Abstract
Abstract. The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice.
Highlights
The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation (Serreze and Stroeve, 2015)
Data from a variety of satellite sensors, including optical satellite images, passive microwave images, and synthetic aperture radar (SAR) images have been employed to observe polar sea ice. Optical images, such as Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectromete (MERIS), and Advanced Very High Resolution Radiometer (AVHRR), have high temporalspatial resolution, they are often contaminated by cloud, even no available images can be obtained due to poor atmospheric conditions (Petrou et al, 2018)
The final products of the Advanced Microwave Scanning Radiometer 2 (AMSR2) have five types: Level 0, Level1B, Level1R, Level2, and Level3.In particular, the Level 1B brightness temperature swath data of horizontal polarization and vertical polarization at 89 GHz is selected because it has the highest spatial resolution (5×5 km)
Summary
The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation (Serreze and Stroeve, 2015). SAR images, such as Sentinel-1 (Xian and Tian, 2017), have high spatial resolution, but limit to the small swath and low temporal resolution, resulting in mass data processing when producing Arctic sea ice characteristics Passive microwave images, such as the Special Sensor Microwave Imager (SSM/I) on the series of satellites of Defense Meteorological Satellite Program (DMSP), the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on Aqua satellite of the National Aeronautics and Space Administration (NASA) Earth Observation System (EOS), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on the Global Change Observation Mission 1st - Water "SHIZUKU" (GCOMW1), are important data sources for Arctic sea ice observation with the advantages of wide coverage, high temporal resolution, strong surface penetration ability and all-weather work (Petrou et al, 2018). AMSR2 can provide daily coverage of the entire Arctic, its typical spatial resolution of around 6.25-25 km makes it difficult to monitor small leads and ridges, and it is prohibitively coarse for some fine-scale application, such as detailed characteristics of sea ice, and smallscale geographical phenomena (Agency and Project, 2013; Wagner et al, 2020)
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