Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing of Floods: Progress, Challenges and Opportunities

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 12199

Share This Special Issue

Special Issue Editors

Dr. A. K. M. Azad Hossain

E-Mail Website
Guest Editor

Department of Biology, Geology and Environmental Science, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
Interests: multispectral, hyperspectral, and microwave remote sensing; GIS, spatial analysis, and numerical modelling; water quality, floods, urban growth, urban heat island impact, environmental sustainability, landslides, soil moisture and climate change

Dr. Taufique Mahmood

E-Mail Website
Guest Editor

Harold Hamm School of Geology and Geological Engineering, University of North Dakota, Grand Forks, ND 58202, USA
Interests: remote sensing; hydrology; water quality

Special Issue Information

Dear Colleagues,

Remote sensing has been used for mapping and monitoring floods for many years. Studying floods is considered as one of the most common and useful applications of remote sensing. With their respective advantages and limitations, optical and microwave remote sensing techniques have been used to study floods in different parts of the world. Their advantages and limitations depend on the physiography, land use and land cover, and climate of the areas studied, and the specific characteristics of the remote sensing systems and sensors used. The recent progress in remote sensing technology, computing, and machine learning algorithms has provided tremendous opportunities to minimize the previously encountered challenges in mapping and monitoring floods. However, these advancements and scopes are not properly researched and documented. Therefore, this Special Issue of Remote Sensing aims to invite original research and review articles on the progress, challenges, and opportunities in remote sensing of floods. The potential topics are as follows:

Mapping and monitoring floods using multi-sensor remote sensing;
Mapping and monitoring floods using sensor fusion;
Mapping flood dynamics using remote sensing;
Application of microwave and optical remote sensing for studying riverine flooding;
Application of microwave and optical remote sensing for studying coastal flooding;
Application of remote sensing for studying flash flooding;
Machine learning/deep learning in remote sensing of floods;
Remote sensing of floods using moderate and high-resolution imagery;
Remote sensing of floods using aerial/air-born/UAV imagery;
Integration of remote sensing with hydrodynamic models for flood simulation and prediction.

Dr. A. K. M. Azad Hossain
Dr. Taufique Mahmood
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

floods
flood monitoring
flood impact
flood damage
remote sensing
progress, challenges, and opportunities
inland flooding
urban flooding
flash flooding
coastal flooding
optical remote sensing
radar remote sensing
hyperspectral remote sensing
spatial resolution
high/moderate/low spatial resolution
temporal resolution
high/moderate/low temporal resolution
space-borne remote sensing
air-borne remote sensing
drone
unmanned aerial vehicle (UAV)
rainfall and runoff
hydrology
geospatial technology
spatial analysis
numerical model
hydrodynamic model
spatial decision support systems (SDSS)
watershed model

Published Papers (7 papers)

Download All Papers

Research

Jump to: Other

30 pages, 9017 KiB

Open AccessArticle

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

by Jamal Hassan Ougahi and John S. Rowan

Remote Sens. 2024, 16(2), 264; https://doi.org/10.3390/rs16020264 - 9 Jan 2024

Cited by 1 | Viewed by 1156

Abstract

Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km² Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures

Figure 1

20 pages, 3805 KiB

Open AccessArticle

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

by Weihua Cao, Suping Nie, Lijuan Ma and Liang Zhao

Remote Sens. 2023, 15(18), 4602; https://doi.org/10.3390/rs15184602 - 19 Sep 2023

Viewed by 802

Abstract

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures

Figure 1

29 pages, 4534 KiB

Open AccessArticle

Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area

by Mohamed Shawky and Quazi K. Hassan

Remote Sens. 2023, 15(10), 2561; https://doi.org/10.3390/rs15102561 - 14 May 2023

Cited by 6 | Viewed by 1990

Abstract

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers’ judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures

Figure 1

27 pages, 21300 KiB

Open AccessArticle

Mapping Pluvial Flood-Induced Damages with Multi-Sensor Optical Remote Sensing: A Transferable Approach

by Arnaud Cerbelaud, Gwendoline Blanchet, Laure Roupioz, Pascal Breil and Xavier Briottet

Remote Sens. 2023, 15(9), 2361; https://doi.org/10.3390/rs15092361 - 29 Apr 2023

Cited by 2 | Viewed by 1839

Abstract

Pluvial floods caused by extreme overland flow inland account for half of all flood damage claims each year along with fluvial floods. In order to increase confidence in pluvial flood susceptibility mapping, overland flow models need to be intensively evaluated using observations from past events. However, most remote-sensing-based flood detection techniques only focus on the identification of degradations and/or water pixels in the close vicinity of overflowing streams after heavy rainfall. Many occurrences of pluvial-flood-induced damages such as soil erosion, gullies, landslides and mudflows located further away from the stream are thus often unrevealed. To fill this gap, a transferable remote sensing fusion method called FuSVIPR, for Fusion of Sentinel-2 & Very high resolution Imagery for Pluvial Runoff, is developed to produce damage-detection maps. Based on very high spatial resolution optical imagery (from Pléiades satellites or airborne sensors) combined with 10 m change images from Sentinel-2 satellites, the Random Forest and U-net machine/deep learning techniques are separately trained and compared to locate pluvial flood footprints on the ground at 0.5 m spatial resolution following heavy weather events. In this work, three flash flood events in the Aude and Alpes-Maritimes departments in the South of France are investigated, covering over more than 160 km² of rural and periurban areas between 2018 and 2020. Pluvial-flood-detection accuracies hover around 75% (with a minimum area detection ratio for annotated ground truths of 25%), and false-positive rates mostly below 2% are achieved on all three distinct events using a cross-site validation framework. FuSVIPR is then further evaluated on the latest devastating flash floods of April 2022 in the Durban area (South Africa), without additional training. Very good agreement with the impact maps produced in the context of the International Charter “Space and Major Disasters” are reached with similar performance figures. These results emphasize the high generalization capability of this method to locate pluvial floods at any time of the year and over diverse regions worldwide using a very high spatial resolution visible product and two Sentinel-2 images. The resulting impact maps have high potential for helping thorough evaluation and improvement of surface water inundation models and boosting extreme precipitation downscaling at a very high spatial resolution. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures

Figure 1

22 pages, 4291 KiB

Open AccessArticle

Flood Predictability of One-Way and Two-Way WRF Nesting Coupled Hydrometeorological Flow Simulations in a Transboundary Chenab River Basin, Pakistan

by Ehtesham Ahmed, Naeem Saddique, Firas Al Janabi, Klemens Barfus, Malik Rizwan Asghar, Abid Sarwar and Peter Krebs

Remote Sens. 2023, 15(2), 457; https://doi.org/10.3390/rs15020457 - 12 Jan 2023

Cited by 4 | Viewed by 1775

Abstract

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R² and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures

Figure 1

21 pages, 10644 KiB

Open AccessArticle

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

by Yuanhao Fang, Yizhi Huang, Bo Qu, Xingnan Zhang, Tao Zhang and Dazhong Xia

Remote Sens. 2022, 14(18), 4609; https://doi.org/10.3390/rs14184609 - 15 Sep 2022

Cited by 5 | Viewed by 1556

Abstract

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures

Figure 1

Other

Jump to: Research

16 pages, 24217 KiB

Open AccessTechnical Note

High Spatiotemporal Flood Monitoring Associated with Rapid Lake Shrinkage Using Planet Smallsat and Sentinel-1 Data

by Satomi Kimijima and Masahiko Nagai

Remote Sens. 2023, 15(4), 1099; https://doi.org/10.3390/rs15041099 - 17 Feb 2023

Cited by 4 | Viewed by 1341

Abstract

High spatiotemporal flood monitoring is critical for flood control, mitigation, and management purposes in areas where tectonic and geological events significantly exacerbate flood disasters. For example, the rapid lake shrinkage resulting from the transformations of enclosed seas into lakes by the rapid land movement in the collision zone dramatically increases the flood risks in Indonesia, which requires frequent and detailed monitoring and assessment. This study primarily quantified the detailed flood disasters associated with the rapid lake shrinkage in Gorontalo Regency in Gorontalo Province, Indonesia using high spatiotemporal monitoring with a combination of PlanetScope smallsat constellations, Sentinel-1, and surface water datasets. Based on the findings that indicated its volume, distribution, pace, and pattern, the flood event that occurred in Gorontalo in November 2022 was demonstrated within a short interval of 2–12 days. The results also indicate both direct and indirect floodwater overflow from different water resources. Combining these results with the surface water occurrences from 1984 to 2021, our findings reveal the historical major flood-prone areas associated with the rapid lake shrinkage. These findings are expected to aid in the timely high spatiotemporal monitoring of rapid environmental change-induced flood disasters, even in tropical regions with high cloud coverage. Furthermore, these are also expected to be integrated into the flood hazard mitigation and management strategies associated with local-specific tectonic and geological systems. Full article

(This article belongs to the Special Issue Remote Sensing of Floods: Progress, Challenges and Opportunities)

► Show Figures