Integrating in-situ data and remote sensing for spatiotemporal assessment of alpine vegetation

Alpine plant communities are highly sensitive to environmental change, making effective monitoring essential to guide conservation in mountain environments where soil properties and topographic heterogeneity strongly constrain vegetation patterns. This study evaluates the potential of remote sensing indicators to capture spatial variation in alpine vegetation driven by soil and topography, as well as short-term temporal dynamics of key soil properties measured in situ. Alpine vegetation was surveyed at 40 sites distributed across four mountain massifs in the southwestern Cantabrian Mountains (Spain), recording plant community composition, soil properties and spatial structure linked to topographic variation. In addition, soil temperature and water potential were monitored over complete annual cycles from 2021 to 2025 in four representative sampling plots. Spatial and temporal field observations were coupled with co-temporal Sentinel-2 time series to derive indicators related to surface temperature, moisture and primary production. Distance-based redundancy analyses and generalized linear mixed models were used to assess the role of remote sensing indicators in explaining vegetation composition and temporal trends in soil conditions. Alpine plant communities were primarily structured by soil properties associated with water-holding capacity, together with spatial structure reflecting fine-scale topographic heterogeneity. Among remote sensing indicators, only the Soil-Adjusted Vegetation Index (SAVI) was significantly associated with vegetation composition, highlighting its potential as a proxy for productivity in topographically complex alpine landscapes. In contrast, all remote sensing variables proved effective in capturing short-term dynamics of soil temperature and water stress, particularly during climatic extremes, although their sensitivity varied with spatial scale. Our results demonstrate that integrating in-situ vegetation data with remote sensing provides a robust and scalable framework for assessing alpine ecosystems across space and time. While satellite-derived indicators can successfully capture topography-mediated compositional gradients and functional responses related to water and temperature, careful scale selection and continuous calibration between field and remote sensing data are essential to avoid misinterpretation in ecologically heterogeneous and complex terrains. This integrative approach is critical for improving biodiversity monitoring and informing conservation strategies in alpine environments under accelerating climate change.