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

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 924

Special Issue Editors

Dr. Jingyu Wang

E-Mail Website
Guest Editor
National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
Interests: regional and global climate modeling and applications; severe convective storms and hazards; meteorological instrumentation; land–atmosphere interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaodong Chen

E-Mail Website
Guest Editor
School of Meteorology, University of Oklahoma, Norman, OK 73072, USA
Interests: hydroclimate extreme events (extreme precipitation and flooding); regional climate modeling and applications; machine learning and neural hydrology; engineering hydrometeorology
Prof. Dr. Donglian Sun

E-Mail Website
Guest Editor
College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
Interests: land surface temperature retrieval; satellite-based observations of flood and standing water; seasonal prediction of tropical climate
Special Issues, Collections and Topics in MDPI journals
Dr. Zizhen Dong

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, Yunnan University, Kunming 650500, China
Interests: climate dynamics; East Asian monsoon variability; weather and climate extremes; climate change

Special Issue Information

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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

33 pages, 6401 KB  
Article
An Explainable Machine Learning Framework for Flood Damage Mapping Using Remote Sensing and Ground-Based Data: Application to the Basilicata Ionian Coast (Italy)
by Silvano Fortunato Dal Sasso, Maríca Rondinone, Htay Htay Aung and Vito Telesca
Remote Sens. 2026, 18(8), 1257; https://doi.org/10.3390/rs18081257 - 21 Apr 2026
Abstract
Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical [...] Read more.
Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article
(This article belongs to the Special Issue Storms and Floods Analysis Based on the Fusion of Satellite, Meteorological and Ground Station Observation Data)
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20 pages, 12216 KB  
Article
Identifying Paddy Rice Fields in the U.S. from the Operational VIIRS Flood Products
by Tianshu Yang, Satya Kalluri, Andrew Lomax and Donglian Sun
Remote Sens. 2026, 18(4), 587; https://doi.org/10.3390/rs18040587 - 13 Feb 2026
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Abstract
Operational satellite-based flood products are generated by comparing water classification maps from satellite imagery with permanent or normal water masks. This approach may misclassify some water bodies—such as irrigated paddy rice fields—as floodwaters because they are not masked as permanent or normal water [...] Read more.
Operational satellite-based flood products are generated by comparing water classification maps from satellite imagery with permanent or normal water masks. This approach may misclassify some water bodies—such as irrigated paddy rice fields—as floodwaters because they are not masked as permanent or normal water sources. Due to the importance of paddy fields for food security, in this study, methodologies based on the long-time duration of water presence combined with paddy rice phenological algorithms and change detection analysis are developed to extract paddy rice fields from the operational VIIRS (Visible Infrared Imaging Radiometer Suite) flood products. This method is also compared with the regression analysis and the Mann–Kendall analysis. Evaluations are performed through confusion matrix analysis by comparing with the USDA rice data. The three paddy rice extraction algorithms show good agreement and can achieve an accuracy of 93% with an F1-score exceeding 80%. Full article
(This article belongs to the Special Issue Storms and Floods Analysis Based on the Fusion of Satellite, Meteorological and Ground Station Observation Data)
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