Sea ice profoundly influences ocean circulation, the polar environment, biology, climate, and commercial activities. The rapidly changing sea ice environment and increased human activities in polar regions drive the demand for sea ice monitoring. Spaceborne synthetic aperture radar (SAR) has been widely adopted for sea ice sensing due to its all-weather, high spatial resolution, and day-and-night imaging capabilities. Previous reviews have addressed sea ice sensing based on various applications and sensors. However, no meta-analysis has been performed to specifically explore spaceborne polarimetric SAR-data-based sea ice sensing. Therefore, this study aims to provide a meta-analysis of spaceborne polarimetric SAR-data-based sea ice sensing by investigating 182 articles published in the last decade. Sea ice sensing applications for retrieving four key geophysical parameters (sea ice types, concentration, thickness, and motion) as well as SAR scattering characteristics analysis for sea ice are included. The review database was created with 15 fields including quantitative and qualitative perspectives, such as SAR frequency, polarization mode, methodology, evaluation metrics, etc. This meta-analysis aims to provide comparisons among different techniques and identify current challenges to determine effective methods for sea ice sensing. Overall, a snapshot of spaceborne polarimetric SAR-databased sea ice sensing is presented through the meta-analysis, which could benefit researchers for future studies to advance this field.
Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.
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