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Monitoring tree phenology is key to understanding forest dynamics under climate change. Events like leaf unfolding and senescence affect ecosystem productivity, tree mortality, and species interactions. While in situ phenology observations provide valuable ground information, they are typically restricted in spatial and temporal coverage and may be influenced by observer-related inconsistencies. Here, we derived species-specific phenology metrics from Sentinel-2 satellite data for Switzerland’s two dominant tree species: beech ( Fagus sylvatica ) and spruce ( Picea abies ). We extracted start (SOS), peak (POS), and end (EOS) of season metrics and compared them to in situ observations to study interannual, regional, and topographic variation. Sentinel-2-derived metrics differed significantly from the in situ observations for the SOS and EOS of Fagus sylvatica and the SOS of Picea abies . Sentinel-2 metrics indicated a shorter growing season – later SOS (5 days for Fagus sylvatica ; 3 days for Picea abies ) and earlier EOS (13 days for Fagus sylvatica ). Despite these offsets, satellite data captured similar annual and regional trends. POS closely tracked SOS trends, but offered more reliable sampling opportunities due to more stable vegetation conditions and typically lower cloud cover during summer. Satellite-derived EOS may reflect stress responses missed by ground observations. Elevation trends also differed, with in situ data showing steeper slopes of the SOS-elevation relationships. Limitations of satellite data remained in mountainous regions due to topography and cloud cover, limiting sampling sizes. Overall, satellite remote sensing can complement in situ observations by facilitating observations across large geographic and temporal domains. In contrast, in situ observations provide long-term historical data unaffected by atmospheric conditions or possible technical issues of satellites. • Phenology metrics for beech and spruce derived from satellite remote sensing. • Comprehensive comparison of in situ and Sentinel-2 species phenology metrics. • Analyzing interannual and regional patterns of phenology between data sources. • Assessing the influence of elevation and topographic wetness on phenology metrics. • Discussing how satellite remote sensing can support conventional phenology monitoring.
Koch et al. (Sat,) studied this question.