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. Plain Language Summary: The Earth's land and ocean surface is monitored from space using different sensors mounted on various satellite platforms. Each type of sensor has its advantages and limitations. Combining data from different sensors can potentially solve ambiguities in information retrievals associated with the use of only a single sensor. Here, we apply a multi‐sensor fusion scheme that can be used for various data combinations in order to extract relevant information. The main goal of this work is to explore the potential of simultaneously applying two sensors for sea ice mapping and monitoring, namely synthetic aperture radar and passive microwave radiometers, in order to improve the separation of sea ice types. Key Points: We propose an adaptive scheme for the fusion of passive microwave radiometers and synthetic aperture radar data for sea ice classificationWe demonstrate the flexibility and efficiency of the proposed scheme on a test case by evaluating the performance of various data scenariosWe illustrate the advantages and limitations of applying the data of each sensor simultaneously and separately [ABSTRACT FROM AUTHOR]