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Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 10552

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Dr. Andrés Navarro

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Institute of Environmental Sciences (ICAM), University of Castilla-La Mancha (UCLM), 45071 Toledo, Spain
Interests: precipitation science; remote sensing of precipitation; extreme precipitation events
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Special Issue Information

Dear colleagues,

Floods and droughts are two of the most devastating natural hazards affecting populations, property and infrastructure. The World Bank estimates that at least 1.65 billion people have been affected by floods and 1.43 billion by droughts in the last two decades. Economic losses and damages are also significant, averaging USD 178 billion per year. Climate change is expected to increase the frequency and intensity of these events, making it more important than ever to develop effective strategies for their monitoring and management.

Remote sensing (RS) has become an essential tool for assessing these hydro-climatic risks, providing timely and accurate information on their extent, severity, and impact over large areas and at regular intervals. This information can be used to support a variety of activities, including (1) climate monitoring; (2) early warning systems; (3) emergency response; (4) recovery efforts; and (5) risk assessment and management.

This Special Issue welcomes papers that deal primarily with RS applied to hydro-climate risks, but also use modeling and ground observations for illustrative purposes (e.g., validation). Manuscripts on applications of RS to the study of single events and regional analysis will also be welcome. Case studies and papers on early warning, monitoring, and disaster management are also welcome.

The scope of this Special Issue is very broad, and there are many other topics that could be relevant to this SI, including insurance, agriculture, infrastructure, and human health.

Dr. Andrés Navarro
Guest Editor

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.

Keywords

precipitation
floods
droughts
extreme precipitation events
natural hazards
hydrology

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Published Papers (10 papers)

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Research

21 pages, 6990 KiB

Open AccessArticle

Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery

by Reda Amer

Remote Sens. 2025, 17(11), 1869; https://doi.org/10.3390/rs17111869 - 28 May 2025

Viewed by 254

Abstract

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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36 pages, 10251 KiB

Open AccessArticle

Integrating Advanced Sensor Technologies for Enhanced Agricultural Weather Forecasts and Irrigation Advisories: The MAGDA Project Approach

by Martina Lagasio, Stefano Barindelli, Zenaida Chitu, Sergio Contreras, Amelia Fernández-Rodríguez, Martijn de Klerk, Alessandro Fumagalli, Andrea Gatti, Lukas Hammerschmidt, Damir Haskovic, Massimo Milelli, Elena Oberto, Irina Ontel, Julien Orensanz, Fabiola Ramelli, Francesco Uboldi, Aso Validi and Eugenio Realini

Remote Sens. 2025, 17(11), 1855; https://doi.org/10.3390/rs17111855 - 26 May 2025

Viewed by 302

Abstract

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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23 pages, 4743 KiB

Open AccessArticle

Utilizing Remote Sensing for Sponge City Development: Enhancing Flood Management and Urban Resilience in Karachi

by Asifa Iqbal, Lubaina Soni, Ammad Waheed Qazi and Humaira Nazir

Remote Sens. 2025, 17(11), 1818; https://doi.org/10.3390/rs17111818 - 23 May 2025

Viewed by 1172

Abstract

Rapid urbanization in Karachi, Pakistan, has resulted in increased impervious surfaces, leading to significant challenges, such as frequent flooding, urban heat islands, and loss of vegetation. These issues pose challenges to urban resilience, livability, and sustainability, which further demand solutions that incorporate urban greening and effective water management. This research uses remote sensing technologies and Geographic Information Systems (GISs), to analyze current surface treatments and their relationship to Karachi’s blue-green infrastructure. By following this approach, we evaluate flood risk and identify key flood-conditioning factors, including elevation, slope, rainfall distribution, drainage density, and land use/land cover changes. By utilizing the Analytical Hierarchy Process (AHP), we develop a flood risk assessment framework and a comprehensive flood risk map. Additionally, this research proposes an innovative Sponge City (SC) framework that integrates nature-based solutions (NBS) into urban planning, especially advocating for the establishment of green infrastructure, such as green roofs, rain gardens, and vegetated parks, to enhance water retention and drainage capacity. The findings highlight the urgent need for targeted policies and stakeholder engagement strategies to implement sustainable urban greening practices that address flooding and enhance the livability of Karachi. This work not only advances the theoretical understanding of Sponge Cities but also provides practical insights for policymakers, urban planners, and local communities facing similar sustainability challenges. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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23 pages, 51122 KiB

Open AccessArticle

Research on the Response Mechanism of Vegetation to Drought Stress in the West Liao River Basin, China

by Yuhong Tian, Huichao Zheng, Mengxuan Yan and Lizhu Wu

Remote Sens. 2025, 17(10), 1780; https://doi.org/10.3390/rs17101780 - 20 May 2025

Viewed by 243

Abstract

Understanding vegetation’s drought response helps predict ecosystem adaptations to climate change and offers scientific insights for managing extreme climate events. Using RS technology, this study systematically investigates the response mechanisms of vegetation to drought and their spatiotemporal variations in the ecologically sensitive semi-arid area and the national grain security zone—West Liao River Basin, China. The findings reveal that (1) from 2000 to 2018, NDVI exhibited a fluctuating upward trend, and drought trends remained pronounced in certain areas and seasons; (2) growing-season droughts impaired productivity, while winter droughts reduced soil moisture, with arid-zone vegetation being most vulnerable; (3) grasslands responded rapidly to drought, forests slowly via deep roots, and croplands suffered most during critical growth phases; and (4) drought-adapted western forests/shrubs recovered best, while eastern croplands required targeted measures like resilient crops and water management. The results of this study not only provide a scientific basis for ecological management in the West Liao River Basin but also offer valuable insights for vegetation and water resource management in other arid and semi-arid regions globally. This research holds significant importance for addressing climate change and achieving regional sustainable development. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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21 pages, 5836 KiB

Open AccessArticle

Application of Remote Sensing Floodplain Vegetation Data in a Dynamic Roughness Distributed Runoff Model

by Andre A. Fortes, Masakazu Hashimoto and Keiko Udo

Remote Sens. 2025, 17(10), 1672; https://doi.org/10.3390/rs17101672 - 9 May 2025

Viewed by 336

Abstract

Riparian vegetation reduces the conveyance capacity and increases the likelihood of floods. Studies that consider vegetation in flow modeling rely on unmanned aerial vehicle (UAV) data, which restrict the covered area. In contrast, this study explores advances in remote sensing and machine learning techniques to obtain vegetation data for an entire river by relying solely on satellite data, superior to UAVs in terms of spatial coverage, temporal frequency, and cost effectiveness. This study proposes a machine learning method to obtain key vegetation parameters at a resolution of 10 m. The goal was to evaluate the applicability of remotely sensed vegetation data using the proposed method on a dynamic roughness distributed runoff model in the Abukuma River to assess the effect of vegetation on the typhoon Hagibis flood (12 October 2019). Two machine learning models were trained to obtain vegetation height and density using different satellite sources, and the parameters were mapped in the river floodplains with 10 m resolution based on Sentinel-2 imagery. The vegetation parameters were successfully estimated, with the vegetation height overestimated in the urban areas, particularly in the downstream part of the river, then integrated into a dynamic roughness calculation routine and patched into the RRI model. The simulations with and without vegetation were also compared. The machine learning models for density and height obtained fair results, with an R² of 0.62 and 0.55, respectively, and a slight overestimation of height. The results showed a considerable increase in water depth (up to 17.7% at the Fushiguro station) and a decrease in discharge (28.1% at the Tateyama station) when vegetation was considered. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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21 pages, 7421 KiB

Open AccessArticle

Study on the Spatial Distribution Patterns and Driving Forces of Rainstorm-Induced Flash Flood in the Yarlung Tsangpo River Basin

by Fei He, Chaolei Zheng, Xingguo Mo, Zhonggen Wang and Suxia Liu

Remote Sens. 2025, 17(8), 1393; https://doi.org/10.3390/rs17081393 - 14 Apr 2025

Viewed by 343

Abstract

Flash floods, typically triggered by natural events such as heavy rainfall, snowmelt, and dam failures, are characterized by abrupt onset, destructive power, unpredictability, and challenges in mitigation. This study investigates the spatial distribution patterns and driving mechanisms of rainstorm-induced flash flood disasters in the Yarlung Tsangpo River Basin (YTRB) by integrating topography, hydrometeorology, human activity data, and historical disaster records. Through a multi-method spatial analysis framework—including kernel density estimation, standard deviation ellipse, spatial autocorrelation (Moran’s I and Getis–Ord Gi*), and the optimal parameter geographic detector (OPGD) model (integrating univariate analysis and interaction detection)—we reveal multiscale disaster dynamics across county, township, and small catchment levels. Key findings indicate that finer spatial resolution (e.g., small catchment scale) enhances precision when identifying high-risk zones. Temporally, the number of rainstorm-induced flash floods increased significantly and disaster-affected areas expanded significantly from the 1980s to the 2010s, with a peak spatial dispersion observed during 2010–2019, reflecting a westward shift in disaster distribution. Spatial aggregation of flash floods persisted throughout the study period, concentrated in the central basin. Village density (TD) was identified as the predominant human activity factor, exhibiting nonlinear amplification through interactions with short-duration heavy rainfall (particularly 3 h [P3] and 6 h [P6] maximum precipitations) and GDP. These precipitation durations demonstrated compounding risk effects, where sustained rainfall intensity progressively heightened disaster potential. Topographic and ecological interactions, particularly between elevation (DEM) and vegetation type (VT), further modulate disaster intensity. These findings provide critical insights for risk zonation and targeted prevention strategies in high-altitude river basins. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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31 pages, 21480 KiB

Open AccessArticle

SSegRef2Surf—Near Real-Time Photogrammetric Flood Monitoring and Refinement of Classified Water Surfaces

by Michael Kögel, Lilly Feile, Fabian Möldner and Dirk Carstensen

Remote Sens. 2025, 17(8), 1351; https://doi.org/10.3390/rs17081351 - 10 Apr 2025

Viewed by 382

Abstract

Effective response to flood events requires high-resolution, frequently updated data on flooded areas for comprehensive flood risk assessments. Unmanned aerial vehicles (UAVs) equipped with conventional camera systems and classification based on orthophotos from photogrammetric postprocessing and artificial intelligence are widely used to detect flooded areas. However, these methods often involve time-intensive pre- and postprocessing steps and fail to incorporate geometric factors such as elevation data and water depths. This study introduces SSegRef2Surf, a novel tool that integrates classified flood raster data with terrain information. SSegRef2Surf refines and optimizes coarse raster classifications by filling shadowed areas and correcting misclassified regions. This tool reduces data requirements for AI training and minimizes postprocessing time, enabling near real-time flood monitoring. All processes necessary for SSegRef2Surf were optimized through sensitivity and accuracy analyses to reduce postprocessing duration to a minimum. A comparison of the SSegRef2Surf results with two-dimensional (2D) numerical model results for a flood event revealed discrepancies in the 2D model, caused by inaccuracies in the underlying terrain data. This comparison showed that 30% of the flooded areas identified in the 2D numerical results were incorrect, while missing areas (11%) were added. This highlights the significant potential of SSegRef2Surf for near real-time flood monitoring and traceability of flood events, as combining UAVs’ high-frequency surveying capabilities with SSegRef2Surf allows for more effective validation and optimization of 2D models. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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29 pages, 13136 KiB

Open AccessArticle

Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco

by Imane Serbouti, Jérôme Chenal, Biswajeet Pradhan, El Bachir Diop, Rida Azmi, Seyid Abdellahi Ebnou Abdem, Meriem Adraoui, Mohammed Hlal and Mariem Bounabi

Remote Sens. 2024, 16(18), 3398; https://doi.org/10.3390/rs16183398 - 12 Sep 2024

Cited by 1 | Viewed by 2068

Abstract

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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21 pages, 10773 KiB

Open AccessArticle

A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods

by Meriam Lahsaini, Felice Albano, Raffaele Albano, Arianna Mazzariello and Teodosio Lacava

Remote Sens. 2024, 16(12), 2193; https://doi.org/10.3390/rs16122193 - 17 Jun 2024

Cited by 4 | Viewed by 2140

Abstract

Satellite data have been widely utilized for flood detection and mapping tasks, and in recent years, there has been a growing interest in using Synthetic Aperture Radar (SAR) data due to the increased availability of recent missions with enhanced temporal resolution. This capability, when combined with the inherent advantages of SAR technology over optical sensors, such as spatial resolution and independence from weather conditions, allows for timely and accurate information on flood event dynamics. In this study, we present an innovative automated approach, SAR-RST-FLOOD, for mapping flooded areas using SAR data. Based on a multi-temporal analysis of Sentinel 1 data, such an approach would allow for robust and automatic identification of flooded areas. To assess its reliability and accuracy, we analyzed five case studies in areas where floods caused significant damage. Performance metrics, such as overall (OA), user (UA), and producer (PA) accuracy, as well as the Kappa index (K), were used to evaluate the methodology by considering several reference flood maps. The results demonstrate a user accuracy exceeding 0.78 for each test map when compared to the observed flood data. Additionally, the overall accuracy values surpassed 0.96, and the kappa index values exceeded 0.78 when compared to the mapping processes from observed data or other reference datasets from the Copernicus Emergency Management System. Considering these results and the fact that the proposed approach has been implemented within the Google Earth Engine framework, its potential for global-scale applications is evident. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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27 pages, 6482 KiB

Open AccessArticle

Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia

by Karam Alsafadi, Bashar Bashir, Safwan Mohammed, Hazem Ghassan Abdo, Ali Mokhtar, Abdullah Alsalman and Wenzhi Cao

Remote Sens. 2024, 16(7), 1179; https://doi.org/10.3390/rs16071179 - 28 Mar 2024

Cited by 5 | Viewed by 2177

Abstract

Global warming has resulted in increases in the intensity, frequency, and duration of drought in most land areas at the regional and global scales. Nevertheless, comprehensive understanding of how water use efficiency (WUE), gross primary production (GPP), and actual evapotranspiration (AET)-induced water losses respond to exceptional drought and whether the responses are influenced by drought severity (DS) is still limited. Herein, we assess the fluctuation in the standardized precipitation evapotranspiration index (SPEI) over the Middle East from 1982 to 2017 to detect the drought events and further examine standardized anomalies of GPP, WUE, and AET responses to multiyear exceptional droughts, which are separated into five groups designed to characterize the severity of extreme drought. The intensification of the five drought events (based on its DS) increased the WUE, decreased the GPP and AET from D5 to D1, where both the positive and negative variance among the DS group was statistically significant. The results showed that the positive values of standardized WUE with the corresponding values of the negative GPP and AET were dominant (44.3% of the study area), where the AET values decreased more than the GPP, and the WUE fluctuation in this region is mostly controlled by physical processes, i.e., evaporation. Drought’s consequences on ecosystem carbon-water interactions ranged significantly among eco-system types due to the unique hydrothermal conditions of each biome. Our study indicates that forthcoming droughts, along with heightened climate variability, pose increased risks to semi-arid and sub-humid ecosystems, potentially leading to biome restructuring, starting with low-productivity, water-sensitive grasslands. Our assessment of WUE enhances understanding of water-carbon cycle linkages and aids in projecting ecosystem responses to climate change. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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