Due to accelerating global warming [1], the polar regions, and in particular the Arctic, are subject to many changes and cascading effects [2]. Northern lands are facing a generalized rise in soil temperature causing changes in the surface cover [3], the hydrological and mechanical state of the subsoil including permafrost thaw [4], [5] and potentially triggering massive release of greenhouse gases [6]. As land cover is a key control parameter for permafrost state, the survey of surface changes is of great importance. Consequently, monitoring the evolution of surface boreal ecosystems over large time scales, satellite imagery combined with reliable and proven methodologies is crucial for understanding the impact of climate change on polar continental regions. In this study, we use a Random Forest algorithm to analyze satellite images from the Copernicus (ESA) Sentinel-1 and Sentinel-2 programs in combination with ground truth data collected in July 2024, to monitor changes in the surface ecosystem over a 480 km² area in the Abisko region (Arctic Sweden). Random Forest method applied to features derived from satellite images allows the production of reliable land cover maps (>87% accuracy). Our results demonstrate that radar imagery is a vital source of information for overcoming the inherent limitations of optical imagery caused by frequent and dense cloud cover, particularly in summer, when average monthly cloud cover can reach up to 85% [7]. Additionally, they highlight that combining optical and radar imageries with a robust machine learning approach enables the production of high-quality land cover maps, providing significant added value for long term and high temporal resolution monitoring of land cover changes in northern continental regions.