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
Ying Sun
CORRESPONDING AUTHOR
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
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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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3722, https://doi.org/10.5194/egusphere-2025-3722, 2025
This preprint is open for discussion and under review for SOIL (SOIL).
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We investigated how the soil degradation status of smallholder grazing, classified using remote sensing, in two districts of Western Kenya, compared with measured soil parameters at 90 sites. Grouping sites using soil data had some agreement with the remote sensing (RS) classification. Overall, our results suggest that supplementing RS methods with microbial biomass C, soil P, percent C and N, and soil pH, could enhance our ability to identify degraded soils and target restoration efforts.
Haodi Xu, Joshua Fan, Feng Tao, Lifen Jiang, Fengqi You, Benjamin Z. Houlton, Ying Sun, Carla P. Gomes, and Yiqi Luo
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2502.00672, https://doi.org/https://doi.org/10.48550/arXiv.2502.00672, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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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 big data. BINN recovered known parameters in synthetic tests and revealed key controls when applied to about 25 000 soil profiles across the contiguous US. It operates more than 50 times faster than Bayesian approaches while reproducing similar key processes governing SOC stocks.
Beatriz P. Cazorla, Ana Meijide, Javier Cabello, Julio Peñas, Rodrigo Vargas, Javier Martínez-López, 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2835, https://doi.org/10.5194/egusphere-2025-2835, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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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 PFTs. EFTs offer a scalable, annually updatable approach to monitor ecosystem functioning and its interannual variability.
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.
Yang Liu, Simon Schallhart, Ditte Taipale, Toni Tykkä, Matti Räsänen, Lutz Merbold, Heidi Hellén, and Petri Pellikka
Atmos. Chem. Phys., 21, 14761–14787, https://doi.org/10.5194/acp-21-14761-2021, https://doi.org/10.5194/acp-21-14761-2021, 2021
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We studied the mixing ratio of biogenic volatile organic compounds (BVOCs) in a humid highland and dry lowland African ecosystem in Kenya. The mixing ratio of monoterpenoids was similar to that measured in the relevant ecosystems in western and southern Africa, while that of isoprene was lower. Modeling the emission factors (EFs) for BVOCs from the lowlands, the EFs for isoprene and β-pinene agreed well with what is assumed in the MEGAN, while those of α-pinene and limonene were higher.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Lutz Merbold, Charlotte Decock, Werner Eugster, Kathrin Fuchs, Benjamin Wolf, Nina Buchmann, and Lukas Hörtnagl
Biogeosciences, 18, 1481–1498, https://doi.org/10.5194/bg-18-1481-2021, https://doi.org/10.5194/bg-18-1481-2021, 2021
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Our study investigated the exchange of the three major greenhouse gases (GHGs) over a temperate grassland prior to and after restoration through tillage in central Switzerland. Our results show that irregular management events, such as tillage, have considerable effects on GHG emissions in the year of tillage while leading to enhanced carbon uptake and similar nitrogen losses via nitrous oxide in the years following tillage to those observed prior to tillage.
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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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