An automated method for polynya detection using a geomorphon algorithm

Abstract. Polynyas, persistent areas of open water within sea ice, are critical features of polar marine systems, facilitating ocean-atmosphere heat exchange, deep water formation, nutrient cycling, and biological productivity. However, current remote sensing approaches typically delineate polynyas using either sea ice concentration thresholds or thin sea ice thickness retrievals. While these methods have proven highly valuable, they can struggle to capture complex polynya morphology, especially fine-scale coastal features, and can be time-consuming to use and inconsistent across spatial scales. This study presents a novel application of a geomorphon pattern recognition algorithm, originally developed for terrestrial landform classification, to automate polynya detection using sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) ARTIST Sea Ice (ASI) product. Focusing on two key Southern Ocean regions, the Weddell and Amundsen Seas, we assess the algorithm's performance through a comprehensive sensitivity analysis involving multiple geomorphon parameter combinations and comparisons with traditional sea ice concentration threshold-based methods. By identifying morphological analogues, such as depressions and valleys in sea ice concentration data, the geomorphon method accurately captures polynya morphology, achieving F1-scores of 0.88–0.94 relative to threshold-based detections, while producing area estimates consistent with published observations. Additional sensitivity analyses examining both geomorphon parameter selection and uncertainty in the underlying sea ice concentration observations indicate that derived polynya areas are robust to plausible observational errors. The method's scalability and self-adaptive lookup distance allows detection of both large-scale open-ocean polynyas, and fine-scale coastal polynyas. The automated nature of the method enables efficient processing of large sea ice concentration data products while avoiding reliance on predefined concentration thresholds. Although further testing across additional regions, datasets, and polynya types is required, these results demonstrate that morphology-based classification provides a promising framework for automated and reproducible polynya detection and the generation of consistent long-term polynya records.