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Introduction: This research explores the application of generative artificial intelligence, specifically the novel ARISGAN framework, for generating highresolution synthetic satellite imagery in the challenging arctic environment. Realistic and high-resolution surface imagery in the Arctic is crucial for applications ranging from satellite retrieval systems to the wellbeing and safety of Inuit populations relying on detailed surface observations. Methods: The ARISGAN framework was designed by combining dense block, multireceptive field, and Pix2Pix architecture. This innovative combination aims to address the need for high-quality imagery and improve upon existing state-of-the-art models. Various tasks and metrics were employed to evaluate the performance of ARISGAN, with particular attention to land-based and sea icebased imagery. Results: The results demonstrate that the ARISGAN framework surpasses existing state-of-the-art models across diverse tasks and metrics. Specifically, land-based imagery super-resolution exhibits superior metrics compared to sea ice-based imagery when evaluated across multiple models. These findings confirm the ARISGAN framework's effectiveness in generating perceptually valid highresolution arctic surface imagery. Discussion: This study contributes to the advancement of Earth Observation in polar regions by introducing a framework that combines advanced image processing techniques with a well-designed architecture. The ARISGAN framework's ability to outperform existing models underscores its potential. Identified limitations include challenges in temporal synchronicity, multispectral image analysis, preprocessing, and quality metrics. The discussion also highlights potential avenues for future research, encouraging further refinement of the ARISGAN framework to enhance the quality and availability of high-resolution satellite imagery in the Arctic. [ABSTRACT FROM AUTHOR]

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as "leads", play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments. [ABSTRACT FROM AUTHOR]

Ice Pathfinder (Code: BNU-1), launched on 12 September 2019, is the first Chinese polar observation microsatellite. Its main payload is a wide-view camera with a ground resolution of 74 m at the subsatellite point and a scanning width of 744 km. BNU-1 takes into account the balance between spatial resolution and revisit frequency, providing observations with finer spatial resolution than Terra/Aqua MODIS data and more frequent revisits than Landsat-8 OLI and Sentinel-2 MSI. It is a valuable supplement for polar observations. Geolocation is an essential step in satellite image processing. This study aims to geolocate BNU-1 images; this includes two steps. For the first step, a geometric calibration model is applied to transform the image coordinates to geographic coordinates. The images calibrated by the geometric model are the Level1A (L1A) product. Due to the inaccuracy of satellite attitude and orbit parameters, the geometric calibration model also exhibits errors, resulting in geolocation errors in the BNU-1 L1A product. Then, a geometric correction method is applied as the second step to find the control points (CPs) extracted from the BNU-1 L1A product and the corresponding MODIS images. These CPs are used to estimate and correct geolocation errors. The BNU-1 L1A product corrected by the geometric correction method is processed to the Level1B (L1B) product. Although the geometric correction method based on CPs has been widely used to correct the geolocation errors of visible remote sensing images, it is difficult to extract enough CPs from polar images due to the high reflectance of snow and ice. In this study, the geometric correction employs an image division and an image enhancement method to extract more CPs from the BNU-1 L1A products. The results indicate that the number of CPs extracted by the division and image enhancements increases by about 30% to 182%. Twenty-eight images of Antarctica and fifteen images of Arctic regions were evaluated to assess the performance of the geometric correction. The average geolocation error was reduced from 10 km to ~300 m. In general, this study presents the geolocation method, which could serve as a reference for the geolocation of other visible remote sensing images for polar observations. [ABSTRACT FROM AUTHOR]