Truls Thorsen Karlsen

We present a robust, incidence angle (IA) and wind speed aware model for separating sea ice from open water and providing high resolution sea ice concentration (SIC) estimates from L-band synthetic aperture radar (SAR) imagery, with potential climate study and operational benefits. By treating open water as a wind speed-dependent class in an established IA-aware Bayesian maximum likelihood ice type classifier, we account for the wind-driven open water backscatter variability by using external wind information. This method circumvents the need for the extensive training datasets or accurate geophysical model functions (GMFs) typically required by established C-band ice/water separation algorithms. To demonstrate this approach, we utilize wide-swath dual-polarized (HH/HV) L-band SAR data from the ALOS-2 mission. The proposed method provides high accuracy during the challenging melt period, achieving high Matthews Correlation Coefficient (MCC) scores (MCC>0.800), and consistently outperforms passive microwave radiometer (PMW) products during this period. During winter conditions, weak sea ice backscatter and system noise limitations hindered reliable ice/water separation (MCC=0.506). We compare our proposed method to a baseline model without incorporated external wind information, and find that the wind-informed classifier achieves consistently higher classification accuracies. Our results demonstrate that integrating external wind data allows for robust, high resolution ice/water separation in L-band SAR imagery during melting conditions based purely on backscatter intensity, removing the need for computationally heavy textural features.

We estimate sea-ice type specific incidence angle (IA) dependencies for dual polarized (HH/HV) L and C-band synthetic aperture radar (SAR) for the winter, melt onset and advanced melt seasons for level and deformed ice, using time-series of Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1 imagery off the north-east coast of Greenland. The IA dependencies are used to radiometrically correct the L and C-band backscatter time-series, which enables analysis of their seasonal evolution. From this, we observe that the L-band backscatter intensity increases for both ice types at the transition from winter to melt onset. We use these results to estimate ice type separability and to train an IA aware Bayesian classifier at both frequencies. These results show that while both frequencies are capable of distinguishing level and deformed ice during the winter, only L-band SAR can reliably make this separation during the melt onset season. During the advanced melt season, the overall classification accuracies are similarly low for L and C-band. This study demonstrates the potential of L-band SAR for sea-ice mapping, which is highly relevant in the light of several upcoming L-band SAR missions.