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In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern’s (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions. [ABSTRACT FROM AUTHOR]

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 HH 0) and its GLCM texture parameters for six ice classes and open water. We found very low σ HH 0 for nilas at both C- and L-band. Although similar σ HH 0 found for grey ice at both frequencies, σ HH 0 decrease with increasing ice thickness at L-band from grey ice, whereas, at C-band, σ HH 0 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 HH 0 and GLCM texture statistics. Due to overlapping σ HH 0 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. • A novel dual-frequency image classification approach is introduced. • L- and C-band backscatter signatures of newly formed Arctic sea ice are presented. • Newly formed ice can be detected with higher accuracy at L-band compared to C-band. • A combination of C- and L-band shows improved image classification accuracy in the Arctic. [ABSTRACT FROM AUTHOR]

We construct and present the first baseline, time series evolution of L-band SAR microwave backscatter (σ 0 HH) for landfast Arctic first-year (FYI) and multi-year (MYI) sea ice from ALOS PALSAR. We compare the winter to freeze-up seasonal L-band σ 0 HH signatures with C-band RADARSAT-2 SAR imagery and Ku-band QuikSCAT scatterometer data. We also explored the utility of L-band σ 0 HH to detect FYI and MYI melt onset dates. For both ice types, L-band exhibited a similar σ 0 HH evolution to C- and Ku-bands. A rise in L-band σ 0 HH was observed from winter to melt onset, however, with a lower magnitude attributed to the larger penetration of L-band microwaves through the snow-covered sea ice medium. Over FYI, L-band σ 0 HH exhibited the most robust response and greater diversity in melt onset signature, compared to C- and Ku-bands. Over MYI, L-band σ 0 HH declined during the winter to melt onset transition contrasting the C- and Ku-band evolution of σ 0 HH during melt onset. Although, a distinct L-band σ 0 HH response was not found during pond onset, a very strong σ 0 HH response was found during sea ice break-up, which could operationally be utilized to identify sea ice break-up timing. During freeze-up, L-band σ 0 HH showed greater diversity compared to C- and Ku-bands. Our study also found that, L-band detected melt onset dates were up to 6 days later than C- and Ku-band detected dates, for both ice types. The melt response was the strongest at L-band for FYI, where C-band detected a robust melt onset date for MYI. Therefore, for operational sea ice monitoring, L-band suits better for FYI, whereas C-band is still a better choice for MYI. In the warming Arctic, where FYI has become the dominant ice type, L-band demonstrates promise to be the optimal choice for sea ice operational monitoring with high spatiotemporal data acquisition from existing, upcoming and proposed L-band SAR missions. • Time series evolution of L-band σ 0 HH for Arctic sea ice was presented. • L-band exhibited similar σ 0 HH evolution to C- and Ku-band over first-year ice. • L-band σ 0 HH evolution over multi-year ice was opposite to C- and Ku-band. • Over first-year ice, L-band σ 0 HH exhibited greater diversity in melt signature. • L-band detected melt onset dates were up to 6 days later than C- and Ku-band. [ABSTRACT FROM AUTHOR]