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Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition)

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: 30 June 2025 | Viewed by 6713

Special Issue Editors

Dr. Valeria Satriano

E-Mail Website
Guest Editor
Institute of Methodologies for Environmental Analysis, National Research Council (CNR-IMAA), 85050 Tito Scalo, Italy
Interests: satellite data management and processing algorithms; optical remote sensing for natural and anthropic risk
Special Issues, Collections and Topics in MDPI journals
Dr. Mariapia Faruolo

E-Mail Website
Guest Editor
Institute of Methodologies for Environmental Analysis, National Research Council (CNR-IMAA), 85050 Tito Scalo, Italy
Interests: satellite data processing; satellite methods for environmental and anthropic risks investigation
Special Issues, Collections and Topics in MDPI journals
Dr. Sandro Martinis

E-Mail Website
Guest Editor
German Remote Sensing Data Center, German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany
Interests: earth observation; sar and optical imagery; natural hazards; civil security; disaster response

Special Issue Information

Dear Colleagues,

In the context of increasingly larger and more disastrous flood events, Earth observation plays a role of primary importance in the relative risk monitoring and management. During the emergency phases related to the occurrence of such events, the authorities’ decision-making process inevitably occurs via the analysis of information retrieved by main optical and microwave satellite sensors. Their ability to observe large areas in a short time allows for prompt and effective action being taken to both safe human lives and reduce the damages to properties and the environment. Remote sensing technologies and techniques have greatly improved in recent years, providing increasingly accurate and efficient information.

This Special Issue will accept studies regarding advances in Earth observation for flood detection, monitoring, and management via satellite data acquired from different optical and microwave sensors. Works using advanced satellite based techniques, in situ modeling methodologies, and machine learning are welcomed.

Dr. Valeria Satriano
Dr. Mariapia Faruolo
Dr. Sandro Martinis
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.

Keywords

  • flood disaster
  • flood monitoring and management
  • remote sensing
  • modelling
  • machine learning

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Related Special Issue

Published Papers (4 papers)

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Research

39 pages, 10616 KiB  
Article
Ensemble Learning for Urban Flood Segmentation Through the Fusion of Multi-Spectral Satellite Data with Water Spectral Indices Using Row-Wise Cross Attention
by Han Xu and Alan Woodley
Remote Sens. 2025, 17(1), 90; https://doi.org/10.3390/rs17010090 - 29 Dec 2024
Viewed by 1017
Abstract
In post-flood disaster analysis, accurate flood mapping in complex riverine urban areas is critical for effective flood risk management. Recent studies have explored the use of water-related spectral indices derived from satellite imagery combined with machine learning (ML) models to achieve this purpose. [...] Read more.
In post-flood disaster analysis, accurate flood mapping in complex riverine urban areas is critical for effective flood risk management. Recent studies have explored the use of water-related spectral indices derived from satellite imagery combined with machine learning (ML) models to achieve this purpose. However, relying solely on spectral indices can lead these models to overlook crucial urban contextual features, making it difficult to distinguish inundated areas from other similar features like shadows or wet roads. To address this, our research explores a novel approach to improve flood segmentation by integrating a row-wise cross attention (CA) module with ML ensemble learning. We apply this method to the analysis of the Brisbane Floods of 2022, utilizing 4-band satellite imagery from PlanetScope and derived spectral indices. Applied as a pre-processing step, the CA module fuses a spectral band index into each band of a peak-flood satellite image using a row-wise operation. This process amplifies subtle differences between floodwater and other urban characteristics while preserving complete landscape information. The CA-fused datasets are then fed into our proposed ensemble model, which is constructed using four classic ML models. A soft voting strategy averages their binary predictions to determine the final classification for each pixel. Our research demonstrates that CA datasets can enhance the sensitivity of individual ML models to floodwater in complex riverine urban areas, generally improving flood mapping accuracy. The experimental results reveal that the ensemble model achieves high accuracy (approaching 100%) on each CA dataset. However, this may be affected by overfitting, which indicates that evaluating the model on additional datasets may lead to reduced accuracy. This study encourages further research to optimize the model and validate its generalizability in various urban contexts. Full article
(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition))
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29 pages, 65789 KiB  
Article
Near Real-Time Flood Monitoring Using Multi-Sensor Optical Imagery and Machine Learning by GEE: An Automatic Feature-Based Multi-Class Classification Approach
by Hadi Farhadi, Hamid Ebadi, Abbas Kiani and Ali Asgary
Remote Sens. 2024, 16(23), 4454; https://doi.org/10.3390/rs16234454 - 27 Nov 2024
Cited by 3 | Viewed by 1831
Abstract
Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for [...] Read more.
Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method’s accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery. Full article
(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition))
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27 pages, 25812 KiB  
Article
Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model
by Amirhossein Rostami, Chi-Hung Chang, Hyongki Lee, Hung-Hsien Wan, Tien Le Thuy Du, Kel N. Markert, Gustavious P. Williams, E. James Nelson, Sanmei Li, William Straka III, Sean Helfrich and Angelica L. Gutierrez
Remote Sens. 2024, 16(23), 4357; https://doi.org/10.3390/rs16234357 - 22 Nov 2024
Cited by 1 | Viewed by 1279
Abstract
Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms [...] Read more.
Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70–80% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER’s versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern. Full article
(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition))
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23 pages, 10174 KiB  
Article
A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event
by Valeria Satriano, Emanuele Ciancia, Nicola Pergola and Valerio Tramutoli
Remote Sens. 2024, 16(18), 3450; https://doi.org/10.3390/rs16183450 - 17 Sep 2024
Viewed by 1891
Abstract
Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human [...] Read more.
Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time. Full article
(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition))
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