Invasive alien plants (IAPs) are increasingly threatening biodiversity worldwide; thus, early detection and monitoring tools are needed. Here, we explored the potential of unmanned aerial vehicle (UAV) images in providing intermediate reference data which are able to link IAP field occurrence and satellite information. Specifically, we used very high spatial resolution (VHR) UAV maps of A. saligna as calibration data for satellite-based predictions of its spread in the Mediterranean coastal dunes. Based on two satellite platforms (PlanetScope and Sentinel-2), we developed and tested a dedicated procedure to predict A. saligna spread organized in four steps: 1) setting of calibration data for satellite-based predictions, by aggregating UAV-based VHR IAP maps to satellite spatial resolution (3 and 10 m); 2) selection of monthly multispectral (blue, green, red, and near infra-red bands) cloud-free images for both satellite platforms; 3) calculation of monthly spectral variables depicting leaf and plant characteristics, canopy biomass, soil features, surface water and hue, intensity, and saturation values; 4) prediction of A. saligna distribution and identification of the most important spectral variables discriminating IAP occurrence using a fandom forest (RF) model. RF models calibrated for both satellite platforms showed high predictive performances (R2 > 0.6; RMSE