Snow plays a critical role in mountain ecosystems, acting as a natural reservoir for water, supporting biodiversity, and influencing energy balance. In Switzerland, where snow is essential for water resources, hydropower, and winter tourism, understanding snow cover dynamics is vital. Snowmelt provides much of the country’s water, contributing to hydropower generation and agricultural irrigation, while snow cover is a cornerstone of winter tourism, a major economic sector. With the increasing influence of climate change, monitoring and understanding snow cover evolution is crucial for sustainable resource management. This thesis develops a medium-resolution (30 m), long-term dataset of snow cover in Switzerland, dating back to 1984, using the Snow Observations from Space (SOfS) algorithm. This dataset fills a critical gap left by current snow monitoring systems like MODIS, which, with its coarser resolution (500 m) and data availability only starting in 2000, is limited in capturing the finer-scale variability of snow cover in mountainous regions and detecting long-term trends related to climate change. The Swiss Data Cube (SDC) was essential in producing this dataset, enabling efficient processing of large-scale satellite data and conducting comprehensive pixel-based, multi-sensor and time-series analyses, making it invaluable for tracking climate-related trends. Results from this study reveal a significant reduction in snow cover over the last four decades, especially at lower and mid-elevations, with winter and spring months seeing the most dramatic declines. Rising temperatures, which shift precipitation from snow to rain, are the main driver of these changes. One of the study’s major achievements was the development of methods to reduce cloud contamination in satellite imagery, although cloud fraction still affects around 30% of the monthly products. The implemented methods have considerably reduced cloud-related data gaps. The spatiotemporal NDSI models developed in this research have also improved snow detection accuracy, particularly in forested areas and regions with complex topography, offering a more accurate picture of snow dynamics in these challenging environments. Future work should focus on further reducing cloud cover and refining the compositing process to improve temporal resolution. Projections under different climate scenarios suggest that snow cover will continue to decline, with severe reductions expected at low to mid-elevations and during winter and spring months, particularly under the RCP 8.5 scenario. These results emphasize the need for adaptive strategies and science-based decision-making to mitigate the impacts of climate change. The insights gained from this research offer a strong foundation for developing sustainable resource management strategies and guiding climate adaptation efforts, not only in Switzerland but also in other regions facing similar challenges. The SOfS algorithm has proven to be a valuable tool for long-term snow monitoring, enhancing our understanding of snow dynamics and complementing existing snow monitoring systems in Switzerland. By filling gaps, especially in remote and mountainous areas, it provides science-based insights that are crucial for policymakers. These insights enable the development of adaptive strategies for key sectors such as water management, winter tourism, and environmental conservation, as they face the challenges posed by changing snow patterns.