Remote Sensing of Global Floods: Observing, Modelling, and Forecasting

Dear Colleagues,

Floods threaten urban and agricultural communities and ecosystems. The escalating humanitarian and financial damages underscore the imperative need for flood management, control, and mitigation strategies. Geospatial science is a pivotal player in these efforts, mainly using a non-structural approach. Flood monitoring enabled by spaceborne observations coupled with artificial intelligence algorithms has transformative potential in designing modern early detection, flood response, and management systems. Satellite-based flood monitoring enables rapid responses by swiftly identifying flood-affected areas. The integration of artificial intelligence algorithms enhances the accuracy and efficiency of flood detection, providing a timely understanding of the spatial extent and severity of the inundation. This synergy between geospatial technologies and artificial intelligence facilitates the creation of spatial models that elucidate the relationships between floods and various contributing factors through geographic information systems (GISs) analysis. GISs, combined with diverse methodologies, including artificial intelligence (machine/deep learning), statistical methods, and multi-criteria decision-making techniques, enable the development of comprehensive and more accurate models. These models facilitate the creation of maps depicting flood susceptibility, vulnerability, risk, hazard, and resilience. By integrating information from satellite observations and spatial models, flood management strategies can be informed by a nuanced understanding of the complex interplay between environmental factors and flood occurrence. Furthermore, satellite observation and spatial modeling insights allow future floods to be predicted. This predictive capability is instrumental in designing robust flood warning systems that can forewarn communities and authorities, offering valuable time for preparedness and response.

The primary aim of this Special Issue is to advance geospatial science’s role in flood management. With a specific focus on satellite observations and artificial intelligence algorithms, the goal is to showcase innovative research contributing to efficient flood monitoring. This Special Issue seeks to facilitate interdisciplinary discussions, highlighting novel methodologies and spatial modeling techniques utilizing GISs, artificial intelligence, statistical methods, and multi-criteria decision making. With this goal in mind, this Special Issue aims to significantly contribute to improving flood mitigation.

In this context, this Special Issue invites expert contributions from around the world exploring topics such as:

• Remote sensing technologies for flood monitoring;
• Machine/deep learning applications for flood detection;
• The spatial modeling of floods using different techniques;
• The integration of GIS with artificial intelligence for flood susceptibility mapping (FSM);
• Flood vulnerability, risk, and hazard assessment;
• Predictive modeling and future flood trends;
• The application of digital technologies in flood risk management (virtual reality (VR), augmented reality (AR), and digital twin (DT));
• The design of flood warning systems.

Dr. Seyed Vahid Razavi-Termeh
Dr. Khabat Khosravi
Dr. Renato Frasson
Dr. Guy Jean Pierre Schumann
Guest Editors

Manuscript Submission Information

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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.

• flood detection
• synthetic aperture radar (SAR) imagery
• artificial intelligence
• spatial modelling
• urban flood risk
• flood susceptibility mapping
• flood vulnerability and resilience
• uncertainties in flood risk assessments
• digital technologies in flood risk management
• flood warning systems