Monitoring Long-term Vegetation Phenology across Europe Using Satellite NDVI Time Series (PKU GIMMS)

Large-scale and long-term satellite observations are essential for environmental monitoring and for detecting gradual ecosystem responses to climate variability and land-use change.

This study presents a remote sensing–based framework to characterize vegetation phenology and its stability across Europe over four decades (1982–2022), using the temporally consistent and cross-sensor-calibrated PKU GIMMS NDVI dataset. The framework integrates NDVI time-series analysis with a newly developed Phenology Variability Index (PVI), designed to assess phenological stability at climatic scales and to complement established methods. Monthly NDVI time series are analyzed using non-parametric statistical tests and long-term mean seasonal profiles to delineate phenologically coherent regions through spatial clustering. Land Surface Phenology (LSP) metrics and the Phenology Variability Index are subsequently derived to characterize seasonal timing, trends, and phenological stability within and across regions. In this way, we integrate spatially explicit, pixel-level NDVI statistics and PVI-based evaluations with analyses of phenologically homogeneous clusters, providing a comprehensive understanding of vegetation dynamics across ecosystems.

Five spatially coherent clusters were identified, each characterized by distinct seasonal signatures linked to major European eco-climatic zones. Results reveal pronounced spatial and temporal heterogeneity, with consistent greening trends in temperate, montane, and Mediterranean regions, weaker and seasonally constrained greening in semi-arid areas, and largely stable winter NDVI conditions in mountainous forests and continental regions. LSP metrics indicate shifts in the timing and duration of the growing season, reflecting combined effects of climate variability and land-use change. The PVI further highlights higher phenological stability in Mediterranean and semi-arid landscapes, contrasted with greater variability in temperate and montane ecosystems.

Overall, this study demonstrates how long-term, high-temporal-resolution satellite data can support ecosystem assessment and environmental monitoring across continental scales. The proposed framework provides a transferable and robust methodological basis for analyzing vegetation dynamics, contributing to remote sensing–driven environmental monitoring and climate change research.

 

Keywords:
Remote sensing; Environmental monitoring; Vegetation dynamics; NDVI time series; Europe; Phenology Variability Index (PVI); Monthly trend analysis; Land Surface Phenology.