieval change with different instrument specifications and the impact of spectrally complex surface albedos on retrievals. We show how surface interferences can be mitigated with an optimal spectral resolution and a higher polynomial degree in a retrieval process. The findings can inform future satellite instrument designs to have robust observations capable of separating real CH4 plume enhancements from surface interferences.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
Short summary
Short summary
Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Yujie Wang, Philipp Köhler, Liyin He, Russell Doughty, Renato K. Braghiere, Jeffrey D. Wood, and Christian Frankenberg
Geosci. Model Dev., 14, 6741–6763, https://doi.org/10.5194/gmd-14-6741-2021, https://doi.org/10.5194/gmd-14-6741-2021, 2021
Short summary
Short summary
We present the first step in testing a new land model as part of a new Earth system model. Our model links plant hydraulics, stomatal optimization theory, and a comprehensive canopy radiation scheme. We compared model-predicted carbon and water fluxes to flux tower observations and model-predicted sun-induced chlorophyll fluorescence to satellite retrievals. Our model quantitatively predicted the carbon and water fluxes as well as the canopy fluorescence yield.
Cited articles
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A next-generation hyperparameter optimization framework, in: KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 4–8 August 2019, Anchorage, AK, USA, 2623–2631, https://doi.org/10.1145/3292500.3330701, 2019. a
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance
and terrestrial photosynthesis, Science Advances, 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. a
Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, 1st edn., New York, NY, ISBN 978-0-387-31073-2, 2007. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Brunet, D., Vrscay, E. R., and Wang, Z.: On the mathematical properties of the structural similarity index, IEEE T. Image Process., 21, 1488–1499, 2011. a
Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E., Herold, M., and Fritz, S.: Copernicus Global Land Service: Land Cover 100 m: collection 3: epoch 2018: Globe, Zenodo [data set], https://doi.org/10.5281/zenodo.3518038, 2020. a, b
Camps-Valls, G., Campos-Taberner, M., Moreno-Martínez, Á., Walther, S., Duveiller, G., Cescatti, A., Mahecha, M. D., Muñoz-Marí, J., García-Haro, F. J., Guanter, L., and Jung, M.: A unified vegetation index for quantifying the terrestrial biosphere, Science Advances, 7, eabc7447, https://doi.org/10.1126/sciadv.abc7447, 2021. a, b, c, d
Castro, A. O., Chen, J., Zang, C. S., Shekhar, A., Jimenez, J. C.,
Bhattacharjee, S., Kindu, M., Morales, V. H., and Rammig, A.: OCO-2
solar-induced chlorophyll fluorescence variability across ecoregions of the
Amazon basin and the extreme drought effects of El Niño (2015–2016),
Remote Sens., 12, 1202, https://doi.org/10.3390/rs12071202, 2020. a
Chen, S., Liu, L., He, X., Liu, Z., and Peng, D.: Upscaling from Instantaneous to Daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for Satellite Products, Remote Sens., 12, 2083, https://doi.org/10.3390/rs12132083, 2020. a
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 13 August 2021), 2017. a
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 pp., https://pubs.usgs.gov/of/2011/1073/ (last access: 20 August 2021), 2011. a
Dechant, B., Ryu, Y., Badgley, G., Köhler, P., Rascher, U., Migliavacca, M., Zhang, Y., Tagliabue, G., Guan, K., Rossini, M., and Goulas, Y.: NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales, Remote Sens. Environ., 268, 112763, https://doi.org/10.1016/j.rse.2021.112763, 2022. a
Duveiller, G., Filipponi, F., Walther, S., Köhler, P., Frankenberg, C., Guanter, L., and Cescatti, A.: A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity, Earth Syst. Sci. Data, 12, 1101–1116, https://doi.org/10.5194/essd-12-1101-2020, 2020. a
Fisher, A., Rudin, C., and Dominici, F.: All Models are Wrong, but Many are
Useful: Learning a Variable's Importance by Studying an Entire Class of
Prediction Models Simultaneously, J. Mach. Learn. Res., 20, 1–81, 2019. a
Frankenberg, C., Fisher, J. B., Worden, J., Badgley, G., Saatchi, S. S., Lee, J. E., Toon, G. C., Butz, A., Jung, M., Kuze, A., and Yokota, T.: New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity, Geophys. Res. Lett., 38, L17706, https://doi.org/10.1029/2011GL048738, 2011. a, b, c, d
Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Hauck, J., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D. C. E., Canadell, J. G., Ciais, P., Jackson, R. B., Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., Bopp, L., Buitenhuis, E., Chandra, N., Chevallier, F., Chini, L. P., Currie, K. I., Feely, R. A., Gehlen, M., Gilfillan, D., Gkritzalis, T., Goll, D. S., Gruber, N., Gutekunst, S., Harris, I., Haverd, V., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A. K., Joetzjer, E., Kaplan, J. O., Kato, E., Klein Goldewijk, K., Korsbakken, J. I., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Marland, G., McGuire, P. C., Melton, J. R., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Neill, C., Omar, A. M., Ono, T., Peregon, A., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Séférian, R., Schwinger, J., Smith, N., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Werf, G. R., Wiltshire, A. J., and Zaehle, S.: Global Carbon Budget 2019, Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, 2019. a
Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., and Chen, J.: TROPOMI SIF high resolution data at 0.005∘ for CONUS as estimated by the convolutional neural network SIFnet (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6321987, 2022. a
Gentine, P. and Alemohammad, S.: Reconstructed solar-induced fluorescence: A machine learning vegetation product based on MODIS surface reflectance to reproduce GOME-2 solar-induced fluorescence, Geophys. Res. Lett., 45, 3136–3146, 2018. a
Google LLC: Google earth. Chicago, USA, 41.895∘ N–42.061∘ N, 88.016∘ W–87.7856∘ W, http://earth.google.com, last access: 15 December 2021. a
Gregorutti, B., Michel, B., and Saint-Pierre, P.: Grouped variable importance
with random forests and application to multiple functional data analysis,
Comput. Stat. Data An., 90, 15–35, 2015. a
Gregorutti, B., Michel, B., and Saint-Pierre, P.: Correlation and variable
importance in random forests, Stat. Comput., 27, 659–678, 2017. a
Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton, J. O., Austin, M. P., Collins, C. D., and Cook, W. M.: Habitat fragmentation and its lasting impact on Earth’s ecosystems, Science Advances, 1, e1500052, https://doi.org/10.1126/sciadv.1500052, 2015. a, b
Hanes, J.: Biophysical applications of satellite remote sensing, Springer
Science & Business Media, https://doi.org/10.1007/978-3-642-25047-7, 2013. a, b
Huete, A. R., Didan, K., Shimabukuro, Y. E., Ratana, P., Saleska, S. R.,
Hutyra, L. R., Yang, W., Nemani, R. R., and Myneni, R.: Amazon rainforests
green-up with sunlight in dry season, Geophys. Res. Lett., 33, L06405, https://doi.org/10.1029/2005GL025583, 2006. a
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65–69, 2021. a
Joiner, J., Yoshida, Y., Vasilkov, A. P., Yoshida, Y., Corp, L. A., and Middleton, E. M.: First observations of global and seasonal terrestrial chlorophyll fluorescence from space, Biogeosciences, 8, 637–651, https://doi.org/10.5194/bg-8-637-2011, 2011. a
Joiner, J., Guanter, L., Lindstrot, R., Voigt, M., Vasilkov, A. P., Middleton, E. M., Huemmrich, K. F., Yoshida, Y., and Frankenberg, C.: Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803–2823, https://doi.org/10.5194/amt-6-2803-2013, 2013. a
Li, X. and Xiao, J.: A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data, Remote Sens., 11, 517, https://doi.org/10.3390/rs11050517, 2019. a, b
Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K.: Enhanced deep residual networks for single image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 136–144, arXiv [preprint], arXiv:1707.02921, 10 July 2017. a
Magney, T. S., Bowling, D. R., Logan, B. A., Grossmann, K., Stutz, J., Blanken, P. D., Burns, S. P., Cheng, R., Garcia, M. A., Köhler, P., and Lopez, S.: Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence, P. Natl. Acad. Sci. USA, 116, 11640–11645, 2019. a
Magney, T. S., Barnes, M. L., and Yang, X.: On the covariation of chlorophyll
fluorescence and photosynthesis across scales, Geophys. Res. Lett.,
47, e2020GL091098, https://doi.org/10.1029/2020GL091098, 2020. a, b
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X., Dunn, A. L., Lin, J. C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A
satellite-based biosphere parameterization for net ecosystem CO2
exchange: Vegetation Photosynthesis and Respiration Model (VPRM), Global
Biogeochem. Cy., 22, GB2005, https://doi.org/10.1029/2006GB002735, 2008. a
Miller, J. B., Lehman, S. J., Verhulst, K. R., Miller, C. E., Duren, R. M., Yadav, V., Newman, S., and Sloop, C. D.: Large and seasonally varying biospheric CO2 fluxes in the Los Angeles megacity revealed by atmospheric radiocarbon, P. Natl. Acad. Sci. USA, 117,
26681–26687, 2020. a
Morreale, L. L., Thompson, J. R., Tang, X., Reinmann, A., and Hutyra, L. R.: Elevated growth and biomass along temperate forest edges, Nat. Commun., 12, 7181, https://doi.org/10.1038/s41467-021-27373-7, 2021. a
OCO-2 Science Team/Michael Gunson, A. E.: OCO-2 Level 2 bias-corrected
solar-induced fluorescence and other select fields from the IMAP-DOAS
algorithm aggregated as daily files, in: Retrospective processing V10r,
Goddard Earth Sciences Data and Information Services Center (GES DISC),
https://doi.org/10.5067/XO2LBBNPO010, 2020. a, b, c, d
OCO-3 Science Team/Michael Gunson, A. E.: OCO-3 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, in: Retrospective processing VEarlyR, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://disc.gsfc.nasa.gov/datacollection/OCO3_L2_Lite_SIF_EarlyR.html (last access: 20 August 2021), 2020. a, b, c, d
PySolar: Python library PySolar, PySolar, https://github.com/pingswept/pysolar, last access: 30 September 2021. a
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., and Grünwald, T.: On the separation of net ecosystem exchange into assimilation and ecosystem
respiration: review and improved algorithm, Glob. Change Biol., 11,
1424–1439, 2005. a
Reinmann, A. B. and Hutyra, L. R.: Edge effects enhance carbon uptake and its
vulnerability to climate change in temperate broadleaf forests, P. Natl. Acad. Sci. USA, 114, 107–112, 2017. a
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF
Adjusted Ref Daily L3 Global – 500 m V006, NASA EOSDIS Land
Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A4.006, 2015. a
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration,
Int. J. Remote Sens., 6, 1335–1372, 1985. a
Shekhar, A., Chen, J., Bhattacharjee, S., Buras, A., Castro, A. O., Zang, C. S., and Rammig, A.: Capturing the impact of the 2018 european drought and
heat across different vegetation types using OCO-2 solar-induced
fluorescence, Remote Sens., 12, 3249, https://doi.org/10.3390/rs12193249, 2020a. a
Shekhar, A., Chen, J., Paetzold, J. C., Dietrich, F., Zhao, X., Bhattacharjee, S., Ruisinger, V., and Wofsy, S. C.: Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite, Environ. Res. Lett., 15, 095010, https://doi.org/10.1088/1748-9326/ab9cfe, 2020b. a
Siegmann, B., Cendrero-Mateo, M. P., Cogliati, S., Damm, A., Gamon, J., Herrera, D., Jedmowski, C., Junker-Frohn, L.V., Kraska, T., Muller, O., and Rademske, P.: Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant, Remote Sens. Environ.,
264, 112609, https://doi.org/10.1016/j.rse.2021.112609, 2021. a
Sims, D. A., Rahman, A. F., Cordova, V. D., El‐Masri, B. Z., Baldocchi, D. D., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., and Oechel, W. C.: On the use of MODIS EVI to assess gross primary productivity of North American ecosystems, J. Geophys. Res.-Biogeo., 111, G04015, https://doi.org/10.1029/2006JG000162, 2006. a
Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K., and Thompson, J. R.: Piecing together the fragments: elucidating edge effects on forest carbon dynamics, Front. Ecol. Environ., 16, 213–221, 2018. a
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, 1979. a
Wang, X., Biederman, J. A., Knowles, J. F., Scott, R. L., Turner, A. J., Dannenberg, M. P., Köhler, P., Frankenberg, C., Litvak, M. E., Flerchinger, G. N., Law, B. E., Kwon, H., Reed, S. C., Parton, W. J., Barron-Gafford, G. A., and Smith, W. K.: Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics, Remote Sens. Environ., 270, 112858,
https://doi.org/10.1016/j.rse.2021.112858, 2022. a
Wu, D., Lin, J. C., Duarte, H. F., Yadav, V., Parazoo, N. C., Oda, T., and Kort, E. A.: A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1), Geosci. Model Dev., 14, 3633–3661, https://doi.org/10.5194/gmd-14-3633-2021, 2021.
a
Yu, L., Wen, J., Chang, C., Frankenberg, C., and Sun, Y.: High-resolution global contiguous SIF of OCO-2, Geophys. Res. Lett., 46, 1449–1458, 2019. a
Zeng, J., Matsunaga, T., Tan, Z.-H., Saigusa, N., Shirai, T., Tang, Y., Peng, S., and Fukuda, Y.: Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest, Scientific Data, 7,
1–11, 2020. a
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S., and Gentine, P.: A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks, Biogeosciences, 15, 5779–5800, https://doi.org/10.5194/bg-15-5779-2018, 2018. a
Zhang, Z., Xu, W., Qin, Q., and Long, Z.: Downscaling Solar-Induced Chlorophyll Fluorescence Based on Convolutional Neural Network Method to Monitor Agricultural Drought, IEEE T. Geosci. Remote, 59, 1012–1028, https://doi.org/10.1109/TGRS.2020.2999371, 2021. a
Short summary
We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
We develop a convolutional neural network, named SIFnet, that increases the spatial resolution...
Altmetrics
Final-revised paper
Preprint