Role of Artificial Intelligence in Remote Sensing of Cryosphere

In remote sensing, Artificial Intelligence (AI) plays a significant role in recognizing earth surface objects and features. With the continuous improvement in the resolution of remote sensing sensors, the applicability of AI is also enhancing with the potential of detailed extraction of Earth surface features. The objective of this article is to explore the potential of AI in remote sensing of the cryosphere, which refers to the portions of the Earth's surface where water is in solid form, including ice sheets, glaciers, snow cover, and permafrost. The incorporation of AI in remote sensing allows the improved monitoring and assessment of the dynamics of the cryosphere. Some of the major milestones of AI in remote sensing of the cryosphere are (a) image classification (e.g., Snow/Ice Detection and Glacier Mapping), (b) Change Detection (e.g., Monitoring Ice Melt and Snow Water Equivalent (SWE) Estimation), (c) Automated Data Processing (e.g., preprocessing, and feature extraction), (d) Climate Modeling and Prediction (e.g., Integration with Climate Models and other relevant datasets), (e) Hazard Assessment (e.g., Glacial Lake Outburst Flood (GLOF) Prediction, snow avalanches, and flash floods), and (f) Data Fusion (e.g., Integration of Multiple Data Sources and Permafrost Thaw Detection). However, these examples are not limited and continuously increasing day by day.