  Understanding the structure, quantity, and type of snow in mountain
landscapes is crucial for assessing avalanche safety, interpreting satellite
imagery, building accurate hydrology models, and choosing the right pair of
skis for your weekend trip. Currently, such characteristics of snowpack are
measured using a combination of remote satellite imagery, weather stations, and
laborious point measurements and descriptions provided by local forecasters,
guides, and backcountry users. Here, we explore how characteristics of the top
layer of snowpack could be estimated while skiing using strain sensors mounted
to the top surface of an alpine ski. We show that with two strain gauges and an
inertial measurement unit it is feasible to correctly assign one of three
qualitative labels (powder, slushy, or icy/groomed snow) to each 10 second
segment of a trajectory with 97% accuracy, independent of skiing style. Our
algorithm uses a combination of a data-driven linear model of the ski-snow
interaction, dimensionality reduction, and a Naive Bayes classifier.
Comparisons of classifier performance between strain gauges suggest that the
optimal placement of strain gauges is halfway between the binding and the
tip/tail of the ski, in the cambered section just before the point where the
unweighted ski would touch the snow surface. The ability to classify snow,
potentially in real-time, using skis opens the door to applications that range
from citizen science efforts to map snow surface characteristics in the
backcountry, and develop skis with automated stiffness tuning based on the snow
type.
