Assessing topographic effects on Vegetation Phenology from MODIS and Sentinel-2 in European Mountains

Mountain ecosystems present pronounced topographic complexity and spatial heterogeneity, posing persistent challenges for satellite-based phenological monitoring. Moderate-resolution products have been extensively applied, yet their ability to resolve fine-scale phenological patterns in mountainous terrain remains uncertain. Quantifying the benefits of emerging high-resolution phenology datasets is therefore essential for improving phenological characterization and climate impact assessments. In this study, we compare three vegetation phenology products, including a moderate-resolution MODIS product (MCD12Q2, 500 m), the Medium-Resolution Vegetation Phenology and Productivity product derived from MODIS (MR-VPP, 392 m), and the High-Resolution Vegetation Phenology and Productivity product from Sentinel-2 (HR-VPP, 10 m), to evaluate their performance in characterizing the start (SOS) and end (EOS) of the growing season across European mountains during 2017-2024. We found that the interannual phenological variability increases with elevation but differs among datasets, with stronger variation observed in MCD12Q2. Additionally, HR-VPP can clearly detect terrain-related phenological variations, whereas the coarser-resolution datasets are less effective at capturing vegetation seasonality in high-elevation areas. All three phenological products reveal delayed vegetation onset and advanced dormancy at higher elevations. We illustrated spatial differences between north- and south-facing aspects within each 0.1° × 0.1° grid, highlighting marked local variations. Although all datasets showed an earlier EOS on north-facing aspects, they differ in aspect-induced SOS differences. Our results provide clear evidence of the added value of high-resolution phenology products for accurately resolving terrain-induced phenological heterogeneity. Future research should further integrate high-resolution datasets with long-term observations to jointly leverage their complementary strengths in spatial detail and temporal continuity for improved mountain phenology monitoring.