The Applications of Remote Sensing, Machine Learning and Deep Learning in Frozen Ground Regions

Dear Colleagues,

Frozen ground is an important component of the cryosphere. Permafrost regions underlie approximately 24% of the exposed land surface of the Northern Hemisphere, and seasonally frozen ground (SFG) regions occupy 57%. Such a vast area extent of frozen ground plays a significant role in the local to global atmospheric circulation, climate, hydrology, and terrestrial ecosystems by affecting the energy, water, and carbon cycles. Due to significant global warming, frozen ground and its environment have experienced great changes, e.g., the freeze–thaw process, the area extent, ground temperature, landform, vegetation, and others. Thus, it is necessary to study this topic. Excluding classical field observations, remote sensing, machine learning, and deep learning methods are popular in the field of frozen ground research.

This Special Issue is aimed at studies covering different applications of remote sensing, machine learning, and deep learning in the frozen ground, including seasonally frozen ground and permafrost. Topics may cover anything from the related frozen ground in the point-regional-hemisphere scales. Hence, algorithms, applications, and simulations in frozen ground studies, among other issues, are welcome. Articles may address, but are not limited to, the following topics:

• Remote sensing in the freeze/thaw status;
• Seasonally frozen ground changes;
• Permafrost changes;
• Landform;
• The application or development of algorithms in the frozen ground study;
• Environment changes in the frozen ground regions.

Prof. Dr. Xiaoqing Peng
Prof. Dr. Dongliang Luo
Dr. Raul-David Șerban
Dr. Lingcao Huang
Prof. Dr. Yonghong Yi
Guest Editors

Manuscript Submission Information

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• frozen ground
• permafrost
• remote sensing
• machining learning
• deep learning
• InSAR
• carbon