Mapping Arctic Polynyas from Sea Ice Concentration Data Using Hierarchical Partitioning and Differentiated Region Growing: Sensitivity to Threshold and Product Choices

Abstract

Accurately delineating Arctic coastal polynyas from sea ice concentration data remains challenging because of complex coastal geometry and spatially heterogeneous ice conditions. We develop a daily sea ice concentration (SIC) mapping framework based on hierarchical partitioning, connected-component analysis, and differentiated region growing for polynya and open-water regions. Applied to September-May ice seasons from 2002/03 to 2024/25, the framework achieves a mean overall accuracy of 96.74% and a mean kappa coefficient of 0.93 in the 63-scene visual assessment. Sensitivity experiments reveal a consistent pattern: the absolute magnitude of SIC-defined polynya extent is strongly configuration-dependent, whereas major temporal features are comparatively more stable. Relative to a 75% upper SIC threshold, seasonal extent integrals differ by 13.48%, 32.47%, and 65.04% at thresholds of 80%, 85%, and 90%, respectively, while the broad interannual structure remains recognizable. The response is also strongly regional and seasonal, with the largest threshold sensitivity in the Central Arctic Ocean, very weak sensitivity in the Gulf of St. Lawrence, and greater pan-Arctic curve separation from January onward. At an 80% threshold, product-comparison MAPE is 68.19% between the 6.25 and 25 km products and 11.47% between the 12.5 and 25 km products under their respective comparison periods and reference definitions. Regional product disagreement is likewise heterogeneous. Together, these results show that reproducible long-term polynya monitoring requires a fixed and explicitly documented combination of SIC product, threshold, and spatial classification rules, while broad temporal patterns can remain informative even when absolute extent differs substantially.