Deep learning based spatio-temporal forecasting of snow cover in the Alps using remote sensing data

Snow cover is a critical component of the earth’s climate and weather system, which exhibits high spatial and temporal variability. Therefore, we predict daily snow cover using spatio-temporal forecasting. Unlike traditional forecasting approaches, that require spatial or temporal aggregation, our approach employs deep learning models specifically designed for spatio-temporal data.  Spatio-temporal predictive learning has primarily focused on nowcasting and sub-seasonal forecasts at a daily scale with a lead time up to 15 days. However, we implemented the capability of using such models with long multi-year satellite image time series to predict at a daily scale annually, specifically for daily snow cover. In our research, we use the historical snow cover data from the DLR Global SnowPack remote sensing product, which is a daily cloud free 500m snow cover representation on the ground. We generate high resolution daily snow cover forecasts for up to 365 days (one year ahead) beginning from 1^st July. We implemented models such as Convolutional Long Short-Term Memory (ConvLSTM) networks, convolutional encoder decoder architectures with attention mechanisms, and Vision Transformer (ViT) based models and adapted them for our use case. To further enhance our predictions, we also made adaptations to the models to include multivariate spatial and temporal data which are key drivers of snow cover variability into the model. Topographical feature maps derived from elevation, and time series of climatological indices (atmospheric oscillation patterns) are two examples. Validation against reference data demonstrates exceptional accuracy and F1-scores exceeding 84% across forecasts.