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

21 May 2025 · 10.5194/essd-2025-213

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 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repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi: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.

Plant phenotyping relevance

葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。

abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
abstractBy 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.

Code and data availability

The paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.

Datasetpublic

ts of the GSTI will focus on expanding data coverage, incorporating data from under- represented biomes and plant functional types. 6. Data and code availability 495 The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). 7. How to contribute to future versions of the GSTI We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this pa

Open resource ↗ESS-DIVE · doi:10.15485/2530733 · pdf-raw-page:22 lines:1-36
Codepublic

going refinement of spectra-trait models as new datasets are incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under- represented biomes and plant functional types. 6. Data and code availability 495 The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). 7. How to contribute to future versions of the GSTI We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure consistency and mai

Open resource ↗GitHub · pdf-raw-page:22 lines:1-36

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