Prediction of sea ice motion with recurrent neural networks

Prediction of sea ice motion is important for ocean-atmosphere interaction modeling and safe naval operations in polar regions. In this study, we investigate the potential of Recurrent Neural Networks (RNNs) in predictions of motion for several days in the future based only on previously observed satellite image data. We collect a large dataset of daily Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) images that cover the entire Arctic. Optical flow is employed to calculate dense sea ice motion between images of each consecutive-day pair. The optical flow images are then used to train an encoder-decoder Long Short-Term Memory (LSTM) RNN and estimate motion for several days in the future. Experiments demonstrate that the proposed method is successful in predicting short-term sea ice motion with accuracy close to motion calculated from the original images and buoys, and proves promising for further applications and research.