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Artificial Intelligence in Earth Cryosphere

Innovations and Applications
Edited by Amit Sharma, Supratim Guha, Neha Bhati, and Abdullah Alzahrani
Expected Pub Date: 2026  |  Hardcover  |  
412 pages

One Line Description
Discover how AI and deep learning are revolutionizing polar and glacial science, giving researchers, engineers, and environmental leaders the cutting-edge tools needed to predict, monitor, and protect Earth’s rapidly changing frozen environments.

Audience
Environmental and climate scientists, policymakers, researchers, and academics seeking to leverage AI techniques for glacier dynamics, permafrost degradation, and sea ice analysis.

Description
The integration of artificial intelligence into cryosphere research represents a cutting-edge advancement in environmental science, enabling more accurate predictions, real-time monitoring, and data-driven insights into the effects of climate change on Earth’s frozen regions, ultimately transforming how we approach environmental conservation and climate resilience across the globe. This book explores the transformative role of AI in understanding, monitoring, and preserving the cryosphere, which encompasses Earth’s frozen regions such as glaciers, ice sheets, and permafrost. It delves into the integration of machine learning, deep learning, and neural networks in addressing complex cryosphere challenges, including climate change impacts, ice dynamics, and environmental monitoring. Through a comprehensive approach, the book highlights how AI technologies can be leveraged to advance cryosphere-related research, offering practical and innovative applications across the field. By combining theoretical concepts with practical case studies and examples, the book aims to cater to researchers, engineers, and practitioners looking to understand and apply AI technologies in Earth cryosphere science.
Readers will find the volume:
• Deeply dives into AI applications for glacier dynamics, permafrost, and sea ice research;
• Presents innovative case studies, showcasing real-world AI-driven cryosphere research solutions;
• Provides a multidisciplinary approach that combines environmental research with cutting-edge AI technology;
• Focuses on future advances, ethics, and the involvement of AI in addressing cryosphere-related environmental challenges.

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Author / Editor Details
Amit Sharma, PhD is an Associate Professor in the Department of Computer Science and Applications at Vivekananda Global University, Jaipur, India, with more than 14 years of teaching experience. A prolific researcher, he has published three books and more than 60 articles in peer-reviewed international journals and conference proceedings, and holds eight patents. His research interests encompass artificial intelligence, machine learning, deep learning, image processing, cloud computing, and big data.

Supratim Guha, PhD is a Postdoctoral Researcher at the MetaEarth Lab, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea. His research primarily investigates Arctic glacier dynamics and their controlling parameters in the Svalbard region. Integrating advanced remote sensing techniques with artificial intelligence and machine learning models, his current work seeks to improve predictions of glacial change and its implications for polar environments under a changing climate.

Neha Bhati, PhD is a Research Associate in the Department of Computer Science and IT, IIS (Deemed to be University), Jaipur,Rajasthan, India. She has authored several articles in international journals and conferences and edited several books. She focuses on developing innovative solutions for environmental monitoring, climate assessment, and resource management using tools such as Google Earth Engine, QGIS, ENVI, and ERDAS Imagine.

Abdullah Alzahrani, PhD is a lead researcher and special lecturer in the Department of Electrical and Computer Engineering at Oakland University, Rochester, Michigan, USA. He spearheads technology transfer initiatives to enhance local industrial capabilities, leveraging global research and development partnerships and emerging digital platforms. His work emphasizes integrating advanced computational methods with sustainable energy technologies to improve efficiency, reliability, and resilience.

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Table of Contents
Preface
Part 1: AI for Snow, Glaciers, and Cryosphere
1. Artificial Intelligence for Snow Cover Mapping in the Western Himalayas: Integrating Sentinel-2 Imagery and Google Earth Engine
Neha Bhati and Narayan Vyas
1.1 Introduction
1.2 Study Area and Satellite Data
1.2.1 Study Area
1.2.2 Satellite Dataset
1.3 Methodology
1.3.1 Image Preprocessing and Composite Generation
1.3.2 Feature Derivation (NDSI and Terrain Variables)
1.3.3 Training Data Generation
1.3.4 SVM Classification
1.3.5 Accuracy Assessment and Visualization
1.4 Results and Discussion
1.4.1 Visual Analysis
1.4.2 Quantitative Analysis
1.5 Conclusion
References
2. Satellite Data and Artificial Intelligence for Climate Change Assessment
Aritra Guin and Santosh Korav
2.1 Introduction
2.2 Satellite Data for Climate Monitoring
2.2.1 Application of Satellite Data for Atmospheric Analysis
2.2.1.1 Greenhouse Gases
2.2.1.2 Aerosols and Clouds
2.2.2 Application of Satellite Data on the Ocean Ecosystem
2.2.3 Application of Satellite Data for Terrestrial Ecosystems
2.3 AI in Climate Change Impact Assessment
2.3.1 AI Techniques Used
2.3.2 Applications of AI in Satellite Data Analysis
2.3.2.1 Enhanced Data Processing and Interpretation
2.3.2.2 Climate Prediction and Modeling
2.3.2.3 Deforestation and Land Use Change
2.3.2.4 Ice Sheet and Glacier Monitoring
2.3.2.5 Analysis of the Oceans and Marine Ecosystems
2.4 Challenges and Limitations
2.4.1 Data Challenges
2.4.2 Modeling and Algorithm Limitations
2.4.3 Instrument-Related Biases and Data Interoperability
2.5 Case Studies and Real-World Examples
2.6 Future Directions
2.6.1 Emerging Satellite Technologies
2.6.2 AI Innovations
2.6.3 Integration with Other Data Sources
2.7 Conclusion
References
3. Machine Learning and Data Science in Cryosphere Applications
R. Srivats, Kalyanasundaram V., Keerthi A.J., Yuvan Shankar Baabu K. and Parvathi R.
3.1 Introduction
3.1.1 Overview of Cryosphere Data
3.1.2 Data Preprocessing and Missing Data Handling
3.2 Machine Learning in Cryosphere Applications
3.2.1 Classification Techniques for Ice, Snow, and Permafrost
3.2.2 Predictive Modelling for Glacier and Ice Sheet Dynamics
3.3 Data Science in Cryosphere Applications
3.3.1 Anomaly Detection for Cryospheric Changes
3.3.2 Statistical Modelling for Ice and Snow Dynamics
3.3.3 Uncertainty Quantification in Climate Predictions
3.4 Explainability and Trust in AI-Driven Cryospheric Models
3.4.1 Interpretability of Machine Learning Models
3.4.2 Model Transparency Frameworks for Climate Applications
3.5 Conclusion
References
4. AI-Driven Clean-Ice Glacier Mapping Using XGBoost Classification for the Western Himalayas
Ocean Agarwal, Paramita Hazra, Iti Shrivas and Ashutosh Laxman Taral
4.1 Introduction
4.1.1 Study Area
4.1.2 Data Sources
4.2 Methodology
4.2.1 Feature Engineering
4.2.2 Mask Generation, Sample Extraction, and Spatial Cross-Validation Design
4.2.3 Hyperparameter Tuning and Model Training
4.2.4 Threshold Determination and Post-Processing
4.2.5 Full-Scene Prediction and Output Generation
4.2.6 Validation and Accuracy Assessment
4.3 Results
4.4 Discussion
4.5 Conclusion
References
5. Nonlinear Spectral Unmixing of Debris-Covered Glaciers
in the Himalayas Using Autoencoders
Subhranil Mustafi, Sarbani Palit and Rajat Kumar Pal
5.1 Introduction
5.2 Region of Interest
5.2.1 Data and Its Sources
5.2.2 Supraglacial Debris Cover Data
5.3 Spectral Unmixing—Set Up
5.3.1 Autoencoder Architecture
5.3.1.1 Loss Function
5.3.1.2 Training Procedure and Post-Processing
5.3.2 Abundance Map Generation Using Autoencoder
5.4 Results
5.4.1 Utilization of High-Resolution Data for Surface Composition Mapping
5.4.2 Abundance Map Visualization
5.4.3 Quantitative Evaluation Metrics
5.4.4 Class-Wise Area Estimation
5.4.5 Comparison with Other Models
5.5 Conclusion
Acknowledgments
Bibliography
6. Integrated GLOF Susceptibility Assessment in the Himachal Himalaya Using Multi-Criteria and Remote Sensing
Approaches
Deepali Gaikwad and Reet Kamal Tiwari
6.1 Introduction
6.1.1 Study Area
6.2 Materials and Methods
6.2.1 Materials
6.2.2 Methods
6.2.2.1 Glacial Lake Mapping
6.2.2.2 GLOF Hazard Assessment
6.3 Results
6.3.1 Inventory of Glacial Lakes and Their Changes
6.3.2 Assessment of Hazardous Glacial Lakes
6.4 Discussion
6.5 Conclusion
Bibliography
Part 2: AI for Hazards and Cryosphere Monitoring
7. AI and Remote Sensing for Hydro-Meteorological Hazard Assessment: Applications in Floods and GLOFs
Daishisha Iawphniaw, Niket Mishra, Saurav Ray, Nischal Sharma, Reet Kamal Tiwari, Ashutosh Laxman Taral and Deepali Gaikwad
7.1 Introduction
7.1.1 Floods
7.1.2 Glacial Lake Outburst Floods
7.2 Methods
7.2.1 RS Techniques
7.2.2 AI Techniques
7.3 Challenges and Limitations
7.3.1 Remote Sensing
7.3.2 Artificial Intelligence
7.4 Conclusion
Bibliography
8. Strategies for Cloud Removal in Snow-Dominated Landscapes: Techniques for Accurate Change Detection
Bhavneet Kaur, Raman Maini and Sartajvir Singh
8.1 Introduction
8.1.1 Importance of Continuous Snow Cover Monitoring
8.1.2 Cloud Cover as a Challenge in Optical Satellite Imagery
8.2 Cloud Removal for Reliable Change Detection
8.2.1 Implications for Snow and Glacier Monitoring
8.2.2 Cloud Removal Techniques for Improved Change Detection
8.2.2.1 Multi-Temporal Compositing
8.2.2.2 Multi-Sensor Fusion
8.2.2.3 Machine Learning and Deep Learning-Based Techniques
8.3 Workflow for Cloud-Free Change Detection
8.4 Case Study: Seasonal Snow Cover Dynamics in the Himalayas
8.5 Limitations and Future Scope for Cloud Removal Strategies
for Change Detection in Snow-Affected Areas
8.6 Conclusion
References
9. Snow Cover Analysis Using Scatterometry Satellite Over
the Himalayan Region
Rajinder Kaur, Sartajvir Singh and Ganesh Kumar Sethi
9.1 Introduction
9.2 The Study Site and Dataset
9.3 Methodology
9.4 Results and Discussion
9.5 Conclusion
References
10. Artificial Intelligence in Monitoring Carbon Storage in Permafrost
Santosh Korav and Umesh Kumar Singh
10.1 Introduction
10.1.1 Understanding Permafrost
10.1.2 Importance of Permafrost in the Global Carbon Cycle
10.1.3 Challenges in Monitoring and Quantifying Carbon Storage in Permafrost
10.2 Role of Remote Sensing and Geospatial Technologies
10.2.1 Overview of Satellite-Based Remote Sensing for Permafrost Monitoring
10.2.2 Sensor Purposes in Permafrost Measurements
10.3 AI in Permafrost Monitoring
10.3.1 Challenges and Limitations
10.4 Artificial Intelligence Use in Permafrost Monitoring
10.5 Predictive Models Development for Carbon Storage and Release Estimation in Permafrost
10.6 Supervised and Unsupervised Learning Approaches for Permafrost Feature Extraction
10.7 Convolutional Neural Networks for High-Resolution Permafrost Mapping
10.8 Challenges and Limitations
10.9 Uncertainties in AI Model Performance and the Need for Validation
10.10 Impact Assessment: AI-Guided Permafrost Monitoring on Policy, Decision-Making, and Climate Change Mitigation
10.11 Future Trends and Opportunities
10.12 Conclusion
References
11. Remote Sensing and Machine Learning Synergies for Carbon Cycle Modeling in Permafrost Regions
Nirmalya Basu, Inderjeet Singh, Ajay Pal Singh, Parvez Rahi
and Yash Mahajan
11.1 Introduction
11.1.1 The Permafrost Carbon Feedback Loop
11.1.2 Challenges in Permafrost Carbon Monitoring
11.1.3 The Emerging Role of Artificial Intelligence in Environmental Monitoring
11.2 Literature Review
11.2.1 Traditional Methods for Monitoring Permafrost Carbon
11.2.2 Remote Sensing Technologies for Permafrost Studies
11.2.3 Evolution of AI Applications in Cryosphere Research
11.2.4 Current State of AI in Permafrost Carbon Monitoring
11.2.5 Research Gaps and Opportunities
11.2.6 Comparative Analysis of AI Techniques
11.3 AI Methodologies and Applications
11.3.1 ML for Carbon Flux Estimation
11.3.2 DL for Remote Sensing Image Analysis
11.3.3 Computer Vision for Landscape Change Detection
11.4 Conclusion
11.4.1 Recommendations for Research and Practice
11.4.2 The Future of AI in Permafrost Carbon Monitoring
References
12. Advancing Cryospheric Monitoring with DExtER Air: A Satellite-Based Temperature Dataset for the Northwestern Himalaya
Bhawna Pathak, Ipshita Priyadarsini Pradhan, Reet Kamal Tiwari and Dericks P. Shukla
12.1 Introduction
12.2 Study Area
12.2.1 Data
12.2.2 Satellite Data
12.2.3 In Situ Air Temperature Data
12.3 Methods
12.3.1 Retrieval of LST from Landsat 8
12.3.2 Retrieval of LST from MODIS Aqua and Terra
12.3.3 Data Preparation
12.3.4 Model Training for Predicting Air Temperature
12.3.5 Statistical Performance Metrics
12.3.6 Nonlinear Model for Air Temperature Estimation
12.4 Results
12.4.1 Predicting Air Temperature Using MLR
12.4.2 Predicting Air Temperature Using NLMR
12.4.2.1 Dharamshala Station
12.4.2.2 Kalpa Station
12.4.2.3 Bhuntar Station
12.4.2.4 Sundernagar Station
12.4.3 Validation of Non-Linear Model Output Using NHP Weather Stations
12.5 Comparative Analysis of DExtER Air Temperature Datasets with ERA 5 Across Selected Glacial Lakes
12.5.1 Lake-Specific Responses to Temperature Variability
12.5.2 Discrepancies in ERA5-Derived Temperatures
12.6 Discussion
12.7 Conclusion
Acknowledgements
Bibliography
Part 3: AI for Future Cryosphere Innovations
13. Artificial Intelligence for Ice Shelf and Sea Ice Thickness Estimation: Advances, Applications, and Future Perspectives
Mihir Lal
13.1 Introduction
13.1.1 Importance of Monitoring Ice Shelf and Sea Ice Thickness
13.1.2 Limitations of Traditional Measurement Techniques
13.1.3 The Role of AI in Transforming Cryospheric Observation
13.2 Remote Sensing Data Sources for Thickness Estimation
13.2.1 Key Satellite Missions: CryoSat-2, ICESat-2, Sentinel-1/2, MODIS, AMSR2
13.2.2 Ground-Based and Airborne Validation Datasets
13.2.3 Feature Extraction: Freeboard, Surface Roughness, Snow Depth, Brightness Temperature
13.3 AI and ML Techniques for Thickness Prediction
13.3.1 Machine Learning Models: Random Forest, Gradient Boosting, SVMs
13.3.2 Deep Learning Models: CNNs, LSTM, Feedforward Neural Networks
13.3.3 Multimodal Approaches: Fusion of SAR, Optical, and Altimetry Data Using AI
13.4 Case Studies and Applications
13.4.1 CryoSat-2 + Deep Neural Networks for Arctic Sea Ice Thickness
13.4.2 Antarctic Ice Shelf Thickness Estimation Using Multisensory AI Fusion
13.4.3 Regional Models for Thickness Mapping in Greenland, Weddell Sea, and Beaufort Gyre
13.5 Model Validation and Performance Metrics
13.5.1 Evaluation Using In Situ and Ice Bridge Measurements
13.5.2 Metrics: RMSE, MAE, R², Bias, and Uncertainty Estimation
13.5.3 Intercomparison with Physical Models and Forecast Systems
13.6 Challenges, Research Gaps, and Ethical Considerations
13.6.1 Data Scarcity, Seasonal Gaps, and Model Generalization Issues
13.6.2 Ethical Use of AI in Climate-Sensitive and Geopolitical Regions
13.7 Conclusions
References
14. Artificial Intelligence for Sea Ice Forecasting and Arctic Navigation: Techniques, Platforms, and Future Directions
Mihir Lal
14.1 Introduction to Sea Ice Forecasting and Arctic Navigation
14.1.1 Importance of Sea Ice Forecasting in a Warming Arctic
14.1.2 Challenges Faced by Traditional Forecasting Models
14.1.3 Role of AI in Improving Forecast Precision and Timeliness
14.2 Remote Sensing and Data Sources for AI-Based Forecasting
14.2.1 Satellite Missions: MODIS, AMSR2, CryoSat-2, Sentinel-1
14.2.2 Auxiliary Datasets: ERA5, Sea Surface Temperature, Wind, Pressure
14.2.3 Preprocessing Techniques: Cloud Masking, Normalization, Label Generation
14.3 Machine Learning and Deep Learning Models Used
14.3.1 Classical Models: Random Forest, Support Vector Machines
14.3.2 Temporal Models: LSTM, GRU for Sequence Prediction
14.3.3 Spatiotemporal Models: CNNs, Conv LSTM, Transformer-Based Approaches
14.4 Real-World Applications and Case Studies
14.4.1 NSIDC’s Sea Ice Forecast System Using LSTM-CNN
14.4.2 ESA CryoSat-2 + Deep Learning for Ice Thickness Mapping
14.4.3 AI-Supported Navigation in the Northern Sea Route
14.5 Integration with Navigation and Maritime Logistics
14.5.1 Sea Ice Route Optimization with AI-Enhanced Ice Charts
14.5.2 Decision-Support Systems for Polar Shipping and Logistics
14.6 Challenges, Limitations, and Ethical Concerns
14.6.1 Data Gaps and Seasonal Variability
14.6.2 Explainability, Model Generalization, and Risk of Misuse
14.6.3 Environmental, Economic, and Indigenous Implications of AI-Driven Navigation
14.7 Conclusion
References
15. Artificial Intelligence and Autonomous Systems in Polar
Expeditions and Scientific Field Research
Pradeep Shree Adhikari, Shubhi Sharma and Ruchi Sharma
15.1 Introduction
15.1.1 The Need for Automation in Polar Expeditions
15.1.2 Evolution from Manual Surveys to AI-Augmented Robotics
15.1.3 Objectives and Scope of the Chapter
15.2 Overview of Autonomous Systems in Polar Environments
15.2.1 Types of Platforms: UAVs, UGVs, AUVs, ROVs, Autonomous Stations
15.2.2 Key Capabilities: Navigation, Sensing, Energy Management
15.2.3 Environmental Constraints: Cold, Ice Load, Communication Blackouts
15.3 Role of AI in Enhancing Autonomous Functionality
15.3.1 Sensor Fusion and Perception: LiDAR, Radar, Thermal, Optical Integration
15.3.2 AI for Decision Making and Mission Planning
15.3.3 Learning-Based Control and Adaptation in Dynamic Polar Conditions
15.4 Applications in Polar Expedition and Field Research
15.4.1 Glacier and Ice Sheet Mapping with UAVs and AI Vision
15.4.2 Under-Ice Navigation for Sub-Ice Oceanography Using AUVs
15.4.3 Autonomous Weather Stations and Climate Monitoring with AI Anomaly Detection
15.5 Human-AI Collaboration and Field Deployment Strategies
15.5.1 Semi-Autonomous Systems and Human-in-the-Loop Control
15.5.2 Remote Operations, Edge AI, and Real-Time Feedback Loops
15.5.3 Safety, Redundancy, and Fail-Safe Design in Field Operations
15.6 Challenges, Risks, and Ethical Considerations
15.6.1 Hardware Limitations and Power Constraints in Extreme Cold
15.6.2 AI Transparency, Data Bias, and Algorithmic Failures
15.6.3 Environmental Impact, Data Sovereignty, and Indigenous Considerations
15.7 Conclusion
References
16. Artificial Intelligence for Predicting Future Cryosphere Changes
Sarita Naruka, Shubhi Sharma, Ruchi Sharma and Surendra Yadav
16.1 Introduction
16.1.1 The Cryosphere. Components and Significance
16.1.2 Impacts of Cryosphere Changes on Climate and Sea Levels
16.1.3 Challenges with Traditional Prediction and Monitoring Methods
16.2 AI: A Transformative Tool in Cryosphere Science
16.2.1 Advantages of AI Over Physical Modeling Approaches
16.2.2 Applications of AI in Environmental Forecasting
16.2.3 Role of AI in Improving Prediction Speed and Accuracy
16.3 Remote Sensing and Data Sources for AI Models
16.3.1 Satellite Data: MODIS, Sentinel, ICE Sat, CryoSat, Etc
16.3.2 Ground Measurements and Auxiliary Climate Variables
16.3.3 Preprocessing: Feature Extraction, Labeling, and Harmonization
16.4 AI Models and Techniques for Predictive Cryosphere Modeling
16.4.1 Classical Machine Learning Algorithms (RF, SVM, Decision Trees)
16.4.2 Deep Learning: CNN, LSTM, Transformer-Based Architectures
16.4.3 Model Validation, Performance Metrics, and Comparison
16.5 Applications and Case Studies
16.5.1 Sea Ice Forecasting in Arctic Regions
16.5.2 Himalayan Snow Cover Mapping Using Google Earth Engine
16.5.3 AI-Based Glacier Mass Balance and Meltwater Prediction
16.6 Technical, Scientific, and Ethical Challenges
16.6.1 Data Availability and Geographic Limitations
16.6.2 Interpretability, Explainability, and Black-Box Risks
16.6.3 Energy Consumption, Infrastructure, and Bias Concerns
16.7 Conclusion
References
17. Real-World Applications of Artificial Intelligence in Cryosphere Monitoring and Forecasting: Case Studies and Insights
Srinath Jettaboina and Amit Sharma
17.1 Introduction
17.1.1 Why Real-World Implementation Matters
17.1.2 Bridging the Gap Between Research and Practice
17.1.3 Scope and Objectives of the Chapter
17.2 AI in Sea Ice Monitoring and Prediction
17.2.1 Arctic Sea Ice Extent Forecasting Using LSTM-CNN Models
17.2.2 Ship Route Optimization with AI-Enhanced Sea Ice Maps
17.2.3 ESA’s CryoSat Data and Deep Learning for Ice Thickness Estimation
17.3 Glacier and Ice Sheet Applications
17.3.1 Automated Glacier Segmentation Using U-Net CNN
17.3.2 Glacier Mass Balance Estimation in the Alps and Himalayas
17.3.3 AI in Predicting Glacier Retreat Under Climate Scenarios
17.4 Snow Cover Mapping and Snowmelt Forecasting
17.4.1 High-Resolution Snow Classification with Random Forest in the Himalayas
17.4.2 Snowmelt Runoff Prediction Using LSTM and Reanalysis Data
17.4.3 AI-Powered Avalanche Risk Zoning Using Multisource Data
17.5 Permafrost and Subsurface Ice Monitoring
17.5.1 Machine Learning (ML) for Permafrost Zonation in Siberia and Alaska
17.5.2 Thermal Regime Forecasting Using AI Time Series Models
17.5.3 AI in Detecting Subsurface Ice from Satellite and Ground Data Fusion
17.6 Integrated Platforms and Operational Systems
17.6.1 Google Earth Engine + AI for Scalable Cryosphere Monitoring
17.6.2 Operational Use of AI by NSIDC and ESA in Decision Support
17.6.3 Emerging Tools: AI Dashboards, APIs, and Cloud Deployments
17.7 Conclusion
References
Index

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