The PeatPic project: predicting plot-scale green leaf phenology across peatlands

Abstract Peatlands store approximately one-third of the world’s soil carbon (C), but their functioning is highly variable at fine spatial scales due to differences in vegetation cover and environmental conditions such as water table depth. This fine-scale heterogeneity plays a key role in carbon dynamics yet capturing it—particularly in relation to green leaf phenology (GLP)—is challenging with traditional remote sensing methods. To address this, we developed a smartphone-based methodology and community-science project called the PeatPic Project. We gathered over 3700 photographs from 27 sites across 10 countries in 2021 and 2022, representing different peatland types (bog, fen, and swamp), at 1–2 week intervals. We calculated GLP metrics, such as the data of the start of the season and end of the season, based on the red-blue-green bands from these photographs. We found that GLP metrics varied significantly across peatland types and dominant vegetation communities. Notably, peak greenness at bog sites occurring approximately 10 days later in the year compared to fen sites. Furthermore, variables relation to peatland/vegetation type and energy balance were key predictors of peatland GLP. The PeatPic Project’s readily available methodology offers low-cost opportunities for further research into peatland phenology, enabling the calculation of additional phenological indices and integration with other data types. By refining our understanding of peatland GLP, we can improve predictive C modelling and better assess the impacts of future changes on these important ecosystems.

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Publication Details

Journal
Environmental Research Letters
Published
2025-09-12
DOI
https://doi.org/10.1088/1748-9326/ae0658
Citations
2
Primary Topic
Peatlands and Wetlands Ecology
Type
article
Field-Weighted Citation Impact
3.43
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article

The PeatPic project: predicting plot-scale green leaf phenology across peatlands

Jorge F. Pérez‐Quezada, Helena Rautakoski, Vincent E. J. Jassey, Gustaf Granath et al.
2 citations
Environmental Research Letters
Peatlands and Wetlands Ecology
3.43
article

The PeatPic project: predicting plot-scale green leaf phenology across peatlands

Jorge F. Pérez‐Quezada, Helena Rautakoski, Vincent E. J. Jassey, Gustaf Granath, Melanie Bird, A. Hartmann, R. Anderson, Javier Lopatin, Elena Aitova, Mike Peacock, Emma Duley, Clarice R. Perryman, Ewen Silvester, Janna M. Barel, Aleicia Holland, Scott J. Davidson, Sung‐Ching Lee, Vytas Huth, Clarisse Brehier, Joel Eklof, Matthias Peichl, Alice M. Milner, Maria Strack, Anna‐Maria Virkkala, Gillian Donaldson-Selby, Lindsey Atkinson, Avni Malhotra, Elvira de Eyto, Emma Wegener, Cheristy P Jones
article en
2 citations

Abstract

Abstract Peatlands store approximately one-third of the world’s soil carbon (C), but their functioning is highly variable at fine spatial scales due to differences in vegetation cover and environmental conditions such as water table depth. This fine-scale heterogeneity plays a key role in carbon dynamics yet capturing it—particularly in relation to green leaf phenology (GLP)—is challenging with traditional remote sensing methods. To address this, we developed a smartphone-based methodology and community-science project called the PeatPic Project. We gathered over 3700 photographs from 27 sites across 10 countries in 2021 and 2022, representing different peatland types (bog, fen, and swamp), at 1–2 week intervals. We calculated GLP metrics, such as the data of the start of the season and end of the season, based on the red-blue-green bands from these photographs. We found that GLP metrics varied significantly across peatland types and dominant vegetation communities. Notably, peak greenness at bog sites occurring approximately 10 days later in the year compared to fen sites. Furthermore, variables relation to peatland/vegetation type and energy balance were key predictors of peatland GLP. The PeatPic Project’s readily available methodology offers low-cost opportunities for further research into peatland phenology, enabling the calculation of additional phenological indices and integration with other data types. By refining our understanding of peatland GLP, we can improve predictive C modelling and better assess the impacts of future changes on these important ecosystems.

Environmental Research LettersVol. 20(11)
Uppsala University (SE), Washburn University (US), Finnish Meteorological Institute (FI), Centre National de la Recherche Scientifique (FR), Woodwell Climate Research Center (US), University of Helsinki (FI), Roslin Institute (GB), Pacific Northwest National Laboratory (US), University of Puget Sound (US), University of Liverpool (GB), University of Waterloo (CA), James Hutton Institute (GB), University of New Hampshire (US), Université Fédérale de Toulouse Midi-Pyrénées (FR), Royal Holloway University of London (GB), Ollscoil na Gaillimhe – University of Galway (IE), University of Hull (GB), Utrecht University (NL), La Trobe University (AU), University of Washington (US), University of Zurich (CH), Adolfo Ibáñez University (CL), Universitätsmedizin Greifswald (DE), Swedish University of Agricultural Sciences (SE), Weihenstephan-Triesdorf University of Applied Sciences (DE), Universität Greifswald (DE), UK Centre for Ecology & Hydrology (GB), Institute of Ecology and Biodiversity (CL), Albury Wodonga Health (AU), Forest Research (GB), University of Washington Applied Physics Laboratory (US), Max Planck Institute for Biogeochemistry (DE), Marine Institute (IE), Institut de Recherche pour le Développement (FR), University of Chile (CL), University of Plymouth (GB), Stanford University (US)
Openalex Percentile: Top 8%
Peatlands and Wetlands Ecology
3.43
DOIOpen Access PDFOpenAlex
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