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

Abstract 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...

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Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Eduard Khachatrian, Wolfgang Dierking, Saloua Chlaily, Torbjørn Eltoft, Frode Dinessen, Nick Hughes, Andrea Marinoni
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2023
Subjects:
remote sensing
sea ice
data fusion
passive microwave radiometer
SAR
unsupervised information selection
Geophysics. Cosmic physics
QC801-809
Sea ice
Online Access:https://doi.org/10.1029/2022GL102083
https://doaj.org/article/8b0e28c0460f4b1ab9de4ff7308f4b45
Description
Summary:Abstract 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.

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