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: Agricultural and Forest Meteorology
• Year: 2026
• Date: 2026-09-29
• Authors: Elia Vangi, Giovanni D’Amico, Vincenzo Saponaro, Mattia Niccoli, Gioele Tiberi, Saverio Francini, Costanza Borghi, Alessio Collalti, Francesco Parisi, Gherardo Chirici
• DOI: 10.1016/j.agrformet.2026.111496

Research Groups

• geoLAB - Laboratory of Forest Geomatics, Dept. of Agriculture, Food, Environment and Forestry, Universit`a degli Studi di Firenze, Via San Bonaventura 13, 50145, Firenze, Italy
• Forest Modelling Laboratory, Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Via Madonna Alta 128, 06128, Perugia, Italy
• Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Piazza Universit`a/Universit¨atsplatz, Bolzano, 39100, Italy
• Department of Architecture (DIDA), University of Florence, Via della Mattonaia 8, 50121, Florence, Italy
• Department of Science and Technology of Agriculture and Environment (DISTAL), University of Bologna, 40126, Bologna, Italy
• Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, 86090, Pesche, Is, Italy
• NBFC, National Biodiversity Future Center, Palermo, 90133, Italy

Short Summary

This study investigates climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products. The results indicate a general tendency toward lengthening of the growing season driven mainly by chilling accumulation and spring temperatures.

Objective

• Investigate the relationship between climate variables and phenological phases in Italian forest species.
• Examine how changes in temperature, precipitation, and other environmental factors affect the timing and duration of the growing season.

Study Configuration

• Spatial Scale: National scale, covering >300,000 km² of Italy's territory.
• Temporal Scale: 2017-2023 period, with a focus on annual climate predictors.

Methodology and Data

• Models used:
  • Conditional Random Forest (RF) regression model for predictive modeling.
  • SHAP analysis for explainable AI and feature contribution estimation.
• Data sources:
  • Sentinel-2-derived vegetation phenology and productivity products (HRVPP).
  • Italian National Forest Inventory (NFI) data.
  • ERA5 reanalysis for climate variables.

Main Results

• The study found a general tendency toward lengthening of the growing season driven mainly by chilling accumulation and spring temperatures.
• Warmer conditions advance the start of the season by 1–10 days across species, while the combined effects of temperature, radiation, and moisture can extend the growing season by up to 20–30 days.
• End-of-season dynamics and season length are more strongly controlled by light and water availability than by temperature alone.

Contributions

• This study provides a comprehensive analysis of climate-phenology relationships in Italian forest species using high-resolution remote sensing data and machine learning techniques.
• The results highlight the importance of considering multiple environmental factors when predicting phenological responses to climate change.

Funding

• Not specified.

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 = {Agricultural and Forest Meteorology},
  year = {2026},
  doi = {10.1016/j.agrformet.2026.111496},
  url = {https://doi.org/10.1016/j.agrformet.2026.111496}
}