Remote Sensing and Data Assimilation in Flood Modeling and Prediction
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 100
Editors
Interests: weather radar; urban flooding; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence (AI); numerical weather prediction (NWP); weather radar
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; water resources; environment science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Flooding remains one of the most destructive natural hazards globally, amplified by intensifying climate extremes and rapid urbanization. Accurate, real-time flood forecasting is vital for disaster mitigation, yet traditional hydrological models often struggle with high uncertainties in initial conditions, rainfall inputs, and boundary parameters. Integrating advanced remote sensing observations—such as multi-band radar rainfall estimates, high-resolution satellite imagery, and inundation extent mapping—with cutting-edge data assimilation (DA) techniques offers a transformative pathway. This synergy drastically improves model calibration, state estimation, and lead-time accuracy, bridging the gap between raw Earth observation data and actionable early-warning intelligence.
This Special Issue, “Remote Sensing and Data Assimilation in Flood Modeling and Prediction,” aims to gather state-of-the-art research that bridges multi-source remote sensing with advanced hydrodynamic and hydrological modeling. It directly aligns with the journal’s scope by publishing innovative methodologies, algorithm developments, and real-world case studies that advance our quantitative understanding of hydrological extremes. Submissions focusing on uncertainty propagation, GPU-accelerated coupled modeling, and assimilation frameworks (e.g., EnKF, particle filters) are particularly welcome.
Suggested themes
- Advanced radar-rainfall error characterization and multi-band/multi-mode radar data assimilation in catchment and urban flood models.
- Merging high-resolution satellite precipitation products with ground-based radar networks for improved flood forecasting.
- Uncertainty quantification and error propagation laws stemming from radar-derived rainfall inputs into hydrodynamic simulations.
- Machine learning and ensemble-based data assimilation (e.g., EnKF) for radar and satellite rainfall fields.
Article Types
- Original Research Articles: Comprehensive studies introducing novel assimilation methodologies or precipitation merging frameworks.
- Review Articles: Critical overviews summarizing progress, challenges, and future trends in radar and satellite-based hydrological forecasting.
- Technical Notes: Focused reports on new open-source algorithms, radar data quality control, or precipitation uncertainty models.
Prof. Dr. Dehua Zhu
Dr. Yunqing Xuan
Prof. Dr. Dingzhi Peng
Dr. Gaoxu Wang
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-anonymized 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 modeling
- weather radar precipitation
- numerical weather prediction
- flood risk management
- probabilistic flood forecasting
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