The exponential spread of the bark beetle (<em>Ips typographus </em>L.) outbreaks across Europe in recent years has led to heightened interest in remote sensing-based detection. This increase is closely linked with ongoing climate change, which has led to rising temperatures, prolonged dry periods, and increasing frequency and intensity of both biotic and abiotic disturbances. These conditions created a favourable environment for bark beetle proliferation, resulting in larger and more widespread infestations. Effective detection and management of these outbreaks is crucial for forest officals, necessitating the implementation of monitoring systems that complement traditional ground-based efforts. At present, remote sensing approaches for bark beetle detection mainly rely on optical data to identify changes in spectral reflectance of vegetation. In this study, we utilised annual Sentinel-1 synthetic aperture radar (SAR) composites from 2021 to 2023 for infestation detection. A Random Forest classification model was applied to distinguish between healthy and infested forest areas. Additionally, vegetation indices were incorporated to evaluate and compare the results. A reference dataset was used to validate model performance. Our results show that the Sentinel-1 based approach achieved lower accuracies (max. overall accuracy: 0.78), compared to Sentinel-2 (max. overall accuracy: 0.87). Despite this, the Sentinel-1 data proved valuable as a tool for bark beetle infestations detection, especially in scenarios where optical data may be unavailable or limited. These results underscore the importance of integrating SAR data into remote sensing workflows to improve the detection of bark beetle outbreaks.