Forest stands are the basic units of forest management, whose delineation is a fundamental and significant step for creating stand-level field inventories. Traditional and recent methods for delineation often rely on expensive data and suffer from limited spatial coverage. The Sentinel-2 mission has special bands aimed at vegetation mapping and provides the highest spatial resolution publicly available imagery of the globe. We aim to take the first step for using solely the Sentinel-2 imagery for forest stand delineation. A superpixel segmentation scheme based on a modified version of the simple linear iterative clustering (SLIC) superpixel algorithm, Gaussian processes regressor SLIC (GPR-SLIC) is employed with a feature set composed of Sentinel-2 time-series spectral bands and vegetation indices. GPR-SLIC replaces the spectral distance metric in the original SLIC algorithm with a Gaussian processes regressor, which is trained using the Sentinel-2 feature set and a ground truth forest stand map. GPR-SLIC, a modified SLIC algorithm, leverages multi-temporal Sentinel-2 spectral bands and vegetation indices as features. It replaces the original SLIC’s spectral distance metric with a trained GPR. Original SLIC, simple non-iterative clustering, linear spectral clustering, and entropy rate superpixels algorithms are employed for producing reference segmentations based on the LiDAR-derived canopy height model. The findings demonstrate that by incorporating Sentinel-2 time-series features, the GPR-SLIC algorithm yields more precise results for boundary adherence performance metrics and undersegmentation error. The results underscore the potential of employing a machine learning-based superpixel segmentation algorithm with multi-temporal Sentinel-2 images for effectively delineating forest stands.