Evaluation of LiDAR-derived Snow Depth Estimates from Consumer Smartphones

Snow is a critical contributor to the global water-energy budget with impacts to springtime flooding and water resource management practices. In situ snow depth measurements are incredibly useful reference observations as inputs to hydrologic models and reanalysis systems. However, Canadian in situ measurement stations have declined in number by over 70% since 1990, and the current observational network is sparsely distributed with large unobserved gaps. Laser altimetry (LiDAR-Light Detection and Ranging) is a remote sensing technique that has demonstrated skill in mapping changes in snow depth, but the expense of purchasing and transporting traditional LiDAR equipment has generally limited their use to large institutions. In this work, we demonstrate that the LiDAR sensor installed on the iPhone 12 Pro acts as a real-time, handheld measurement instrument for accurately observing changes in snow depth. Two independent field experiments in southern Ontario found that the iPhone LiDAR measurements were able to accurately capture daily changes in snow depth when compared to in situ snow ruler measurements. In situ and LiDAR comparisons of n=75 days at measurement site 1 exhibit correlations above 0.99, mean absolute bias less than 1 mm, and an RMSE of approximately 6 mm. Similar positive agreement was also noted at the second field study site for n=16 measurements over a similar period. As LiDAR sensors become commonplace in future smartphones, their capabilities as portable snow depth measurement instruments cannot be understated. The high accuracy of the LiDAR sensor suggests that a mobile application could be developed which allows users to quickly scan a snow-covered area before and after a snowfall event and consequently use this data to aid in filling current observational gaps through a citizen-science based-approach of measuring local-scale changes in snow depth.