Abstract
Addressing the current situation where in situ observations in the Arctic primarily target physical and a few biogeochemical parameters, leaving a gap in systematic direct observation of biological populations beneath sea ice, this study developed a polar ice-based ecological observation buoy system. Building upon conventional meteorological and oceanographic hydrographic sensors, this system innovatively integrates an underwater imaging module and key technologies such as machine learning-based automatic fish target recognition and reliable dual-channel satellite data transmission in polar environments. Its successful deployment during the 2025 15th Chinese National Arctic Research Expedition verified the system’s stability. During the initial one-month operation period (designed for a monitoring cycle of not less than one year), the data return rates for conventional and image data reached 100% and 96.8%, respectively, achieving quasi-real-time continuous observation of physical and ecological parameters at the air–sea interface in the Arctic Ocean, and it is capable of acquiring not only physical parameters but also visual observations of under-ice fauna. The system successfully acquired and transmitted images containing suspected biological targets and reference objects, providing the first in situ, image-based biological observation dataset for the central Arctic Ocean. This work establishes a new methodological capability for direct ecological monitoring, offering essential equipment support for quantifying biological presence, studying population dynamics, and informing evidence-based polar ecosystem governance.