Vangi et al. (2026) Investigating climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products

Identification

• Journal: bioRxiv (Cold Spring Harbor Laboratory)
• Year: 2026
• Date: 2026-02-24
• Authors: Elia Vangi, Giovanni D'Amico, Vincenzo Saponaro, Mattia Niccoli, Gioele Tiberi, Saverio Francini, Costanza Borghi, Alessio Collalti, FRANCESCO PARISI
• DOI: 10.64898/2026.02.23.707431

Research Groups

• geoLAB - Laboratory of Forest Geomatics, Department of Agriculture, Food, Environment and Forestry, Università degli Studi di Firenze
• Forest Modelling Laboratory, Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM)
• Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano
• Department of Architecture (DIDA), University of Florence
• Department of Science and Technology of Agriculture and Environment (DISTAL), University of Bologna
• Department of Biosciences and Territory, University of Molise
• National Biodiversity Future Center (NBFC)
• Fondazione PerIl Futuro Delle Citta

Short Summary

This study analyzes the relationship between climate and phenology in common Italian forest species using high-resolution Sentinel-2 data and machine learning to determine how climatic drivers influence the timing and productivity of the growing season.

Objective

• To investigate how climatic and site-specific drivers regulate the start, end, and length of the growing season and how these phenological shifts impact total seasonal productivity across Mediterranean, temperate, and mountain forest environments in Italy.

Study Configuration

• Spatial Scale: National (Italy), covering Mediterranean, temperate, and mountain environments.
• Temporal Scale: Seasonal/Annual phenological cycles.

Methodology and Data

• Models used: Random Forests (RF) for modeling and SHAP (SHapley Additive exPlanations) for explainable artificial intelligence (XAI) analysis.
• Data sources: High-Resolution Vegetation Productivity and Phenology product from the Copernicus Land Monitoring Service (derived from Sentinel-2 satellite data).

Main Results

• Growing Season Length: General lengthening of the growing season, primarily driven by spring temperatures and chilling accumulation.
• Season Start: Warmer conditions advanced the start of the growing season by 1–10 days across species.
• Season Extension: The combined influence of moisture, radiation, and temperature can extend the growing season by 20–30 days.
• Season End: In Mediterranean species, the end of the season can be advanced by up to 40 days due to high vapor pressure deficit (VPD), site exposure, and summer drought.
• Species-Specific Responses: Mediterranean species exhibit compensatory shifts (balancing onset and senescence), while mountain species show a coupling between delayed onset and shorter overall season length.
• Productivity Decoupling: Phenological shifts are frequently decoupled from productivity, as the latter is governed more by water and energy availability than by timing alone.

Contributions

• Demonstrates the efficacy of integrating high-resolution remote sensing data (Sentinel-2) with machine learning and XAI to analyze forest phenology and productivity across diverse climatic gradients.
• Provides quantitative evidence of the differential responses of Mediterranean versus mountain forest species to climate change.

Funding

• European Commission (Project code: 101184989)
• Ministry of Education, Universities and Research (Project codes: I53D24000060005, CN_00000033)

Citation

@article{Vangi2026Investigating,
  author = {Vangi, Elia and D'Amico, Giovanni and Saponaro, Vincenzo and Niccoli, Mattia and Tiberi, Gioele and Francini, Saverio and Borghi, Costanza and Collalti, Alessio and PARISI, FRANCESCO and Chirici, Gherardo},
  title = {Investigating climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products},
  journal = {bioRxiv (Cold Spring Harbor Laboratory)},
  year = {2026},
  doi = {10.64898/2026.02.23.707431},
  url = {https://doi.org/10.64898/2026.02.23.707431}
}