Abstract

Water is arguably Earth’s most valuable resource. Nearly 70% of the world’s freshwater is permanently stored in glaciers and ice sheets, located in some of the most rapidly changing regions of the planet. The increasing mass loss from the Greenland and Antarctic Ice Sheets, coupled with a decline in seasonal snow cover, has far-reaching consequences including rising sea levels, changes to freshwater availability, and disruptions to the global surface energy balance. As such, constraining predictions of ice sheet mass loss and seasonal snow cover change is a dominating issue in this century. Polar and seasonal snow regions are remote, largely with limited in-situ observations. However, the recent availability of frequent, high-resolution, multi-source satellite imagery (e.g. Sentinel, Landsat archives), and the advent of big-data and machine learning approaches to process this image archive has provided the opportunity to explore changes in these remote regions at large spatial and temporal scales. Here, I present my multidisciplinary work at the intersection of remote sensing, machine learning, and land surface modeling to gain a more comprehensive understanding of meltwater on the Greenland Ice Sheet and seasonal snow depth in the European Alps.