Snow density plays an important role in natural and human systems but current methods for estimating snow density are limited, especially in the Arctic. This work presents a new method using satellite data to estimate snow density in remote areas. An experiment was conducted in the Canadian Arctic to evaluate the algorithm and it appears to replicate density estimates from manual sampling well. With more work this algorithm could be applied to estimate snow density across the pan-Arctic.

This study adapts a method to detect snowline altitude (SLA) using Google Earth Engine with high-resolution satellite imagery. Applying this method to five glaciated watersheds in the Himalayas reveals regional consistencies and differences in snow dynamics, as well as 20-year trends of snowline increases (3 catchments) and decreases (1) and no trend (1). We investigate the controls of these dynamics by analyzing climatic factors and topographic characteristics.

The subglacial landforms beneath actively fast-flowing ice in Greenland have not been explored in detail, as digital elevation models have not had a high enough resolution to see these features. We use swath radar imaging to visualise landforms at the onset of an ice stream, revealing mega-scale glacial lineations, that would usually be assumed to be indicative of faster ice flow than the current velocities. Interpretation of the landscape also gives an indication of the properties of the bed.