Identifying sea ice types in the early stages of development from L-band SAR imagery remains an active research area during the Arctic freeze-up period. We used ScanSAR C- and L-band imagery from RADARSAT-2, ALOS PALSAR and ALOS-2 PALSAR-2, to identify ice types in the North Water Polynya (NOW) and Victoria Strait (VS) region of the Canadian Arctic. We investigated the HH-polarized microwave backscatter coefficient (σ_HH⁰) and its GLCM texture parameters for six ice classes and open water. We found very low σ_HH⁰ for nilas at both C- and L-band. Although similar σ_HH⁰ found for grey ice at both frequencies, σ_HH⁰ decrease with increasing ice thickness at L-band from grey ice, whereas, at C-band, σ_HH⁰ increases from grey to grey-white ice and then decreases as the ice grows. GLCM texture parameters show lower values for L-band than C-band; however, separability among classes was found only for a few selected parameters. We used the support vector machine (SVM) algorithm for ice type classification from SAR scenes using σ_HH⁰ and GLCM texture statistics. Due to overlapping σ_HH⁰ signatures at C-band, early-stage ice classes were substantially misclassified. L-band identified early-stage ice classes with higher accuracy compared to C-band but misclassified thicker ice types and open water. L-band alone provided very good classification results (~80% accuracy) and combining L- and C-band (i.e., dual-frequency approach) further increased accuracy to >90%. C-band alone resulted in the lowest accuracy of <60%. We acknowledge that developing a universal ice classification is still a challenge and requires some manual supervision to adopt variable ice conditions into the classification method. However, a dual-frequency approach can achieve higher classification accuracy than conventionally used single-frequency approaches. This research highlights the value of upcoming L-Band SAR missions to improve sea ice classification in regions where a variety of ice types exist, including many thinner types, which are now dominating an increasingly warming Arctic.

在北极结冰期间，从 L 波段 SAR 图像识别早期发展阶段的海冰类型仍然是一个活跃的研究领域。我们使用来自 RADARSAT-2、ALOS PALSAR 和 ALOS-2 PALSAR-2 的 ScanSAR C 和 L 波段图像来识别加拿大北极的北水波利尼亚 (NOW) 和维多利亚海峡 (VS) 地区的冰类型。我们研究了六类冰和开阔水域的 HH 极化微波后向散射系数 ( σ _HH ^{0 ) 及其 GLCM 纹理参数。}我们发现nilas 在 C 波段和 L 波段的σ _HH ^{0非常低。}尽管在两个频率下发现灰冰的σ _HH ⁰相似，但σ _HH ⁰从灰冰到 L 波段，随着冰厚度的增加而减小，而在 C 波段，σ _HH ⁰从灰色冰增加到灰白色冰，然后随着冰的增长而减小。GLCM 纹理参数显示 L 波段的值低于 C 波段；但是，仅在少数选定参数中发现了类之间的可分离性。我们使用支持向量机 (SVM) 算法使用σ _HH ⁰和 GLCM 纹理统计从 SAR 场景中进行冰类型分类。由于重叠σ _HH ⁰C 波段的特征，早期冰级被严重错误分类。与 C 波段相比，L 波段以更高的准确度识别早期冰类，但错误地分类了较厚的冰类型和开阔水域。单独的 L 波段提供了非常好的分类结果（约 80% 的准确度），结合 L 波段和 C 波段（即双频方法）进一步将准确度提高到 >90%。单独的 C 波段导致 <60% 的最低准确度。我们承认，开发通用的冰分类仍然是一个挑战，需要一些人工监督才能将可变的冰条件纳入分类方法。然而，双频方法可以实现比传统使用的单频方法更高的分类精度。