From space to species: A high-resolution dataset of biodiversity-relevant remote sensing products for the Alps 

Recent advances in remote sensing technology and methods have generated an unprecedented number of biodiversity-related products, significantly enhancing our ability to monitor and understand biodiversity. The combination of high-revisit satellites, such as harmonized Landsat and Sentinel-2 (HLS), along with spaceborne LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI), provides both temporal frequency and structural information at unprecedented detail. Numerous datasets are now accessible, either as analysis-ready products or variables that can be generated from the growing archive of satellite observations. These data include topographical features, land cover, land use, phenology, productivity, plant traits and their diversity. However, these observations and products are scattered across different platforms, making data discovery, preparation, and processing laborious and time-consuming. To address this limitation, we compiled and standardized a collection of over 70 remote sensing products relevant to biodiversity - including vegetation structure, plant traits, land cover classes, dynamic habitat indices, spectral diversity and habitat heterogeneity. All datasets have a very high spatial resolution (10–100 m) and cover the entire Alpine Region (as delineated by the European Union Strategy for the Alpine Region - EUSALP). By integrating these products with in-situ biodiversity monitoring records from Switzerland for birds, butterflies, and plants, we demonstrate how remote sensing data can enable landscape-scale biodiversity modelling to identify biodiversity hotspots and assess the representativeness of monitoring networks across diverse Alpine ecosystems. These models achieved robust predictions of species richness at the landscape scale (1 km²), with cross-validated R² values exceeding 0.7 across the three taxonomic groups. Independent validation using monitoring data from Germany, Austria, and Italy further confirmed the potential of remote sensing datasets for developing accurate and transferable modelling approaches. The entire dataset will be made openly available to facilitate the integration of remote sensing data into species distribution and macroecological models, providing improved potential for both prediction and ecological inference.