Storms and Floods Analysis Based on the Fusion of Satellite, Meteorological and Ground Station Observation Data

Dear Colleagues,

Floods and storms are among the most devastating natural hazards, with increasing frequency and intensity due to climate change and anthropogenic activities. The accurate monitoring, prediction, and risk assessment of these events require integrating multi-source data, including satellite remote sensing, meteorological models, and ground-based observations. This Special Issue aims to showcase innovative research focused on advanced methodologies and applications for analyzing flood and storm hazards through the synergistic fusion of these diverse datasets.

This Special Issue aims to focus on advancing the integration of multi-source Earth observation data for the improved monitoring, analysis, mechanisms, and prediction of flood and storm hazards. As climate change exacerbates the frequency and severity of these events, there is an urgent need to leverage synergies across satellite remote sensing, meteorological models, and ground-based measurements to enhance hazard assessment and early warning systems.

This Special Issue aligns closely with the scope of Remote Sensing by emphasizing innovative geospatial technologies, data fusion methodologies, and applications of remote sensing for environmental monitoring and disaster risk reduction. We seek the submission of contributions that demonstrate how integrated observational datasets can overcome the limitations of individual systems (e.g., spatial coverage, temporal resolution, or accuracy) to provide improved, actionable insights for policymakers and practitioners.

We invite contributions that address challenges and opportunities in data fusion, such as improving spatiotemporal resolution, strengthening physical understanding, enhancing predictive accuracy, and developing early warning systems. Topics of interest for this Special Issue include, but are not limited to, the following:

• Multi-source data integration: Novel techniques for merging satellite (e.g., SAR, optical, and LiDAR), meteorological (e.g., reanalysis, radar), and ground station data (e.g., river gauges, weather stations).
• Machine learning and AI: Applications of deep learning, ensemble methods, or hybrid models for hazard prediction and impact assessment.
• Climate change impacts: Trends in flood and storm patterns and their linkages to climatic drivers.
• Urban flood modeling: High-resolution inundation mapping and risk evaluation in urban areas.
• Early warning systems: Real-time monitoring and decision support tools for disaster preparedness.
• Uncertainty quantification: Methods to assess and increase robustness in fused datasets.
• Case studies: Innovative case studies demonstrating how data fusion generates transformative advances in storm and flood disaster management.

All types of submissions are welcome, including articles, reviews, technical notes, and communications. Priority will be given to research that (1) develops original fusion techniques addressing specific limitations in current systems; (2) quantifies measurable improvements; and (3) delivers transferable solutions for high-impact scenarios with clear implications for operational disaster management. Studies merely applying existing methods to new regions will not be considered for publication.

Dr. Jingyu Wang
Dr. Xiaodong Chen
Prof. Dr. Donglian Sun
Dr. Zizhen Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• remote sensing
• flood monitoring
• storm tracking
• data fusion
• extreme event analysis
• AI and machine learning
• disaster risk reduction