Water resources from snow sustain the lives of about one-sixth of our planet’s population, thrive our economy, and regulate our climate. Therefore, the monitoring of seasonal snow and its water equivalent (SWE) is of critical importance. And yet, SWE remains one of the largest unknowns in the cryosphere, especially in mountainous regions. “How much seasonal snow do we have in the world’s mountains?”, “Which mountain ranges are strongest affected by climate change and face a shrinking snowpack or earlier melt?” and “How are these snowpack changes impacting the water availability for agriculture, for hydropower generation, or for the more than a billion people that depend on snow for drinking water?”. These major questions today remain unsolved. Many regions, such as the Himalayas, are too remote and harsh for measuring snow properties on the ground, whereas model simulations suffer from poor precipitation estimates in mountains. Traditional remote sensing approaches fail over complex topography and deep snow. Progress in snow remote sensing is therefore urgently needed. SNOW-MODE aims to address this long-standing observation gap by: (i) developing novel methods for accurate satellite SWE retrieval, (ii) unravelling the physical mechanisms that impact SWE remote sensing and (iii) quantifying the dynamics in SWE by modes of climate variability and the associated threats on water security for society.