Advancing Sea Ice Surface Classification by Self-Supervised Contrastive Learning for Radar Altimetry

Accurate estimates of Arctic sea ice thickness from radar altimeter waveforms are essential for climate monitoring but do not yet meet the target accuracy requirements. A sea ice density parametrization resulting from a classification of surface types such as new ice (NI), first-year ice (FYI), multiyear ice (MYI), and open water from the same data could bring an essential improvement. Traditional approaches rely on a small set of hand-crafted waveform parameters, potentially discarding valuable information. At the same time, it is desired to retrieve a low-dimensional representation of the waveforms to facilitate the separation of surface types without relying on class labels. To address this, we are the first to adapt self-supervised contrastive learning (SSL) on Sentinel-3 SRAL data for Arctic Sea ice classification. We adapt supervised contrastive learning (SupCon) to demonstrate class separation in embedding space. Correspondingly, we introduce a novel batch-sampling strategy, and we utilize positive pairs defined by the spatio-temporal proximity of altimeter footprints. By curating a seasonal dataset of ice chart-labeled radar altimeter measurements, we test the quality of our SSL-representations by k-NN. Our approach improves classification accuracy by 4% over baselines on waveform parameters. A combined approach mixing traditional parameters with SSL-distilled features yields a performance gain of 7%. Through numerical experiments, we identify clear bounds for the optimal dimensionality of the embedding space.