Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring

Dear Colleagues,

Multi-source remote sensing images from satellite and airborne platforms are being acquired daily, including optical, multi-spectral, hyperspectral, LiDAR, SAR, etc. Due to the different imaging mechanisms, each source of imagery provides unique observation signals, enabling the accurate monitoring of the dynamic and complex earth environment from diverse perspectives. The integration of multi-source Earth observation data can improve the temporal-spatial-spectral resolution, accuracy, and data coverage of observations, allowing for fine-grained analyses of vegetation dynamics, forest health, species diversity, and biomass estimation.

As an ancient topic, multi-source remote sensing data fusion is now facing new challenges with the exponential growth of data volume and complexity. By addressing large-scale heterogeneous data, the differences in imaging mechanisms, resolutions, and environments become prominent. How to develop innovative techniques to address these challenges and serve for better Earth environment monitoring is an open question for remote sensing research.

This Special Issue aims at advancing innovative techniques or datasets for multi-source remote sensing data fusion, covering diverse applications that could help scientists and decision-makers to understand complex Earth system processes and to better respond to global environmental and climate change. We encourage the integration of recent deep learning techniques (large pre-trained multi-modal models, domain adaptation strategies, etc.) and large-scale applications (land-cover mapping, ecosystem monitoring, urban analysis, disaster assessment, etc.). The scope includes, but is not limited to, the following:

• Multi-source remote sensing data fusion;
• Multi-source transferable or domain adaptation models;
• Multi-modal models for remote sensing tasks;
• Land-cover mapping;
• Ecosystem monitoring;
• Urban analysis;
• Crop monitoring and yield forecasting;
• Glacier and sea ice monitoring;
• Atmospheric monitoring;
• Biodiversity and ecological conservation;
• Greenhouse gas emission monitoring;
• Vegetation dynamics and the carbon cycle.

Dr. Junjue Wang
Dr. Jiaqi Yang
Dr. Yinhe Liu
Dr. Zhuo Zheng
Dr. Yonghao Xu
Guest Editors

Mr. Weihao Xuan
Guest Editor Assistant
Affiliation: Graduate School of Frontier Sciences, The University of Tokyo & RIKEN Center for Advanced Intelligence Project, Chiba 277-8561, Japan
Email: [email protected]
Webpage: https://weihaoxuan.com/
Interests: foundation models for remote sensing data; multi-modal learning; vision-language models; point cloud analysis

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• multi-source remote sensing data fusion
• multi-source transferable or domain adaptation models
• multi-modal models for remote sensing tasks
• ecosystem monitoring
• urban analysis
• crop monitoring and yield forecasting
• glacier and sea ice monitoring
• atmospheric monitoring
• greenhouse gas emission monitoring
• vegetation dynamics and carbon cycle