Advancements in Remote Sensing of the Cryosphere in Polar Regions: Contributions of Artificial Intelligence Techniques over the Last Decade

This chapter investigates the significant role of artificial intelligence (AI)-supported remote sensing (RS) studies in the polar cryosphere, focusing on both Antarctica and the Arctic. Recent developments in RS, including high-resolution satellite imagery and varied sensor capabilities, have considerably improved data collection, while AI techniques, including machine- and deep-learning, have reformed satellite data analysis. We aim to provide a brief overview of AI-supported RS studies, emphasizing the recent technological advancements and their implications for the polar cryosphere. Analysis of satellite data for feature extraction, change detection, and predictions has been automatized by deep algorithms, specifically in convolutional neural network and U-Net. Some of the significant findings are achieved in the automated mapping of supraglacial lakes, high-resolution mapping of bed topography, and the detailed analysis of ice sheet dynamics. With the ongoing advancements in RS, the Internet of Things, and big data, real-time monitoring would probably be possible in the near future. An overview emphasizes the future need for expanding high-resolution satellite imagery, continuous monitoring, and developing prediction models. All these not only enhance our understanding of the polar cryosphere but also help in mitigating the impacts of global climate change.