Forecasting water resources from satellite image time series using a graph-based learning strategy

In the context of climate change, it is important to monitor the dynamics of the Earth's surface in order to prevent extreme weather phenomena such as floods and droughts. To this end, global meteorological forecasting is constantly being improved, with a recent breakthrough in deep learning methods. In this paper, we propose to adapt a recent weather forecasting architecture, called GraphCast, to a water resources forecasting task using high-resolution satellite image time series (SITS). Based on an intermediate mesh, the data geometry used within the network is adapted to match high spatial resolution data acquired in two-dimensional space. In particular, we introduce a predefined irregular mesh based on a segmentation map to guide the network's predictions and bring more detail to specific areas. We conduct experiments to forecast water resources index two months ahead on lakes and rivers in Italy and Spain. We demonstrate that our adaptation of GraphCast outperforms the existing frameworks designed for SITS analysis. It also showed stable results for the main hyperparameter, i.e., the number of superpixels. We conclude that adapting global meteorological forecasting methods to SITS settings can be beneficial for high spatial resolution predictions.