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.