Abstract The Arctic Ocean ecosystem is undergoing dramatic changes as the sea ice cover retreats, underscoring the need for technologies that can monitor biological responses in the upper ocean, particularly beneath sea ice, where traditional ship-based investigations are severely constrained. Fish such as polar cod, which depend strongly on under-ice habitats, are widely regarded as key indicator species of Arctic warming, making their observation crucial for understanding ecosystem changes. In this study, we developed and deployed an integrated, fully automated observation system for long-term fish monitoring under the Arctic sea ice. The system combines a custom-designed underwater multi-focal automatic camera system (UMACS), a robust computing platform, and a satellite communication module to realize an autonomous ‘detection-to-transmission’ workflow under extreme polar conditions. A deep learning-based detection model trained on a multi-source dataset was implemented to address challenges such as low illumination, turbidity, and blurred backgrounds. A three-month continuous deployment in the Central Arctic Ocean demonstrated the robust engineering performance of the system under realistic field conditions. Although no fish were unambiguously confirmed, highlighting the intrinsic difficulty of discriminating small biological targets against a pure water background in this region, the system successfully achieved persistent, unattended, under-ice visual observation with data return. Therefore, this study provides a practical and transferable engineering framework for the scalable, technology-driven ecological monitoring of one of the planet’s most remote and fragile marine environments.
JI et al. (2026) studied this question.