  Accurately estimating the snowpack in key mountainous basins is critical for
water resource managers to make decisions that impact local and global
economies, wildlife, and public policy. Currently, this estimation requires
multiple LiDAR-equipped plane flights or in situ measurements, both of which
are expensive, sparse, and biased towards accessible regions. In this paper, we
demonstrate that fusing spatial and temporal information from multiple,
openly-available satellite and weather data sources enables estimation of
snowpack in key mountainous regions. Our multisource model outperforms
single-source estimation by 5.0 inches RMSE, as well as outperforms sparse in
situ measurements by 1.2 inches RMSE.
