Articles | Volume 22, issue 8
https://doi.org/10.5194/bg-22-2049-2025
© Author(s) 2025. 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-22-2049-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Apr 2025
Research article |
| 25 Apr 2025
| 25 Apr 2025
Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF)
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
Giulia Tagliabue
Remote Sensing of Environmental Dynamics Laboratory, Department of Environmental and Earth Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
Micol Rossini
Remote Sensing of Environmental Dynamics Laboratory, Department of Environmental and Earth Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
Francesco Pietro Fava
Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy
Cinzia Panigada
Remote Sensing of Environmental Dynamics Laboratory, Department of Environmental and Earth Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
Lutz Merbold
Integrative Agroecology Group, Research Division Agroecology and Environment, Agroscope, Reckenholzstr. 191, 8046 Zurich, Switzerland
Sonja Leitner
International Livestock Research Institute, Mazingira Centre, P.O. Box 30709, 00100 Nairobi, Kenya
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-12-2026, 17–24, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-17-2026, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-17-2026, 2026
Haodi Xu, Joshua Fan, Feng Tao, Lifen Jiang, Fengqi You, Benjamin Houlton, Ying Sun, Carla P. Gomes, and Yiqi Luo
Geosci. Model Dev., 19, 6777–6795, https://doi.org/10.5194/gmd-19-6777-2026, https://doi.org/10.5194/gmd-19-6777-2026, 2026
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We developed the Biogeochemistry-Informed Neural Network (BINN) which embeds a process-based model inside an AI framework so the model’s parameters can be learned from empirical data. BINN could recover prescribed parameters in synthetic tests and retrieve key processes controlling the modeled soil organic carbon when applied to ~25 000 soil profiles in US. It operates more than 50 times faster than the traditional Bayesian approach for data-model integration.
Sharon Gubamwoyo, Sonja M. Leitner, Gabriele Weigelhofer, Damaris G. Kisha, Oswald Omuron, Dominik Henrik Zak, Thomas Hein, and Gretchen M. Gettel
EGUsphere, https://doi.org/10.5194/egusphere-2026-3028, https://doi.org/10.5194/egusphere-2026-3028, 2026
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Wetlands regulate greenhouse gas (GHG) dynamics, but data from tropical and sub-Saharan African wetlands are limited. This study measured N₂O, CO₂, and CH₄ fluxes in intact, recovering, and converted highland valley-bottom wetlands in Taita Hills, Kenya, to assess effects of conversion on GHG dynamics, biogeochemical drivers, and soil organic carbon stocks. Conversion shifted systems from CH₄- to N₂O-dominated, while intact wetlands had the highest SOC stocks.
John N. Quinton, Gabriel Yesuf, German Baldi, Mengyi Gong, Kelvin Kinuthia, Ellen L. Fry, Yuda Odongo, Barthelemew Nyakundi, Joseph Hitimana, Patricia de Britto Costa, Alice A. Onyango, Sonja M. Leitner, Richard D. Bardgett, and Mariana C. Rufino
SOIL, 12, 451–469, https://doi.org/10.5194/soil-12-451-2026, https://doi.org/10.5194/soil-12-451-2026, 2026
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Beatriz P. Cazorla, Ana Meijide, Javier Cabello, Julio Peñas, Javier Martínez-López, Rodrigo Vargas, Leonardo Montagnani, Alexander Knohl, Lukas Siebicke, Benimiano Gioli, Jiří Dušek, Ladislav Šigut, Andreas Ibrom, Georg Wohlfahrt, Eugénie Paul-Limoges, Kathrin Fuchs, Antonio Manco, Marian Pavelka, Lutz Merbold, Lukas Hörtnagl, Pierpaolo Duce, Ignacio Goded, Kim Pilegaard, and Domingo Alcaraz-Segura
Biogeosciences, 23, 1223–1243, https://doi.org/10.5194/bg-23-1223-2026, https://doi.org/10.5194/bg-23-1223-2026, 2026
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We assess whether satellite-derived Ecosystem Functional Types (EFTs) reflect spatial heterogeneity in carbon fluxes across Europe. Using Eddy Covariance data from 50 sites, we show that EFTs capture distinct Net Ecosystem Exchange dynamics and perform slightly better than Plant Functional Types (PFTs). EFTs offer a scalable, annually updatable approach to monitor ecosystem functioning and its interannual variability.
Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk
Biogeosciences, 22, 4163–4186, https://doi.org/10.5194/bg-22-4163-2025, https://doi.org/10.5194/bg-22-4163-2025, 2025
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Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected spectral anomalies in satellite data associated with the herds. Our method can be used for diverse point sources, thereby improving emission inventories.
Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
Earth Syst. Sci. Data, 16, 5375–5404, https://doi.org/10.5194/essd-16-5375-2024, https://doi.org/10.5194/essd-16-5375-2024, 2024
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This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
Elodie Salmon, Fabrice Jégou, Bertrand Guenet, Line Jourdain, Chunjing Qiu, Vladislav Bastrikov, Christophe Guimbaud, Dan Zhu, Philippe Ciais, Philippe Peylin, Sébastien Gogo, Fatima Laggoun-Défarge, Mika Aurela, M. Syndonia Bret-Harte, Jiquan Chen, Bogdan H. Chojnicki, Housen Chu, Colin W. Edgar, Eugenie S. Euskirchen, Lawrence B. Flanagan, Krzysztof Fortuniak, David Holl, Janina Klatt, Olaf Kolle, Natalia Kowalska, Lars Kutzbach, Annalea Lohila, Lutz Merbold, Włodzimierz Pawlak, Torsten Sachs, and Klaudia Ziemblińska
Geosci. Model Dev., 15, 2813–2838, https://doi.org/10.5194/gmd-15-2813-2022, https://doi.org/10.5194/gmd-15-2813-2022, 2022
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A methane model that features methane production and transport by plants, the ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model, which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4, was calibrated and evaluated on 14 peatland sites. Results show that the model is sensitive to temperature and substrate availability over the top 75 cm of soil depth.
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
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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.
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
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The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
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Short summary
Solar-induced chlorophyll fluorescence (SIF), a tiny optical signal emitted from the core photosynthetic machinery, has emerged as a promising tool to evaluate vegetation growth from satellites. We find satellite SIF can capture intra-seasonal (i.e., from days to weeks) vegetation dynamics of dryland ecosystems, while greenness-based vegetation indices cannot. This study generates novel insights for developing effective real-time vegetation monitoring systems to inform climate risk management.
Solar-induced chlorophyll fluorescence (SIF), a tiny optical signal emitted from the core...
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