Abstract
Arctic sea ice leads, which present as linear openings in sea ice, play a critical role in modulating ocean–atmosphere interactions and regional heat fluxes. High-resolution pan-Arctic observations remain limited due to the harsh polar environment. This study develops a deep-learning model, LeadNet, for detecting Arctic sea ice leads from Sentinel-1 synthetic aperture radar (SAR) imagery. LeadNet integrates HH-polarized backscatter and gray-level co-occurrence matrix (GLCM) texture features within a dual-branch architecture and retains sensitivity to narrow leads in the 100–300 m range. The trained model is applied to 14,093 SAR scenes acquired from October 2019 to April 2020 to generate an 80 m pan-Arctic LeadNet Arctic Lead Dataset, including monthly lead frequency maps and pixel-level LeadNet classification results for each scene. Based on the dataset, we analyze lead characteristics. The lead frequency maps show clear seasonal variability, with higher activity during autumn and early winter, a minimum in late winter, and an increase in early spring. In addition, analysis in the Beaufort Sea indicates that detected leads narrower than 300 m or shorter than 5 km account for 20–40% of the total lead area. The dataset provides a high-resolution observational benchmark for studies of Arctic sea ice dynamics, heat fluxes, and climate model evaluation.