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.

翻译：理解山区积雪的结构、数量和类型对于评估雪崩安全性、解读卫星图像、构建精确水文模型以及为周末滑雪旅行选择合适的雪板至关重要。当前，积雪层的此类特征需结合遥感卫星图像、气象站数据以及当地预报员、向导和野外滑雪者提供的繁琐点测量与描述来测定。本文探索了如何利用安装在高山滑雪板顶面的应变传感器，在滑雪过程中估算表层积雪的特性。我们证明，通过两个应变片和一个惯性测量单元，能够以97%的准确率正确地为轨迹中每10秒片段分配三个定性标签之一（粉雪、湿雪或冰状/压雪），且此结果与滑雪风格无关。我们的算法融合了基于数据驱动的雪板-雪面线性模型、降维处理以及朴素贝叶斯分类器。比较应变片之间的分类器性能表明，应变片的最佳安装位置位于雪板固定器与板尖/板尾之间的中间区域，即空载雪板接触雪面前方的拱形弧段。利用雪板实现雪况实时分类的能力，为从山区雪面特性绘图的公民科学项目到基于雪况自动调节刚度的雪板研发等应用打开了大门。