Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-245-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-245-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
09 Jan 2026
Data description article |
| 09 Jan 2026
| 09 Jan 2026
The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity
Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3), Toulouse, France
Shawn P. Serbin
Biospheric Sciences Laboratory (BSL), Code 618 NASA Goddard Space Flight Center 8800 Greenbelt Road Greenbelt, MD 20771 USA
Alistair Rogers
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA
Kelvin T. Acebron
Smithsonian Environmental Research Center, Edgewater MD 21403, USA
Elizabeth Ainsworth
University of Illinois Urbana, Champaign, Urbana, IL 61801, USA
Loren P. Albert
Oregon State University Department of Forest Ecosystems & Society, Corvallis, OR 97333, USA
Michael Alonzo
Department of Environmental Science, American University, Washington, DC 20016, USA
Jeremiah Anderson
Biospheric Sciences Laboratory (BSL), Code 618 NASA Goddard Space Flight Center 8800 Greenbelt Road Greenbelt, MD 20771 USA
Owen K. Atkin
ARC Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
Nicolas Barbier
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
Mallory L. Barnes
The O'Neill School of Public & Environmental Affairs, Indiana University Bloomington, Bloomington, IN, USA
Carl J. Bernacchi
USDA-ARS Photosynthesis Research Unit, Urbana, IL 61801, USA
University of Illinois Urbana, Champaign, Urbana, IL 61801, USA
Ninon Besson
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
UMR EcoFoG, AgroParisTech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane, Kourou, France
Angela C. Burnett
Advanced Research + Invention Agency, London, UK
Joshua S. Caplan
Department of Architecture & Environmental Design, Temple University, Ambler PA 19010, USA
Jérôme Chave
Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3), Toulouse, France
Alexander W. Cheesman
College of Science & Engineering, James Cook University, Cairns, Queensland 4878, Australia
Ilona Clocher
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
UMR EcoFoG, AgroParisTech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane, Kourou, France
Onoriode Coast
School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
Sabrina Coste
UMR EcoFoG, AgroParisTech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane, Kourou, France
Holly Croft
Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, South Yorkshire, UK
School of Biosciences, Institute for Sustainable Food, University of Sheffield, South Yorkshire, UK
Boya Cui
Department of Physical and Environmental Sciences, University of Toronto Scarborough, M1C 1A4, Toronto, Canada
Clément Dauvissat
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
Kenneth J. Davidson
American Forests, Washington, DC 20005, USA
Christopher Doughty
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Kim S. Ely
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA
John R. Evans
ARC Centre of Excellence for Translational Photosynthesis, Research school of Biology, Australian National University, Canberra, ACT 2601, Australia
Jean-Baptiste Féret
TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Université Montpellier, Montpellier, France
Iolanda Filella
Center for Ecological Research and Forestry Applications (CREAF) – National Research Council (CSIC), Edifici C, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
Claire Fortunel
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
Peng Fu
School of Plant, Environmental and Soil Sciences, Louisiana State University and Louisiana State University AgCenter, Baton Rouge, LA 70803, USA
Robert T. Furbank
ARC Centre of Excellence for Translational Photosynthesis, Research school of Biology, Australian National University, Canberra, ACT 2601, Australia
Maquelle Garcia
Oregon State University Department of Forest Ecosystems & Society, Corvallis, OR 97333, USA
Bruno O. Gimenez
Department of Geography, University of California – Berkeley (UCB), 507 McCone Hall #4740, Berkeley, CA 94720, USA
Forest Management Laboratory, National Institute of Amazonian Research (INPA), Av. Andre Araújo, 69060-082, Manaus-AM, Brazil
Kaiyu Guan
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Department of Nature Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Zhengfei Guo
School of Biological Sciences, University of Hong Kong, Pokfulam Road, Hong Kong, China
David Heckmann
Bayer Crop Science, 40789 Monheim am Rhein, Germany
Patrick Heuret
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
Marney Isaac
Department of Physical and Environmental Sciences, University of Toronto Scarborough, M1C 1A4, Toronto, Canada
Shan Kothari
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2E3, Canada
Etsushi Kumagai
Institute for Agro-Environmental Sciences, NARO, Tsukuba, Ibaraki 305-8604, Japan
Thu Ya Kyaw
Department of Environmental Science, American University, Washington, DC 20016, USA
Liangyun Liu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Lingli Liu
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China
Shuwen Liu
Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, USA
School of Biological Sciences, University of Hong Kong, Pokfulam Road, Hong Kong, China
Joan Llusià
Center for Ecological Research and Forestry Applications (CREAF) – National Research Council (CSIC), Edifici C, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
Troy Magney
Department of Plant Sciences, University of California, Davis, California, USA
Isabelle Maréchaux
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
Adam R. Martin
Department of Physical and Environmental Sciences, University of Toronto Scarborough, M1C 1A4, Toronto, Canada
Katherine Meacham-Hensold
Carl R Woese Institute for Genomic Biology, University of Illinois Urbana Champaign, Urbana, Illinois, USA
Christopher M. Montes
USDA-ARS Photosynthesis Research Unit, Urbana, IL 61801, USA
University of Illinois Urbana, Champaign, Urbana, IL 61801, USA
Romà Ogaya
Center for Ecological Research and Forestry Applications (CREAF) – National Research Council (CSIC), Edifici C, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
Joy Ojo
School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
Regison Oliveira
Forest Management Laboratory, National Institute of Amazonian Research (INPA), Av. Andre Araújo, 69060-082, Manaus-AM, Brazil
Alain Paquette
Centre for Forest Research, Université du Québec à Montréal, Montréal, Canada
Josep Peñuelas
Center for Ecological Research and Forestry Applications (CREAF) – National Research Council (CSIC), Edifici C, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
Antonia Debora Placido
Forest Management Laboratory, National Institute of Amazonian Research (INPA), Av. Andre Araújo, 69060-082, Manaus-AM, Brazil
Juan M. Posada
Biology Department, Carrera 24 # 63C – 69, Universidad del Rosario, Bogotá, Colombia
Xiaojin Qian
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Heidi J. Renninger
Department of Forestry, Mississippi State University, Mississippi State, MS 39762, USA
Milagros Rodriguez-Caton
Institute for Snow, Glaciers and Environmental Research, IANIGLA-CONICET, Mendoza, Argentina
Department of Plant Sciences, University of California, Davis, California, USA
Andrés Rojas-González
Laboratorio de Ecología Funcional y Ecosistemas Tropicales (LEFET), Escuela de Ciencias Biológicas, Facultad de Ciencias Exactas y Naturales, Universidad Nacional, Heredia, Costa Rica
Urte Schlüter
Institute for Plant Biochemistry, Heinrich Heine University Düsseldorf, Germany
Giacomo Sellan
UMR EcoFoG, AgroParisTech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane, Kourou, France
Courtney M. Siegert
Department of Forestry, Mississippi State University, Mississippi State, MS 39762, USA
Viridiana Silva-Perez
ARC Centre of Excellence for Translational Photosynthesis, Research school of Biology, Australian National University, Canberra, ACT 2601, Australia
Guangqin Song
School of Biological Sciences, University of Hong Kong, Pokfulam Road, Hong Kong, China
Charles D. Southwick
Oregon State University Department of Forest Ecosystems & Society, Corvallis, OR 97333, USA
Daisy C. Souza
Forest Management Laboratory, National Institute of Amazonian Research (INPA), Av. Andre Araújo, 69060-082, Manaus-AM, Brazil
Clément Stahl
UMR EcoFoG, AgroParisTech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane, Kourou, France
Yanjun Su
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China
Leeladarshini Sujeeun
Department of Physical and Environmental Sciences, University of Toronto Scarborough, M1C 1A4, Toronto, Canada
To-Chia Ting
Agronomy Department, Purdue University, West Lafayette, IN, USA
Vicente Vasquez
University of Florida, School of Forestry, Fisheries and Geomatics, Gainesville, Florida, USA
Amrutha Vijayakumar
School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
Marcelo Vilas-Boas
Forest Management Laboratory, National Institute of Amazonian Research (INPA), Av. Andre Araújo, 69060-082, Manaus-AM, Brazil
Diane R. Wang
Agronomy Department, Purdue University, West Lafayette, IN, USA
Sheng Wang
Department of Agroecology, Aarhus University, Aarhus 8000, Denmark
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Han Wang
Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Jing Wang
School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
Xin Wang
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China
Andreas P. M. Weber
Institute for Plant Biochemistry, Heinrich Heine University Düsseldorf, Germany
Cluster of Excellence on Plant Sciences (CEPLAS), Düsseldorf, Germany
Christopher Y. S. Wong
Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton NB E3B 5A3, Canada
Jin Wu
School of Biological Sciences, University of Hong Kong, Pokfulam Road, Hong Kong, China
Institute for Climate and Carbon Neutrality, University of Hong Kong Kong, Pofulam Road, Hong Kong, China
State Key Laboratory of Agrobiotechnology, Chinese University of Hong Kong, Sha Tin, Hong Kong, China
Fengqi Wu
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China
Shengbiao Wu
Future Urbanity and Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong, China
Zhengbing Yan
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China
China National Botanical Garden, Beijing 100093, China
University of Chinese Academy of Sciences, Yuquanlu, Beijing 100049, China
Dedi Yang
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Yingyi Zhao
School of Biological Sciences, University of Hong Kong, Pokfulam Road, Hong Kong, China
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Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data, 17, 4039–4062, https://doi.org/10.5194/essd-17-4039-2025, https://doi.org/10.5194/essd-17-4039-2025, 2025
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This work describes a novel global 10 m land-cover dataset with a fine classification system, which contains 30 land-cover subcategories and achieves sufficient performance on a global scale.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
Atmos. Meas. Tech., 18, 3647–3667, https://doi.org/10.5194/amt-18-3647-2025, https://doi.org/10.5194/amt-18-3647-2025, 2025
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TanSat-2 is designed for global carbon monitoring, offering high-resolution dual-band observations of solar-induced chlorophyll fluorescence – a key indicator of photosynthesis. Simulations show its data processing can retrieve fluorescence with high accuracy. These results suggest TanSat-2 will enhance global tracking of the carbon cycle and vegetation health, providing valuable insights for climate change research.
Rubaya Pervin, Scott Robeson, Mallory Barnes, Stephen Sitch, Anthony Walker, Ben Poulter, Fabienne Maignan, Qing Sun, Thomas Colligan, Sönke Zaehle, Kashif Mahmud, Peter Anthoni, Almut Arneth, Vivek Arora, Vladislav Bastrikov, Liam Bogucki, Bertrand Decharme, Christine Delire, Stefanie Falk, Akihiko Ito, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Michael O’Sullivan, Wenping Yuan, and Natasha MacBean
EGUsphere, https://doi.org/10.5194/egusphere-2025-2841, https://doi.org/10.5194/egusphere-2025-2841, 2025
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Drylands contribute more than a third of the global vegetation productivity. Yet, these regions are not well represented in global vegetation models. Here, we tested how well 15 global models capture annual changes in dryland vegetation productivity. Models that didn’t have vegetation change over time or fire have lower variability in vegetation productivity. Models need better representation of grass cover types and their coverage. Our work highlights where and how these models need to improve.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
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Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
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The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Tiangang Yuan, Tzung-May Fu, Aoxing Zhang, David H. Y. Yung, Jin Wu, Sien Li, and Amos P. K. Tai
Atmos. Chem. Phys., 25, 4211–4232, https://doi.org/10.5194/acp-25-4211-2025, https://doi.org/10.5194/acp-25-4211-2025, 2025
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This study utilizes a regional climate–air quality coupled model to first investigate the complex interaction between irrigation, climate and air quality in China. We found that large-scale irrigation practices reduce summertime surface ozone while raising secondary inorganic aerosol concentration via complicated physical and chemical processes. Our results emphasize the importance of making a tradeoff between air pollution controls and sustainable agricultural development.
Tea Thum, Tuuli Miinalainen, Outi Seppälä, Holly Croft, Cheryl Rogers, Ralf Staebler, Silvia Caldararu, and Sönke Zaehle
Biogeosciences, 22, 1781–1807, https://doi.org/10.5194/bg-22-1781-2025, https://doi.org/10.5194/bg-22-1781-2025, 2025
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Climate change has the potential to influence the carbon sequestration potential of terrestrial ecosystems, and here the nitrogen cycle is also important. We used the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) in a mixed deciduous forest in Canada. We investigated the usefulness of using the leaf area index and leaf chlorophyll content to improve the parameterization of the model. This work paves the way for using spaceborne observations in model parameterizations, also including information on the nitrogen cycle.
Sarah Camelo da Silva, Bárbara Bomfim, Jeffrey Quintin Chambers, Regison Costa de Oliveira, Cacilda Adélia Sampaio de Souza, Marcelo Nunes Vilas-Boas, Adriano José Nogueira Lima, Niro Higuchi, and Bruno Oliva Gimenez
EGUsphere, https://doi.org/10.5194/egusphere-2025-391, https://doi.org/10.5194/egusphere-2025-391, 2025
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Tropical forest soils are known for low fertility, but support vegetation with high species diversity and biomass. Nutrients and carbon essential for forest functioning are stored in the soil and biomass. The region’s topographic gradient, with variations in soil texture and water table depth, influences species distribution. This study quantified macronutrients and carbon in trunks, leaves, and soil of generalist and specialist species across different soil types.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
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To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Liangyun Liu and Xiao Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 137–143, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, 2024
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
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
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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
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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.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
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This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
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We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Matthew P. Dannenberg, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Susan K. Meerdink, Xian Wang, Russell L. Scott, and Joel A. Biederman
Biogeosciences, 20, 383–404, https://doi.org/10.5194/bg-20-383-2023, https://doi.org/10.5194/bg-20-383-2023, 2023
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Earth's drylands provide ecosystem services to many people and will likely be strongly affected by climate change, but it is quite challenging to monitor the productivity and water use of dryland plants with satellites. We developed and tested an approach for estimating dryland vegetation activity using machine learning to combine information from multiple satellite sensors. Our approach excelled at estimating photosynthesis and water use largely due to the inclusion of satellite soil moisture.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
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An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
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Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Niel Verbrigghe, Niki I. W. Leblans, Bjarni D. Sigurdsson, Sara Vicca, Chao Fang, Lucia Fuchslueger, Jennifer L. Soong, James T. Weedon, Christopher Poeplau, Cristina Ariza-Carricondo, Michael Bahn, Bertrand Guenet, Per Gundersen, Gunnhildur E. Gunnarsdóttir, Thomas Kätterer, Zhanfeng Liu, Marja Maljanen, Sara Marañón-Jiménez, Kathiravan Meeran, Edda S. Oddsdóttir, Ivika Ostonen, Josep Peñuelas, Andreas Richter, Jordi Sardans, Páll Sigurðsson, Margaret S. Torn, Peter M. Van Bodegom, Erik Verbruggen, Tom W. N. Walker, Håkan Wallander, and Ivan A. Janssens
Biogeosciences, 19, 3381–3393, https://doi.org/10.5194/bg-19-3381-2022, https://doi.org/10.5194/bg-19-3381-2022, 2022
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In subarctic grassland on a geothermal warming gradient, we found large reductions in topsoil carbon stocks, with carbon stocks linearly declining with warming intensity. Most importantly, however, we observed that soil carbon stocks stabilised within 5 years of warming and remained unaffected by warming thereafter, even after > 50 years of warming. Moreover, in contrast to the large topsoil carbon losses, subsoil carbon stocks remained unaffected after > 50 years of soil warming.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci., 26, 3517–3536, https://doi.org/10.5194/hess-26-3517-2022, https://doi.org/10.5194/hess-26-3517-2022, 2022
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A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. Lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for water preservation and restoration.
Yuanyuan Luo, Olga Garmash, Haiyan Li, Frans Graeffe, Arnaud P. Praplan, Anssi Liikanen, Yanjun Zhang, Melissa Meder, Otso Peräkylä, Josep Peñuelas, Ana María Yáñez-Serrano, and Mikael Ehn
Atmos. Chem. Phys., 22, 5619–5637, https://doi.org/10.5194/acp-22-5619-2022, https://doi.org/10.5194/acp-22-5619-2022, 2022
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Diterpenes were only recently observed in the atmosphere, and little is known of their atmospheric fates. We explored the ozonolysis of the diterpene kaurene in a chamber, and we characterized the oxidation products for the first time using chemical ionization mass spectrometry. Our findings highlight similarities and differences between diterpenes and smaller terpenes during their atmospheric oxidation.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
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Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Mathilda Hancock, Stephen Sitch, Fabian Jörg Fischer, Jérôme Chave, Michael O'Sullivan, Dominic Fawcett, and Lina María Mercado
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-87, https://doi.org/10.5194/bg-2022-87, 2022
Publication in BG not foreseen
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Global vegetation models often underestimate the spatial variability of carbon stored in the Amazon forest. This paper demonstrates that including spatially varying tree mortality rates, as opposed to a homogeneous rate, in one model, significantly improves its simulations of the forest carbon store. To overcome the limited resolution of tree mortality data, this research presents a simple method of calculating mortality rates across Amazonia using a dependence on wood density.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
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By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Lore T. Verryckt, Sara Vicca, Leandro Van Langenhove, Clément Stahl, Dolores Asensio, Ifigenia Urbina, Romà Ogaya, Joan Llusià, Oriol Grau, Guille Peguero, Albert Gargallo-Garriga, Elodie A. Courtois, Olga Margalef, Miguel Portillo-Estrada, Philippe Ciais, Michael Obersteiner, Lucia Fuchslueger, Laynara F. Lugli, Pere-Roc Fernandez-Garberí, Helena Vallicrosa, Melanie Verlinden, Christian Ranits, Pieter Vermeir, Sabrina Coste, Erik Verbruggen, Laëtitia Bréchet, Jordi Sardans, Jérôme Chave, Josep Peñuelas, and Ivan A. Janssens
Earth Syst. Sci. Data, 14, 5–18, https://doi.org/10.5194/essd-14-5-2022, https://doi.org/10.5194/essd-14-5-2022, 2022
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We provide a comprehensive dataset of vertical profiles of photosynthesis and important leaf traits, including leaf N and P concentrations, from two 3-year, large-scale nutrient addition experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N and P, and other leaf nutrients, in photosynthesis in tropical forests.
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
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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.
Bharat Rastogi, John B. Miller, Micheal Trudeau, Arlyn E. Andrews, Lei Hu, Marikate Mountain, Thomas Nehrkorn, Bianca Baier, Kathryn McKain, John Mund, Kaiyu Guan, and Caroline B. Alden
Atmos. Chem. Phys., 21, 14385–14401, https://doi.org/10.5194/acp-21-14385-2021, https://doi.org/10.5194/acp-21-14385-2021, 2021
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Predicting Earth's climate is difficult, partly due to uncertainty in forecasting how much CO2 can be removed by oceans and plants, because we cannot measure these exchanges directly on large scales. Satellites such as NASA's OCO-2 can provide part of the needed information, but data need to be highly precise and accurate. We evaluate these data and find small biases in certain months that are similar to the signals of interest. We argue that continued improvement of these data is necessary.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
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Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
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Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Attilio Naccarato, Antonella Tassone, Maria Martino, Sacha Moretti, Antonella Macagnano, Emiliano Zampetti, Paolo Papa, Joshua Avossa, Nicola Pirrone, Michelle Nerentorp, John Munthe, Ingvar Wängberg, Geoff W. Stupple, Carl P. J. Mitchell, Adam R. Martin, Alexandra Steffen, Diana Babi, Eric M. Prestbo, Francesca Sprovieri, and Frank Wania
Atmos. Meas. Tech., 14, 3657–3672, https://doi.org/10.5194/amt-14-3657-2021, https://doi.org/10.5194/amt-14-3657-2021, 2021
Short summary
Short summary
Mercury monitoring in support of the Minamata Convention requires effective and reliable analytical tools. Passive sampling is a promising approach for creating a sustainable long-term network for atmospheric mercury with improved spatial resolution and global coverage. In this study the analytical performance of three passive air samplers (CNR-PAS, IVL-PAS, and MerPAS) was assessed over extended deployment periods and the accuracy of concentrations was judged by comparison with active sampling.
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Short summary
We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf...
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