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Special Issue "Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 June 2018)

Special Issue Editors

Guest Editor
Dr. Alessio Domeneghetti

DICAM, University of Bologna, 40136 Bologna, Italy
Website | E-Mail
Interests: flood damage and flood risk assessment, hydrological and hydraulic modelling; remote sensing; altimetry data; river bathymetry
Guest Editor
Dr. Guy J.-P. Schumann

Remote Sensing Solutions Inc., Monrovia, CA 91016, USA/University of Bristol, Bristol, UK
E-Mail
Interests: flood hazard; flood inundation modeling; remote sensing of floods; large scale; flood disaster assistance
Guest Editor
Dr. Angelica Tarpanelli

Research Institute for Geo-hydrological Protection of the National Research Council, Via Madonna Alta 126, 06128, Perugia, Italy
Website | E-Mail
Interests: natural hazard; hydrology; remote sensing; soil moisture; river discharge; climate change

Special Issue Information

Dear Colleagues,

We are, unfortunately, aware of the significant socio-economic impacts associated with floods. According to the International Disaster Database (EM-DAT), floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 0.8 billion people were affected by inundations in the last decade (2006–2015), while the overall economic damage is estimated to be more than $300 billion. Despite this evidence, and the awareness of the environmental role of rivers and their inundation, our knowledge and modelling capacity of flood dynamics remain poor, mainly related to the availability of measurements and ancillary data.

In this context, remote sensing represents a value source of data and observations that may alleviate the decline in field surveys and gauging stations, especially in remote areas and developing countries. The implementation of remotely-sensed variables (such as digital elevation model, river width, flood extent, water level, land cover, etc.) in hydraulic modelling promises to considerably improve our process understanding and prediction and during the last decades, an increasing amount of research has been undertaken to better exploit the potential of current and future satellite observations. In particular, in recent years, the scientific community has shown how remotely sensed variables have the potential to play a key role in the calibration and validation of hydraulic models, as well as provide a breakthrough in real-time flood monitoring applications. However, except for a few pioneering studies, the potential of remotely sensed data to enhance flood modelling has not yet been fully enough explored, and the use of such data for operational flood mapping is far away from being consolidated. In this scenario, the forthcoming satellite missions dedicated to global water surfaces monitoring will enhance the quality, as well as the spatial and temporal coverage, of remotely sensed data, thus offering new frontiers and opportunities to enhance the understanding of flood dynamics and our capability to map their extents.

This Special Issue aims to collect studies and experiences aimed at aiding and advancing flood monitoring and mapping through remotely sensed data. The list below provides a general (but not exhaustive) overview of the topics that are solicited for this Special Issue:

- Remote sensing data for flood hazard and risk mapping;
- Remote sensing techniques to monitor flood dynamics;
- The use of remotely sensed data for the calibration, or validation, of hydrological or hydraulic models;
- Data assimilation (DA) of remotely sensed data into hydrological and hydraulic models;
- Improvement of river discretization and monitoring by means of satellite based observations;
- River flows estimation by means of remote sensed observations.
- River and flood dynamics estimation from satellite (especially time lag, flow velocity, etc.)

Dr. Alessio Domeneghetti
Dr. Guy J.-P. Schumann
Dr. Angelica Tarpanelli
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 papers will be 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 monthly 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 1800 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

  • Flooding
  • Floodplains
  • Rivers Dynamics
  • Surface Water
  • Remote Sensing
  • Inundation
  • Flood Mapping
  • Flood Monitoring
  • Hazard Mapping
  • Hydrodynamic Modeling

Published Papers (11 papers)

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Research

Open AccessFeature PaperArticle Flow Duration Curve from Satellite: Potential of a Lifetime SWOT Mission
Remote Sens. 2018, 10(7), 1107; https://doi.org/10.3390/rs10071107
Received: 1 June 2018 / Revised: 10 July 2018 / Accepted: 10 July 2018 / Published: 11 July 2018
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Abstract
A flow duration curve (FDC) provides a comprehensive description of the hydrological regime of a catchment and its knowledge is fundamental for many water-related applications (e.g., water management and supply, human and irrigation purposes, etc.). However, relying on historical streamflow records, FDCs are
[...] Read more.
A flow duration curve (FDC) provides a comprehensive description of the hydrological regime of a catchment and its knowledge is fundamental for many water-related applications (e.g., water management and supply, human and irrigation purposes, etc.). However, relying on historical streamflow records, FDCs are constrained to gauged stations and, thus, typically available for a small portion of the world’s rivers. The upcoming Surface Water and Ocean Topography satellite (SWOT; in orbit from 2021) will monitor, worldwide, all rivers larger than 100 m in width (with a goal to observe rivers as small as 50 m) for a period of at least three years, representing a potential groundbreaking source of hydrological data, especially in remote areas. This study refers to the 130 km stretch of the Po River (Northern Italy) to investigate SWOT potential in providing discharge estimation for the construction of FDCs. In particular, this work considers the mission lifetime (three years) and the three satellite orbits (i.e., 211, 489, 560) that will monitor the Po River. The aim is to test the ability to observe the river hydrological regime, which is, for this test case, synthetically reproduced by means of a quasi-2D hydraulic model. We consider different river segmentation lengths for discharge estimation and we build the FDCs at four gauging stations placed along the study area referring to available satellite overpasses (nearly 52 revisits within the mission lifetime). Discharge assessment is performed using the Manning equation, under the assumption of a trapezoidal section, known bathymetry, and roughness coefficient. SWOT observables (i.e., water level, water extent, etc.) are estimated by corrupting the values simulated with the quasi-2D model according to the mission requirements. Remotely-sensed FDCs are compared with those obtained with extended (e.g., 20–70 years) gauge datasets. Results highlight the potential of the mission to provide a realistic reconstruction of the flow regimes at different locations. Higher errors are obtained at the FDC tails, where very low or high flows have lower likelihood of being observed, or might not occur during the mission lifetime period. Among the tested discretizations, 20 km stretches provided the best performances, with root mean absolute errors, on average, lower than 13.3%. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Dynamic Change Analysis of Surface Water in the Yangtze River Basin Based on MODIS Products
Remote Sens. 2018, 10(7), 1025; https://doi.org/10.3390/rs10071025
Received: 7 May 2018 / Revised: 13 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
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Abstract
The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or
[...] Read more.
The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data
Remote Sens. 2018, 10(6), 910; https://doi.org/10.3390/rs10060910
Received: 4 April 2018 / Revised: 27 May 2018 / Accepted: 5 June 2018 / Published: 8 June 2018
PDF Full-text (4779 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image
[...] Read more.
Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Automated Extraction of Surface Water Extent from Sentinel-1 Data
Remote Sens. 2018, 10(5), 797; https://doi.org/10.3390/rs10050797
Received: 6 March 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
Cited by 1 | PDF Full-text (6597 KB) | HTML Full-text | XML Full-text
Abstract
Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic
[...] Read more.
Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping
Remote Sens. 2018, 10(5), 780; https://doi.org/10.3390/rs10050780
Received: 3 April 2018 / Revised: 10 May 2018 / Accepted: 17 May 2018 / Published: 18 May 2018
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Abstract
Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness
[...] Read more.
Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for each. Historical open water maps from 202 Landsat scenes spanning the years 1985–2016 generated previously were enhanced to improve flooded vegetation mapping along the Saint John River in New Brunswick, Canada. Open water and flooded vegetation maps were created over the same region from 181 RADARSAT 1 and 2 scenes acquired between 2003–2016. Comparisons of maps from different sensors and hydrometric data were performed to examine consistency and robustness of products derived from different sensors. Simulations reveal that the methodology used to map open water from dual-pol RADARSAT 2 is insensitive to up to about 20% training error. Landsat depicts open water inundation well, while flooded vegetation can be reliably mapped in leaf-off conditions. RADARSAT mapped approximately 8% less open water area than Landsat and 0.5% more flooded vegetation, while the combined area of open water and flooded vegetation agreed to within 0.2% between sensors. Derived historical products depicting inundation frequency and trends were also generated from each sensor’s time-series of surface water maps and compared. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Applying Independent Component Analysis on Sentinel-2 Imagery to Characterize Geomorphological Responses to an Extreme Flood Event near the Non-Vegetated Río Colorado Terminus, Salar de Uyuni, Bolivia
Remote Sens. 2018, 10(5), 725; https://doi.org/10.3390/rs10050725
Received: 5 April 2018 / Revised: 20 April 2018 / Accepted: 6 May 2018 / Published: 8 May 2018
Cited by 1 | PDF Full-text (5243 KB) | HTML Full-text | XML Full-text
Abstract
In some internally-draining dryland basins, ephemeral river systems terminate at the margins of playas. Extreme floods can exert significant geomorphological impacts on the lower reaches of these river systems and the playas, including causing changes to flood extent, channel-floodplain morphology, and sediment dispersal.
[...] Read more.
In some internally-draining dryland basins, ephemeral river systems terminate at the margins of playas. Extreme floods can exert significant geomorphological impacts on the lower reaches of these river systems and the playas, including causing changes to flood extent, channel-floodplain morphology, and sediment dispersal. However, the characterization of these impacts using remote sensing approaches has been challenging owing to variable vegetation and cloud cover, as well as the commonly limited spatial and temporal resolution of data. Here, we use Sentinel-2 Multispectral Instrument (MSI) data to investigate the flood extent, flood patterns and channel-floodplain morphodynamics resulting from an extreme flood near the non-vegetated terminus of the Río Colorado, located at the margins of the world’s largest playa (Salar de Uyuni, Bolivia). Daily maximum precipitation frequency analysis based on a 42-year record of daily precipitation data (1976 through 2017) indicates that an approximately 40-year precipitation event (40.7 mm) occurred on 6 January 2017, and this was associated with an extreme flood. Sentinel-2 data acquired after this extreme flood were used to separate water bodies and land, first by using modified normalized difference water index (MNDWI), and then by subsequently applying independent component analysis (ICA) on the land section of the combined pre- and post-flood images to extract flooding areas. The area around the Río Colorado terminus system was classified into three categories: water bodies, wet land, and dry land. The results are in agreement with visual assessment, with an overall accuracy of 96% and Kappa of 0.9 for water-land classification and an overall accuracy of 83% and Kappa of 0.65 for dry land-wet land classification. The flood extent mapping revealed preferential overbank flow paths on the floodplain, which were closely related to geomorphological changes. Changes included the formation and enlargement of crevasse splays, channel avulsion, and the development of erosion cells (floodplain scour-transport-fill features). These changes were visualized by Sentinel-2 images along with WorldView satellite images. In particular, flooding enlarged existing crevasse splays and formed new ones, while channel avulsion occurred near the river’s terminus. Greater overbank flow on the floodplain led to rapid erosion cell development, with changes to channelized sections occurring as a result of adjustments in flow sources and intensity combined with the lack of vegetation on the fine-grained (predominantly silt, clay) sediments. This study has demonstrated how ICA can be implemented on Sentinel-2 imagery to characterize the impact of extreme floods on the lower Río Colorado, and the method has potential application in similar contexts in many other drylands. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka
Remote Sens. 2018, 10(3), 448; https://doi.org/10.3390/rs10030448
Received: 20 December 2017 / Revised: 3 March 2018 / Accepted: 7 March 2018 / Published: 13 March 2018
Cited by 1 | PDF Full-text (11112 KB) | HTML Full-text | XML Full-text
Abstract
Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE)
[...] Read more.
Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain
Remote Sens. 2017, 9(10), 1013; https://doi.org/10.3390/rs9101013
Received: 18 July 2017 / Revised: 23 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
Cited by 1 | PDF Full-text (9912 KB) | HTML Full-text | XML Full-text
Abstract
The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location
[...] Read more.
The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water ( γ w ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ w . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China
Remote Sens. 2017, 9(10), 1011; https://doi.org/10.3390/rs9101011
Received: 14 August 2017 / Revised: 20 September 2017 / Accepted: 22 September 2017 / Published: 29 September 2017
Cited by 1 | PDF Full-text (5980 KB) | HTML Full-text | XML Full-text
Abstract
Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used
[...] Read more.
Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used Gravity Recovery and Climate Experiment (GRACE) satellite data, flood potential index (FPI), precipitation data (Tropical Rainfall Measuring Mission, TRMM 3B43), and other meteorological data to generate monthly terrestrial water storage anomalies (TWSA) and to evaluate flood potential in the YRB. The results indicate that the basin contained increasing amounts of water from 2003 to 2014, with a slight increase of 3.04 mm/year in the TWSA. The TWSA and TRMM data exhibit marked seasonal characteristics with summer peaks and winter dips. Estimates of terrestrial water storage based on GRACE, measured as FPI, are critical for understanding and predicting flooding. The 2010 flood (FPI ~ 0.36) was identified as the most serious disaster during the study period, with discharge and precipitation values 37.95% and 19.44% higher, respectively, than multi-year average values for the same period. FPI can assess reliably hydrological extremes with high spatial and temporal resolution, but currently, it is not suitable for smaller and/or short-term flood events. Thus, we conclude that GRACE data can be effectively used for monitoring and examining large floods in the YRB and elsewhere, thus improving the current knowledge and presenting potentially important political and economic implications. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka
Remote Sens. 2017, 9(10), 998; https://doi.org/10.3390/rs9100998
Received: 19 July 2017 / Revised: 15 September 2017 / Accepted: 22 September 2017 / Published: 27 September 2017
Cited by 3 | PDF Full-text (5918 KB) | HTML Full-text | XML Full-text
Abstract
The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation
[...] Read more.
The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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Open AccessArticle Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery
Remote Sens. 2017, 9(8), 807; https://doi.org/10.3390/rs9080807
Received: 17 May 2017 / Revised: 18 July 2017 / Accepted: 26 July 2017 / Published: 7 August 2017
Cited by 4 | PDF Full-text (6692 KB) | HTML Full-text | XML Full-text
Abstract
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over
[...] Read more.
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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