The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity

Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

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Journal
Earth system science data
Published
2026-01-09
DOI
https://doi.org/10.5194/essd-18-245-2026
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity

Kaiyu Guan, Adam R. Martin, Isabelle Maréchaux, Maquelle Neves Garcia et al.
Earth system science data
Remote Sensing in Agriculture
article

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity

Kaiyu Guan, Adam R. Martin, Isabelle Maréchaux, Maquelle Neves Garcia, Xiaojin Qian, To‐Chia Ting, Loren P. Albert, Iolanda Filella, Etsushi Kumagai, Michael Alonzo, Owen K. Atkin, Katherine Meacham‐Hensold, Alexander W. Cheesman, Robert T. Furbank, Thu Ya Kyaw, Shengbiao Wu, Joshua S. Caplan, Dedi Yang, Leeladarshini Sujeeun, Andreas P.M. Weber, Diane Wang, John R. Evans, Christopher M. Montes, Joan Llusià, Claire Fortunel, Ilona Clocher, Onoriode Coast, Troy S. Magney, Kenneth Davidson, Heidi J. Renninger, Courtney M. Siegert, Fengqi Wu, Jeremiah Anderson, Charles D. Southwick, Carl J. Bernacchi, Giacomo Sellan, Marney E. Isaac, David Heckmann, Elizabeth A. Ainsworth, Bruno Gimenez, Angela C. Burnett, Juan Manuel Posada, Guangqin Song, Josep Penuelas, Jean-Baptiste Féret, Marcelo Vilas-Boas, Clément Dauvissat, Andrés Rojas-González, Regison C Oliveira, Mallory L. Barnes, Zhengfei Guo, Lingli Liu, Christopher Y. S. Wong, Alain Paquette, Yanjun Su, Sheng Wang, Joy Ojo, Romà Ogaya, Christopher Doughty, Jin Wu, Viridiana Silva-Perez, Boya Cui, Jérôme Chave, Shuwen Liu, Shan Kothari, Jing Wang, Alistair Rogers, Yingyi Zhao, Kelvin T. Acebron, Nicolas Barbier, Zhengbing Yan, Daisy C. Souza, Patrick Heuret, Peng Fu, Kim S. Ely, Vicente Vasquez, Shawn P. Serbin, Milagros Rodriguez-Caton, Xin Wang, Han Wang, Ninon Besson, Urte Schlüter, Holly Croft, Antonia Debora Placido, Amrutha Vijayakumar, Liangyun Liu
article en

Abstract

Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

Earth system science dataVol. 18(1)
University of New Brunswick (CA), Australian National University (AU), Louisiana State University (US), Universitat Autònoma de Barcelona (ES), Oak Ridge National Laboratory (US), Centre National de la Recherche Scientifique (FR), European Medicines Agency (NL), Goddard Space Flight Center (US), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Oregon State University (US), Louisiana State University Agricultural Center (US), Université Toulouse III - Paul Sabatier (FR), University of Wisconsin–Madison (US), Lawrence Berkeley National Laboratory (US), University of Alberta (CA), University of Illinois Urbana-Champaign (US), Sun Yat-sen University (CN), Université du Québec à Montréal (CA), Chinese University of Hong Kong (HK), Université Fédérale de Toulouse Midi-Pyrénées (FR), American University (US), Urbana University (US), University of Toronto (CA), Instituto Nacional de Pesquisas da Amazônia (BR), Chinese Academy of Sciences (CN), Universidad Nacional (CR), Université de Montpellier (FR), Northern Arizona University (US), Aarhus University (DK), Purdue University West Lafayette (US), AgroParisTech (FR), Institut Agro Montpellier (FR), Scarborough Health Network (CA), Beijing Botanical Garden (CN), Temple University Ambler (US), Society of American Foresters (US), University of Florida (US), Nanjing University of Posts and Telecommunications (CN), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), ForestGEO (US), ARC Centre of Excellence in Plant Energy Biology (AU), National Institute for Agro-Environmental Sciences (JP), Indiana University Bloomington (US), Hong Kong Biotechnology Organization (CN), Centre for Sustainable Energy (GB), UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations (FR), Cluster of Excellence on Plant Sciences (DE), Aerospace Information Research Institute (CN), Plant (United States) (US), Centro Científico Tecnológico - Mendoza (AR), Department of Forestry (MW), Institute of Botany (CN), University of Chinese Academy of Sciences (CN), Institut de Recherche pour le Développement (FR), Territoires, Environnement, Télédétection et Information Spatiale (FR), China National Botanical Garden, Heinrich Heine University Düsseldorf (DE), Smithsonian Environmental Research Center (US), Bayer (Germany) (DE), University of California, Davis (US), James Cook University (AU), University of Hong Kong (HK), University of New England (AU), Universidad del Rosario (CO), University of Sheffield (GB), University of California, Berkeley (US), Mississippi State University (US), Tsinghua University (CN)
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Remote Sensing in Agriculture
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