Mishra et al. (2026) Editorial: Remote sensing and data science for mapping climate change impacts in mountainous regions

Identification

• Journal: Frontiers in Environmental Science
• Year: 2026
• Date: 2026-09-07
• Authors: Bhogendra Mishra, Manoj Kumar, Daniela Stroppiana
• DOI: 10.3389/fenvs.2026.1940158

Research Groups

• Science Hub, Kathmandu, Nepal
• Centre of Excellence on Sustainable Land Management, Indian Council of Forestry Research and Education, Dehradun, India
• Consiglio Nazionale delle Ricerche, Area della Ricerca Milano 1, Milan, Italy

Short Summary

This editorial presents six contributions that applied remote sensing and machine learning to mountain environments, highlighting the value of combining optical and microwave sensors to overcome cloud cover in monsoon regions.

Objective

• Investigate the impact of climate change on mountainous regions using remote sensing and data science techniques.

Study Configuration

• Spatial Scale: Mountainous regions worldwide.
• Temporal Scale: Long-term monitoring (e.g., 10-20 years).

Methodology and Data

• Models used: Machine learning algorithms such as Random Forest, XGBoost, ANN, DLNN, SVR, and ensemble combinations thereof.
• Data sources: Multi-source Earth Observation data, including optical, thermal, and SAR sensors.

Main Results

• The studies highlight the value of combining optical and microwave sensors to overcome cloud cover in monsoon regions.
• The groundwater and flood studies show that both water shortages and floods are getting worse at the same time due to changing rainfall and melting glaciers.
• The vegetation studies show that plant health is harder to judge than it looks, with simple greenness measurements being misleading.

Contributions

• This research topic presents methodological trends that extend beyond any single study, including the use of hybrid designs combining multi-source Earth Observation data with machine learning or deep learning architectures.
• The studies highlight the importance of interpretability and comparative model benchmarking in decision-ready outputs.

Funding

• No funding was received for this work.

Citation

@article{Mishra2026Editorial,
  author = {Mishra, Bhogendra and Kumar, Manoj and Stroppiana, Daniela},
  title = {Editorial: Remote sensing and data science for mapping climate change impacts in mountainous regions},
  journal = {Frontiers in Environmental Science},
  year = {2026},
  doi = {10.3389/fenvs.2026.1940158},
  url = {https://doi.org/10.3389/fenvs.2026.1940158}
}