Tree and leaf: merging multi-stream data, models and citizen science for phenological detection

Accurately quantifying phenological dynamics in vegetated systems is essential as the timing of seasonal plant activity drives water, nutrient, and carbon cycling, but it is also one of the processes most disrupted by environmental changes. Land Surface Models (LSMs) integrate phenology to represent these feedback loops between terrestrial ecosystem functioning and the global climate, but their performance relies on the type and quality of data used for evaluation. Traditional phenological datasets span from point observations (e.g. leaf-on, leaf-off dates), to high resolution ground measurements of greenness and productivity (e.g. GCC), to time series of remote sensed vegetation indexes (e.g. EVI). However, each observation type measures distinct ecosystem properties, and no single data source provides both the temporal and spatial coverage needed to fully represent phenology at regional and global scales. Here we integrate growing season metrics for 89 European temperate forests sites across scales, derived from eddy covariance measurements, phenocam time series, and MODIS remote sensed vegetation indexes. For the first time, we incorporate phenological dates derived from citizen science, drawing data from the Flora Incognita app, GBIF and iNaturalist, to calculate species-specific annual observation curves for 11 characteristic understory species spanning 2020-2024. By combining data streams, we evaluate the advantages and limitations of each for data-model integration, and further assess the potential of opportunistic species observations to scale up non-overlapping phenological data to ecosystems. By simulating the same sites with the QUINCY LSM we also investigate the role of process-based models to bridge between datasets, as the processes underlying growing season that they provide aid the interpretation of differences between observed phenological metrics. This work highlights the potential of integrating multi-scale phenological information, including underutilised contribution from citizen science, to improve our understanding of phenological dynamics. We additionally explore how LSMs can be leveraged together with data for ecological insight beyond evaluation, moving away from the traditional one-way relationship between data and models.