Four decades of vegetation phenology across Europe using PKU GIMMS NDVI: assessing timing, stability and spatial patterns

Large-scale, long-term analyses of vegetation dynamics provide essential insights into ecosystem functioning and reveal early evidence of environmental change. This study investigates phenological variability and monthly trends across Europe from 1982 to 2022 using the high-accuracy PKU GIMMS NDVI dataset, which offers improved temporal consistency and calibration. We present a novel framework integrating established analytical methods with a newly developed Phenology Variability Index (PVI), designed to assess phenological stability at climatic scales. The framework combines spatially explicit pixel-level analyses, including interdecadal NDVI statistics and PVI evaluations, with clustering methods to identify phenologically homogeneous regions, quantify their variability, and enable inter-cluster comparisons. Following preprocessing and quality control, monthly NDVI series were analysed using non-parametric statistical tests, K-means clustering, Land Surface Phenology (LSP) metrics, and monthly trend estimation. Five spatially coherent clusters were identified, displaying distinct seasonal signatures across ecological zones. Results reveal spatially heterogeneous trends, including consistent greening in temperate, montane, and Mediterranean regions, weaker seasonal greening in semi-arid areas, and largely stable winter NDVI in mountainous forests and continental areas. LSP metrics indicate shifts in the timing and duration of growing seasons, consistent with climate- and land use- driven phenological change. The PVI further highlights higher phenological stability in Mediterranean landscapes and semi-arid regions and greater variability in montane forests and temperate zones. This integrated approach enhances understanding of vegetation responses to environmental variability across scales and provides a robust methodological basis for long-term ecosystem monitoring, supporting both applied geoinformation analyses and broader ecological research.