Phenology, the science of the timing of annual recurring biological events, is one of the best indicators to demonstrate effects of global warming on organisms. Long term monitoring and experimental studies show that the spring phenology of plants has advanced in the last decades. However, long term phenological data in the alpine region are often limited to few locations and thus, not much is known about climate-change induced phenological shifts above the treeline. In seasonally snow-covered regions, snow melt and spring temperatures are the most important phenological cues that initiate plant growth. Therefore, it is essential to track phenological shifts, snowmelt and near-ground temperatures simultaneously. In this study, we make use of a climate station network in the Swiss Alps to reveal phenological advance and relate them to climatic changes. The climate stations are equipped with a snow height sensor, which measures the height of an underlying object, independently of whether this object is snow or not, allowing study plant growth signal over the vegetation period. To accurately distinguish between a plant- and snow signal we use a machine learning algorithm that classifies the multivariate temporal input signal into snow and plants. We pinned down the timing of snow melt and extracted the start of growth from logistic growth curve that were fitted to the plant growth signal at 40 climate stations between 1500 and 2700m over a time of 25 years (1998 – 2023). We observe a 2.4 days/decade earlier occurrence of green-up coinciding with a shorter time lag between snow melt and the onset of plant growth. Because the timing of snowmelt has not changed significantly, we attribute the observed phenological advance of six days over the study period to the steep increase in spring temperature of 0.75°C/decade. The observed phenological shifts in alpine grasslands are in the same range as the advancing leaf-out timing of forests at lower elevation. Our study provides evidence of the profound impact of climate change on alpine vegetation phenology based on in-situ climatic- and vegetation height data.