SAR and Passive Microwave Fusion Scheme: A Test Case on Sentinel-1/AMSR-2 for Sea Ice Classification

The most common source of information about sea ice conditions is remote sensing data, especially images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). Here we introduce an adaptive fusion scheme based on Graph Laplacians that allows us to retrieve the most relevant information from satellite images. In a first test case, we explore the potential of sea ice classification employing SAR and PMR separately and simultaneously, in order to evaluate the complementarity of both sensors and to assess the result of a combined use. Our test case illustrates the flexibility and efficiency of the proposed scheme and indicates an advantage of combining AMSR-2 89 GHz and Sentinel-1 data for sea ice mapping.

Automated summary

The most commonly used source of information for sea ice conditions is remote sensing data, particularly images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). In this study, we propose an adaptive fusion scheme based on Graph Laplacians to extract the most relevant information from satellite images. Through a test case, we evaluate the complementarity of SAR and PMR for sea ice classification and observe the advantage of combining AMSR-2 89 GHz and Sentinel-1 data for sea ice mapping.