Deep learning for the detection of melt ponds on sea ice from airborne infrared images

Abstract

Melt ponds are puddles of water on Arctic summer sea ice that play an important role in the Arctic climate system, mainly through their influence on the surface energy balance. The albedo of polar oceans is primarily determined by the area fractions of sea ice and darker open ocean. However, the presence of melt ponds also significantly reduces the reflective properties of sea ice. This allows more solar energy to be absorbed, accelerating regional ice melt in a phenomenon known as ice-albedo feedback. Despite their importance, there is a lack of observational datasets that explicitly parameterize melt ponds next to sea ice and open water, and the existing data are mainly based on optical imagery. In this study, we present a new helicopter-borne dataset based on (TIR) and employ a deep learning-based semantic segmentation approach to classify surface types (melt ponds, sea ice, and open water). We evaluate the performance of several model architectures: U-Net, UNet++, PSPNet, DeepLabV3+, SwinTransformer, SAM2-UNet, and AutoSAM. Among these models, UNet++ pre-trained on the AID dataset demonstrates the highest performance, achieving a melt pond (IoU) of 0.486, a sea ice IoU of 0.764, and an open water IoU of 0.660. Furthermore, we apply the most effective model to the datasets of five helicopter flights, resulting in (MPF) estimates with a bias of approximately 11%, which is higher than for melt pond classifications based on visible observations.