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date: 26 May 2017

Remote Sensing of Floods

Summary and Keywords

For about 40 years, with a proliferation over the last two decades, remote sensing data, primarily in the form of satellite and airborne imagery and altimetry, have been used to study floods, floodplain inundation, and river hydrodynamics. The sensors and data processing techniques that exist to derive information about floods are numerous. Instruments that record flood events may operate in the visible, thermal, and microwave range of the electromagnetic spectrum. Due to the limitations posed by adverse weather conditions during flood events, radar (microwave range) sensors are invaluable for monitoring floods; however, if a visible image of flooding can be acquired, retrieving useful information from this is often more straightforward. During recent years, scientific contributions in the field of remote sensing of floods have increased considerably, and science has presented innovative research and methods for retrieving information content from multi-scale coverages of disastrous flood events all over the world. Progress has been transformative, and the information obtained from remote sensing of floods is becoming mature enough to not only be integrated with computer simulations of flooding to allow better prediction, but also to assist flood response agencies in their operations.

Furthermore, this advancement has led to a number of recent and upcoming satellite missions that are already transforming current procedures and operations in flood modeling and monitoring, as well as our understanding of river and floodplain hydrodynamics globally. Global initiatives that utilize remote sensing data to strengthen support in managing and responding to flood disasters (e.g., The International Charter, The Dartmouth Flood Observatory, CEOS, NASA’s Servir and the European Space Agency’s Tiger-Net initiatives), primarily in developing nations, are becoming established and also recognized by many nations that are in need of assistance because traditional ground-based monitoring systems are sparse and in decline. The value remote sensing can offer is growing rapidly, and the challenge now lies in ensuring sustainable and interoperable use as well as optimized distribution of remote sensing products and services for science as well as operational assistance.

Keywords: floods, remote sensing, satellite imagery, altimetry, flood forecasting, hydrodynamic modeling, disaster assistance, interoperability

Introduction

Flooding is one of the costliest natural disasters. According to Munich RE, over the last 35 years, three of the five costliest natural disasters have been floods from storm surges and tsunamis (the 2012 Sandy event, the 2011 Japan tsunami, and Katrina in 2005), and this is the case for nearly every year in this decade (see Figure 1 as an example for 2015). By 2050, costs of floods in coastal cities alone could reach $1 trillion annually (Hallegate, Green, Nicholls, & Corfee-Morlot, 2013). Recently, all around the world, floods have been of exceptionally high magnitude, with rainfalls exceeding record amounts and causing unprecedented damage in many countries (e.g., U.S. East Coast, Malawi, Philippines, India, U.S. Southwest, Northern England, U.S. Mississippi/Midwest, etc.).

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Figure 1. The ten costliest natural disasters in 2015 by overall losses, insured losses, and fatalities. © Munich RE.

Recent events have covered spatial scales well beyond past records and are frequently surpassing traditional regional measurement and disaster response coverage. In essence, these large-scale events highlight the need for data and monitoring coverage that can only be provided by remote sensing platforms (i.e., satellite and airborne sensors).

Remote sensing, in its true definition, is the acquisition of information about an object or phenomenon without making physical contact with the object, but is more commonly referred to as the scanning of the Earth by satellite or high-flying aircraft and therefore slowly becoming popularly synonymous with the science of Earth Observation.

Remote sensing of flood event processes and variables is of great value to many sectors (flood risk mitigation planning, disaster relief services, global reinsurance markets, and research) but the amount and quality of information available varies greatly with location, spatial scales, and time. Remote sensing of floods can complement ground-based observations and be integrated with computer models of flooding for event re-analysis and forecasting to augment the amount of information available to end users, decision makers, and scientists.

However, before using remote sensing data or products for flood monitoring and management, it is important to consider end-user requirements and the appropriate timeline as well as the spatial resolution of the delivered products. Figure 2 shows the requirement in terms of spatial resolution and turnaround time for different flood management sectors. For instance, flood mapping for emergency management could be done at any spatial resolution, and accuracy may be less important, but data or products should be made available within 48 hours, preferably in 12- to 24-hour intervals, whereas for the insurance industry, the opposite situation would apply; spatial resolutions should be finer than 10 m, but timeliness might be of lesser concern.

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Figure 2. Temporal and spatial resolution requirements by different sectors active in flood monitoring and management. Composed after Blyth, 1997.

Although at first glance this situation seems favorable, as it is in line with the fundamental physical principle that by increasing satellite swath coverage, thus decreasing spatial resolution, the orbital revisit time is increased, research scientists and product developers are far from meeting needs, at least in the field of flood monitoring. The main reason for this gap is two-fold. On the one hand, the science lacks understanding of end-user needs, and on the other hand, decision makers are oftentimes reluctant to use new data and methods in their operations or systems.

Nevertheless, over the past two decades, remote sensing has clearly been transformative in the way we now understand flood processes at different scales, model and predict floods, and assist flood disaster response. Success stories are numerous, but both the science and end-user communities need to be aware of the fundamental limitations of remote sensing of floods and manage expectations accordingly.

The Science of Remote Sensing of Floods

Within the electromagnetic spectrum, there are several ranges of wavelengths that are typically used for remote sensing of floods. The atmosphere absorbs portions of the signal, and there are only a few so called atmospheric windows that allow the satellite signals to be transmitted (Figure 3). The most common ranges of wavelengths used for floods, or surface water detection in general, extend from the visible (optical) to the near-infrared and thermal infrared range, and the microwave (radar) range at the opposite side of the spectrum.

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Figure 3. Top panel: The electromagnetic spectrum. Wavelength regions typically used in remote sensing of floods are shown. Bottom panel: Graph showing the parts of the spectrum that are referred to as “atmospheric windows,” that is, where atmospheric transmission of wavelengths is high. Modified after Lubin and Massom (2007).

Imaging Sensors

Optical and Infrared Range

The information about floods that can be retrieved from a satellite or airborne instrument operating in the visible-to-infrared ranges depends on daylight, weather conditions (particularly clouds and rain), vegetation canopy density (especially emerging flooded vegetation or overhanging vegetation), and the chemical composition as well as the turbidity of the water (e.g., water sediment loads), all of which vary considerably during flood events.

These limitations are considerable, and the situation is oftentimes further aggravated by the fact that the satellite actually needs to be passing over the flood at the right time. Unless satellites have a very frequent revisit time (such as NASA’s Aqua and Terra satellites, or NOAA’s AVHRR series) or operate in constellation for a given mission (e.g., [the Italian space agency] ASI’s COSMO-SkyMed, ESA’s Sentinel-1, or the International Charter), it is rather by chance to get a useful image of a flood, given that the average satellite revisit time is about two weeks, which is more than the typical flood duration in non-monsoonal regions.

If an image can be acquired, either by aircraft or satellite, in the visible to near-infrared range, classifying water bodies and inundated area is often more straightforward than on a radar image due to the fact that the human eye is more accustomed to this type of image and thus interpretation is easier (Figure 4).

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Figure 4. Landsat-5 captures Missouri River flooding near Omaha, Nebraska, on July 6, 2011. The flood waters show up as very dark blue and, where the water is shallow, medium blue. In the image, the interstate is cut off by flood waters, just south of Missouri Valley, Iowa, and about 20 miles north of Omaha. © USGS/NASA.

Given the very high spatial resolution of aerial photography, flood extent is often derived from color or panchromatic aerial photography by simply digitizing the edges at the contrasting land-water interface. A very simple and straightforward approach to retrieving useful flood information from space is that of extracting a binary map consisting of dry and flooded pixels, most often in a fully- or semi-automated way. This procedure is applied throughout the world by many research teams and engineering and consulting companies as well as emergency response services and governmental institutions. Probably the most common image processing algorithm applied to an optical image for classifying water versus dry land is the Normalized Difference Water Index (NDWI), or versions thereof, which uses the reflected near-infrared radiation and visible green light to enhance the presence of open water surfaces while eliminating the presence of soil and terrestrial vegetation features (McFeeters, 1996).

The potential that (optical) satellite images can contribute to flood science and applications has been known for over 40 years. Several studies in the early 1970s demonstrated the value of optical satellite imagery to map the evolution of flooding from space and indicated strong application potential for such maps for a number of sectors (e.g., Currey, 1977; Deutsch & Ruggles, 1978; Robinove, 1978).

Presently, the Moderate Resolution Imaging Spectrometer (MODIS) onboard NASA’s Terra and Aqua satellites acquires images twice daily all around the globe. This offers a unique capability to monitor flood events and assist disaster response where and when possible. Despite the relatively low spatial resolution (250 m), persistent cloud cover and vegetation, as well as densely built areas that limit successful flood detection, NASA processes these images in near-real time (NRT) and provides flood maps to a number of flood relief agencies (for further reference, see NASA’s NRT Global Flood Mapping and the Dartmouth Flood Observatory).

Notwithstanding the success of optical imagery for flood mapping (see Marcus & Fonstad, 2008 for a detailed scientific review), as noted earlier, the systematic application of such imagery is hampered by persistent cloud cover during floods, particularly in small to medium-sized basins where floods often recede before weather conditions improve, as is oftentimes the case in Europe for instance. Also, as already mentioned, the inability to map flooding in urban areas or beneath vegetation canopies, limits the applicability of optical sensors (Figure 5).

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Figure 5. Photographs depicting flood disasters in environments that pose considerable challenges to satellite remote sensing.

Microwave Range

Given the limitations of sensors operating in the visible and infrared spectrum to acquire flood information routinely, microwave (radar) remote sensing is often considered an attractive alternative or complementary technology for flood detection and monitoring. Microwaves penetrate cloud cover, fog, and light rain, and in commonly employed radar frequencies (X or C, see Figure 3), active radar signals from synthetic aperture radar (SAR) are reflected away from the sensor by smooth open water bodies, so, consequently, flooded versus dry land is typically of high contrast.

Active radars transmit a signal and receive the backscatter characteristics of many different surface features, which may be difficult to distinguish accurately. However, if knowledgeable in backscatter characterization, valuable information can be retrieved from a SAR image that is only possible with very high resolution commercial optical imagery or aerial photography (Figure 6), such as emerging flooded vegetation (e.g., Hess, Melack, Filoso, & Wang, 1995; Hess, Melack, Novo, Barbosa, & Gastil, 2003), flood conditions within urban areas (e.g., Mason, Speck, Devereux, Schumann, Neal, & Bates, 2010), or even road and other infrastructure damage (e.g., Mouratidis & Sarti, 2013).

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Figure 6. The port of Sendai after the tsunami in early 2011. This TerraSAR-X image, acquired on March 12, 2011, shows the devastation of the port of the Japanese city of Sendai. The magenta-colored areas reveal the extent of damage in the form of boulders and debris deposits; the blue areas denote flooding. © DLR (CC-BY 3.0).

However, the use of passive microwave systems over land surfaces, meaning that surface-emitted wavelengths are measured by the satellite, is difficult given the large angular beams of such systems (Rees, 2001), resulting in spatial resolutions as large as 20 to100 km. Interpretation of the wide range of materials with many different emissivities is thus rendered nearly impossible. Nevertheless, as the sensor is sensitive to changes in the electric field, very large areas of water, for instance, can be detected but their uncertainties may be large (Schumann, 2009).

Active microwave imagery from SAR seems to be, at the moment, the only reliable source of information for routinely monitoring floods on rivers much smaller than 1 km in width. The spatial resolution from current and planned spaceborne SAR sensors (i.e., typically 3–30 m) should satisfy requirements for most applications. Satellite data with a ground resolution of 100 m or even coarser would still be of value for rapid response requirements for floods on large rivers, but this needs to be further investigated. Indeed, the need for rapid dissemination of information is probably of greater importance in the first instance than the production of a high-resolution product (Blyth, 1997). For example, Di Baldassarre, Schumann, and Bates(2009a) demonstrate that inundation width derived from a 75-m resolution SAR image in wide swath mode (delivered 24 h after an event on the Po River, Italy, in early June 2008) can be used in near-real time to verify timely flood inundation modeling. Obviously, near-real time availability of higher-resolution SAR data is preferred, but the cost of such data may quickly become non-trivial.

More recently, advances in radar technology have led to several high resolution SAR missions (e.g., TerraSAR-X, Radarsat-2, Cosmo-SkyMed constellation, Sentinel-1A) that can complement optical imagery by allowing more successful detection of flooding, as illustrated in Figure 7, and also demonstrated by Martinis, Schumann, and Bates(2013) within a fully automated processing chain. This complementarity can be advantageous in a variety of environments where optical imagery has clear limitations, such as in urban areas, coasts, wetlands, forests, and during adverse weather conditions where optical sensors are often limited (Schumann & Moller, 2015).

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Figure 7. Map showing rivers in flood and maximum flood extent during the May–June 2015 flood disaster in Texas, Oklahoma, and Arkansas.

Many SAR image-processing techniques exist to more or less successfully derive flood area or extent (for a review, refer to Schumann, Bates, Horritt, Matgen, & Pappenberger, 2009), but classification inaccuracies of flooded areas (i.e., dry areas mapped as flooded and vice versa) vary greatly. Typically, detection of flooding is from a single image using Otsu’s method (Otsu, 1979) or a version thereof, which automatically performs image thresholding. The algorithm maximizes the inter-class variance between image pixels assuming that the image contains only two classes of pixels following a bi-modal distribution, which is often an acceptable assumption in the case of a SAR flood image.

Noteworthy is that, in a number of cases, multitemporal SAR images have been used successfully to monitor the evolution of a flood event or to map inundation dynamics (e.g., Bates, Wilson, Horritt, Mason, Holden, & Currie, 2006; Pulvirenti, Chini, Pierdicca, Guerriero, & Ferrazzoli, 2011; Schumann, Neal, Mason, & Bates, 2011). In such cases, rapid mapping and dissemination is preferable, of course; yet in urban areas, as well as in wetlands and forests, detection of flooding from a satellite SAR image still poses considerable challenges (Schumann & Moller, 2015).

Also, in some cases, SAR images acquired over the same area but at different times have even been used to derive spatially distributed water levels through a complex but powerful technique known as interferometry, or InSAR (refer to e.g., Alsdorf, Melack,Dunne, Mertes, Hess, & Smith, 2000 for details on this technique), which will be employed on the upcoming NASA/CNES Surface Water Ocean Topography (SWOT) mission to measure water levels and map water surfaces of the world’s lakes and main rivers (Fjørtoft et al., 2014).

Despite the many challenges, SAR systems are attractive for monitoring, mapping, and analyzing floods, and much progress in addressing those challenges is expected in the coming years given the growing number of current and planned satellite missions carrying SAR sensors.

Altimeters

Altimeters (most common are radar altimeters) transmit signals to Earth, and receive the echo from the surface. The satellite orbit has to be accurately tracked, and its position is determined relative to an arbitrary reference surface (an ellipsoid). Altimetry instruments determine the distance from the satellite to a target surface by measuring the satellite-to-surface round-trip time of the signal pulse, thus retrieving precise surface height measurements (Figure 8). The magnitude and shape of the echoes (or waveforms) also contain information about the characteristics of the surface. Surfaces that are large and spatially homogeneous, such as the ocean, or indeed flooding, are well suited for altimetry and lead to high measurement accuracy. However, surfaces that contain discontinuities or significant slopes, such as some ice, rivers, or land surfaces, make accurate interpretation more difficult.

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Figure 8. A simple illustration of the measurement principle of altimetry.

Satellite tracks passing over rivers and lakes are considered virtual measurement stations (VS), usually complementing the declining network of gauging stations on the ground (Alsdorf, Rodriguez, & Lettenmaier, 2007). Altimetry data series may provide essential information for characterizing a hydrological regime, especially in remote and data-sparse regions. Some recent studies have even combined altimetry data with traditional information for the estimation of river discharge in ungauged basins (see e.g., Bjerklie, Dingman, Vorosmarty, Bolster, & Congalton, 2003; Durand et al., 2016; Tourian, Sneeuw, & Bárdossy, 2013). However, limitations related to typical ground-track spacing (up to a few hundred km), wide footprint resolution (from a few hundred meters to several km), and low revisit time of most altimetry missions (10–35 days) make the use of altimetry data on rivers with small to medium width or with a dynamic flow regime very difficult (Schumann & Domeneghetti, 2016).

Nevertheless, satellite altimetry has proven extremely valuable, and innovative approaches are being developed to overcome some of the fundamental limitations, such as spatial and temporal low resolution (e.g., Tourian et al., 2016). Altimetry is particularly useful for the monitoring of water resources from reservoirs and lakes in countries like Africa (Figure 9), where information is difficult to access and obtain with traditional measurement techniques on the ground because of the high cost in equipment, manpower, and communications, and because it is difficult to obtain these precious hydrological data from many countries.

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Figure 9. Example of altimetry-derived rivers and lake levels across Africa. © ESA.

Over the years, long time series (in some cases >15 years) of both radar and laser altimetry data have become available and have stimulated innovative research among hydrologists, in particular for flood modeling and forecasting. For example, Baugh, Bates, Schumann, and Trigg, (2013) have demonstrated the use of ICESat-1 (NASA’s laser altimetry mission) vegetation canopy height measurements (Simard, Pinto, Fisher, & Baccini, 2011) to correct floodplain bare-earth topography from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), thereby improving the ability to simulate floodplain flow patterns.

Altimetry data can also be used to understand first- and second-order hydrodynamics of rivers (Figure 10), such as water surface slopes and backwater effects, as demonstrated by O’Loughlin, Trigg, Schumann, and Bates(2013) for the Congo River, or other large rivers that are now mostly ungauged (see also Hall, Schumann, Bamber, Bates, & Trigg, 2012 for a pioneer study using ICESat-1 water levels on the Amazon River). Also using ICESat-1 altimeter data, Schumann et al. (2013b) successfully calibrated in-channel water levels of a large scale flood inundation forecasting model over the Lower Zambezi River in SE Africa.

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Figure 10. ICESat-1 altimetry observations within 160 m of a floodplain channel greater than 120 m in width (cyan circles) across the middle reach of the Amazon River. From Hall (2013).

One of the most notable research studies in this context has been undertaken by Tourian et al. (2016), who developed an approach to densify the time series of altimetry data from many satellite missions in both space and time by statistically and hydrodynamically connecting all the time series after removing inter-satellite biases. This produces an accurate water level time series at any location along a river (~50 cm error on average).

With upcoming satellite radar and laser altimetry missions (e.g., Sentinel-3 constellation, SWOT, ICESat-2), there will be a proliferation of measurements of the world’s major rivers and lakes, and the challenge will be in making sense of all that data and what it actually means to have that much information available (see Schumann & Domeneghetti, 2016 for a discussion on this topic).

Integration With Computer Simulations and Forecasting of Floods

Integrating remotely sensed flood information with flood process models, such as one-dimensional and two-dimensional hydrodynamic (hydraulic) models, can be done for model evaluation (i.e., model calibration or validation) or assimilation with models.

Simply put, hydrodynamic models simulate water flow volumes and depths within channel networks (commonly in 1-D) and in the adjacent floodplain lands when channel bank overtopping occurs and water spreads across low-lying topography (in 2-D). Of course, such models are needed for predicting flood events as well as for event re-analysis. Although traditionally applied to relatively small sections or reaches of rivers, recent advances in computational model code and computing power have enabled flood simulations over spatial and temporal scales much larger than in the past; in fact, such models can now be run at continental-to-global scales (Dottori, Salamon, Bianchi, Alfieri, Hirpa, & Feyen, 2016; Sampson, Smith, Bates, Neal, Alfieri, & Freer, 2015).

Given the inherent nature of large spatial coverage and of pixel information being two-dimensional, using imagery of floods, in particular from satellites, is an inviting alternative to the typically few and far apart ground-based measurements traditionally used to evaluate the performance and prediction skills of flood models.

As described in the previous sections, remote sensing can provide information about both flood area and water levels; however, information about flow velocities and actual depth of water is difficult or in many cases impossible to obtain, especially where and when such information would be most valuable, such as within urban areas during high-magnitude events. This is largely due to fundamental limitations related to the physical principles of signal interactions with surface water; most wavelengths are typically either fully absorbed when reaching certain depths or reflected away from calm, open water surfaces. Only very few successful studies have been conducted that demonstrate the ability of remote sensing to infer information about water depth or to retrieve flow velocity fields, not merely enough to reach application credibility or acceptable readiness levels.

Model Validation and Calibration

Valuable information can be retrieved from flood area and water level, either directly from altimetry and SAR interferometry, or indirectly, by merging flood shorelines with a digital elevation model (e.g., Schumann et al., 2007). A variety of methods exist to integrate such information with flood models. As noted earlier, the most common use of flood area or extent data is for model calibration or validation (see, e.g., Aronica, Bates, & Horritt, 2002, as one of the classic studies on this topic). Calibration is defined as adjusting model parameters (such as surface roughness or boundary conditions) to improve the fit between model predictions and observations. Validation, or verification, involves comparing model output with observations and using this to deduce model performance (Figure 11). The processes of model calibration and validation with remotely sensed data involve some common steps: (a) extraction of flood area, shorelines, or water level from the remote sensing data, and (b) comparison with model predictions, using some form of performance metric or statistical measure.

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Figure 11. Map showing fit between a global 2-D flood inundation model and the U.K. Environment Agency benchmark data for the Severn catchment (~11,000 km2). Green shading represents predicted correct flooding, blue shading represents flooded area unique to the global model, and red shading represents flooded area unique to the benchmark data. From Sampson et al. (2015).

When using flood area, a map overlay operation with some sort of spatial comparison metric is typically used. This is applied to find the best model simulation within a set of simulations run with different model parameter values, or to evaluate the accuracy of a model. Early studies of this type have focused on simple wet/dry flood maps either in a deterministic or probabilistic model evaluation context (see Schumann et al., 2009; Yan, Di Baldassarre, Solomatine, & Schumann, 2015, for a review of existing literature). More recently, however, probabilistic approaches have been employed to process a flood image (e.g., Giustarini, Chini, Hostache, Pappenberger, & Matgen, 2015), which has been shown to increase information content considerably, thereby achieving better model calibration (Di Baldassarre, Schumann, & Bates, 2009b). The potential of radar and laser altimetry for model calibration has also been examined by a number of studies (e.g., Domeneghetti, Castellarin, Tarpanelli, & Moramarco, 2015; Neal, Schumann, & Bates, 2012; Schumann et al., 2013b;).

Assimilation With Models

Assimilation, which solves the optimization problem by combining model and observation while accounting for errors in both, may appear conceptually simple (Figure 12); however, successful application of the many different mathematical variants that exist is rather difficult and requires an expert skillset (see Evensen, 2006 for a popular introduction to assimilation).

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Figure 12. Abstract schematic of the process of assimilation.

Water level and flood area have been used in data assimilation studies, although assimilation of flood area information is still being studied, and only a very limited number of examples exist in the literature to date (Andreadis & Schumann, 2014; Lai, Liang, Yesou, & Daillet, 2014). In general, the problem with assimilating inundated area in hydrodynamic models is that of localization; that is, the impact the information content has is highly localized and, as a result, may wear off rapidly in space and time. In addition, the inherent two-dimensionality of flood area complicates this problem, and advanced local smoothing techniques are needed (see e.g., Andreadis & Schumann, 2014). Assimilating water levels, obtained either directly with altimetry or by intersecting flood shorelines with accurate floodplain topography, however, is relatively straightforward and more commonly reported in the scientific literature.

When carefully applied, assimilation can be used not only to correct or update a model forecast but also to pinpoint areas in the floodplain and channel where important model parameters (e.g., friction values) are homogeneous, thereby inferring localized flood flow processes (see Hostache, Lai, Monnier, & Puech, 2010). In a similar context, assimilation can help determine the impact of satellite observations on the prediction of a number of flood parameters such as flood levels, discharge, and inundation area, as demonstrated by Andreadis and Schumann (2014).

With an increasing number of satellite missions dedicated to hydrology and the monitoring of floods, information on how satellite revisit time impacts forecast performance is crucial to optimize satellite operations (i.e., acquisition of observations) as well as models, and here assimilation can also help (García-Pintado, Neal, Mason, Dance, & Bates, 2013).

In the plethora of methodologies and numerical techniques exploiting the potential of satellite observations of flood parameters, data assimilation appears very promising, especially for flood forecasting (Schumann & Domeneghetti, 2016). Assimilation studies have begun to investigate the value of integrating remote sensing of floods with computer models, but more research in this field is needed to fully grasp the potential that may be offered by the current and future proliferation of satellite observations of floods. This is especially true in the context of real-time forecasting during complex scenarios, or indeed, when assimilating multiple observations in a coupled modeling system that can simulate processes from “clouds to inundation.” Here, non-linearities and complex feedbacks between different processes exist and need to be more fully understood (Matgen et al., 2010).

End-User Applications and Operational Assistance

It is clear that recent and upcoming satellite missions are transforming current procedures and operations in flood mapping, monitoring, and modeling, as well as our understanding of river and floodplain hydrodynamics globally (Bates, Neal, Alsdorf, & Schumann, 2014). Indeed there is an increasing trend in initiatives that utilize remote sensing data to strengthen support in global flood mapping and monitoring, and in responding to flood disasters (e.g. the International Charter, the Dartmouth Flood Observatory, UMD’s Global Flood Monitoring System, CEOS, NASA’s Servir, and ESA’s Tiger-Net initiatives). Figure 13 illustrates three very successful end-user oriented applications focusing on flood detection and forecasting.

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Figure 13. News feature articles of successful end-user-oriented applications based on satellite observations.

Global Initiatives

The International Charter

The International Charter provides a unified system of space data acquisition and delivery to those affected by disasters. Space agency members commit resources to support the provisions of the Charter, which can be activated by pre-defined authorized users. Due to its ongoing success over the last decades, the Charter is soon widening access to satellite imagery for disaster response to any national disaster management authority without the need for membership.

Tiger-Net

Tiger-Net is a major component of the European Space Agency (ESA) in supporting the African continent with Earth Observation (EO) capacity for water resource monitoring, including flood mapping and monitoring through ESA’s satellites, in particular SAR. This is done in close collaboration with African water authorities and experts. Main achievements of Tiger-Net include the development of an open-source Water Observation and Information System (WOIS) for monitoring, assessing, and inventorying water resources, and training of African water authorities and technical centers to fully exploit the increasing observation capacity offered by current and upcoming generations of satellites.

Servir

Servir is a joint venture between NASA and the U.S. Agency for International Development (USAID). Similar to ESA’s Tiger-Net, it provides satellite-based monitoring, imaging, and mapping, as well as GIS capabilities and predictive models to help improve environmental decision-making among developing nations. Among other applications, Servir helps end users to rapidly respond to and assess damage from natural disasters such as floods.

With regard to remote sensing of floods, one precursor success story of Servir is without doubt the implementation of an operational flood forecasting system based on radar altimetry in Bangladesh (Figure 13). The operational forecasting system based on radar altimetry provides water level forecasts with acceptable accuracies. The system is now operated by Bangladesh’s Flood Forecasting and Warning Center and serves the entirety of the Ganges-Brahmaputra-Meghna river basin complex as well as more than thirty flood-prone nations in the region, currently deprived of real-time flow data from upstream nations (Hossain et al., 2014).

The CEOS Flood Pilot

The working group on disasters of the Committee on Earth Observation Satellites (CEOS) aims to deliver satellite data seamlessly to end users during natural disasters. The working group’s Flood Pilot has been leveraging and coordinating a number of ongoing projects, particularly in three regions (the Caribbean, southern Africa, and Southeast Asia). One of the Flood Pilot’s main objectives is to create a Global Flood Dashboard to serve as a “one-stop shop” for information from a number of existing systems for monitoring and predicting floods in real-time, as also argued by Schumann and Domeneghetti (2016) in an Invited Commentary on the proliferation of satellite data. Pilot regions are encouraged to develop a basic capacity to access data and include them in their decision-making process.

Global Flood Mapping, Detection, and Forecasting

The Dartmouth Flood Observatory (DFO) and NASA’s NRT Flood Mapping

The Dartmouth Flood Observatory (DFO) conducts global remote sensing-based flood mapping and measurements in near-real time (NRT) and archives this information. The DFO also performs global hydrological modeling, which it integrates with its global surface water mapping. Collaborating and partnering with a number of humanitarian and flood disaster emergency management agencies, such as the United Nations World Food Programme (UN WFP), ensures maximum utility of the information. The DFO is most known for its rapid flood mapping with the MODIS instrument onboard NASA’s Aqua and Terra satellites (Figure 13). The observatory offers two map series accessible from the global index: “Current Flood Conditions,” providing daily, satellite-based updates of surface water extent, and the “Global Atlas of Floodplains,” a remote sensing record of floods, 1993 to 2015. These systems have been sustained by grants and contracts, among others, from NASA, the European Commission, the World Bank, and the Latin American Development Bank.

NASA’s NRT flood mapping is similar to the DFO, and in fact, since 2012, feeds its maps seamlessly to the DFO. The LANCE processing system at NASA Goddard provides such products typically within a few hours of satellite overpass. As with the DFO, open water is detected using a ratio of MODIS bands in the visible and near-infrared at 250 m spatial resolution. The impact of clouds is minimized by compositing images typically over two or more days. Flooding is classified as anomaly to a reference water layer denoting “normal” water extent.

Global Radar-Based Flood Mapping on ESA’s G-POD System

Also in a global context, ESA hosts a SAR-based mapping tool on their Grid-Processing on Demand (G-POD) system, which is freely available to end users, who can query the ESA SAR database for a flood image and retrieve an automatically generated flood map. The mapping algorithm is based on a region-growing algorithm refined by change detection and works on different SAR image modes and resolutions (Matgen, Hostache, Schumann, Pfister,Hoffmann, & Savenije,2011).

Global Flood Detection System (GFDS)

The Global Disaster Alert and Coordination System’s (GDACS) experimental Global Flood Detection System (GFDS) monitors floods worldwide using near-real time passive microwave remote sensing from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Dynamics over a large inundated area can be observed when surface water increases significantly and affects the emitted microwave signal, which allows the system to flag the area as flooded. Time series are calculated in more than 10,000 monitoring areas, along with flood maps and animations.

Global Flood Monitoring System (GFMS)

Real-time quasi-global hydrological calculations at 1/8th degree and 1 km resolution inundation simulations are performed with the University of Maryland’s Global Flood Monitoring System (GFMS), which is a NASA-funded experimental system that uses real-time TRMM Multi-satellite Precipitation Analysis (TMPA) data and now the iMERG product from the Global Precipitation Measurement (GPM) mission. This system issues flood forecasts with 4–5 days lead time and mapping of inundation at a 3-hour time step. Although at a relatively low resolution, the timeliness of the system and its forecast capability make it very attractive to flood relief services and flood disaster response organizations worldwide (Figure 13).

Primarily in the developing world, global initiatives and systems such as presented in this article are now becoming established and recognized by many nations that are in need of assistance because traditional ground-based monitoring systems are sparse and in decline. Satellite data only realize their full value if those who can benefit from these data know where and how to access the many products and services, and know how to use them. Achieving this is still extremely challenging, however, especially in the area of natural disasters such as floods, where the data are needed only infrequently and many satellite acquisitions of floods are still inherently “opportunistic,” with the exception of the International Charter.

Nevertheless, the value satellites can offer during floods is growing rapidly, and the challenge now lies in ensuring sustainable and interoperable use as well as optimized distribution of remote sensing products and services for science as well as operational assistance (see discussions by e.g., Hossain et al., 2016 as well as Schumann & Domeneghetti, 2016).

The Era of Data Proliferation and Interoperability

For about 40 years, with a proliferation over the last two decades, remote sensing data, primarily in the form of satellite and airborne imagery and altimetry, have been used to monitor, map, and study floods (Figure 14). The sensors and data processing techniques that exist to derive information about floods are numerous. During recent years, scientific contributions in the field of remote sensing of floods have increased considerably, and science has presented innovative research and methods for retrieving information content from multi-scale coverage of disastrous flood events all over the world. Progress has been transformative, and the information obtained from remote sensing of floods is becoming mature enough not only to be integrated with computer simulations of flooding to allow better prediction, but also to assist flood response agencies in their operations.

Remote Sensing of FloodsClick to view larger

Figure 14. Graphical illustration of past, current, and future satellite missions relevant to the monitoring of surface water, floods, and reservoirs. A: Imagery-sensor missions. B: Altimetry missions. Modified from Schumann and Domeneghetti (2016).

This said, numerous challenges remain and need to be resolved if the current trend of data proliferation continues, and even more importantly, if remote sensing of floods is to assist flood disaster response worldwide in a timely manner, so that relief teams and decision makers can act quickly, and organizations all around the world can deal swiftly with successive large events and deliver relevant geospatial data and flood imagery when and where needed.

Most major challenges related to remote sensing of floods have been alluded to in the previous sections. Generally speaking, the most pressing challenge is related to widespread confusion about what data and products there are, where and when they can be accessed, and what information they actually contain that is relevant to decision making at hand. This is further complicated by the fact that this “confusion” may now be as common among scientists and product developers as it is to the end-user communities.

Central to solving current challenges is the notion of data interoperability. Interoperability of data or a system means that data and system interfaces are completely understood, work with other products or systems, present or future, in either implementation or access, without any restrictions (Figure 15). Interoperability is certainly a welcomed concept in the era of data, products, and system multiplicity. Achieving this, however, is easier said than done and could take many years or even decades.

Remote Sensing of FloodsClick to view larger

Figure 15. Illustration of data interoperability in the context of the development and dissemination of remote sensing–based flood products. A new “community of practice” (CoP) needs to form in order to engage the different communities involved. A data interoperability platform connects traditionally disparate and separated data streams and establishes a seamless connectivity across all parts of the new CoP network. Interoperability increases access to information and timeliness of distribution, thereby leveraging information in more valuable ways, all while maintaining the integrity and intellectual property of the data and products exchanged.

One of the most notable examples that integrates remote sensing of floods, big data processing technology, and interoperability is the Water Observations from Space (WOfS) dataset provided by Geoscience Australia (Mueller et al., 2016). WOfS is a web service, available as an online viewer and as a Web Mapping Service (WMS), displaying historical surface water observations derived from satellite imagery for all of Australia from 1987 to 2014 (Figure 16).

The purpose of this big dataset is to map from continent-wide satellite imagery (acquired by Landsat-5 and -7) where water is frequently present, where it is rarely observed, and where inundation of the surface (i.e., flooding) has been occurring. In addition to the frequency of water detection, the service provides a confidence level that a water observation in a given location is correct. Over- and under-estimation of surface water can occur in the presence of clouds, shadow, steep slopes, tall and dense vegetation, and urban areas, as well as in occasional snow cover.

Given the relatively long repeat cycle of the Landsat satellite series of 16 days, not all historical floods may have been observed. Also, in places like dams, where changes in water drainage and infrastructure take place over time, past water observations may no longer be adequate to infer future probabilities of water observations.

The WOfS project began in 2011, and is now complete, but data will continue to be updated every three months. For storing, organizing, and processing the huge data volumes, the project uses a high performance compute structure known as the Australian Geoscience Data Cube (AGDC), designed for large-scale parallel processing. For a complete scientific account of WOfS, the reader should refer to Mueller et al. (2016).

Remote Sensing of FloodsClick to view larger

Figure 16. A snapshot of the online WOfS viewer. © Geoscience Australia.

Big and potentially globally available and free datasets from satellites like the WOfS are expected to change ways in which we understand processes such as floods at global scales. It is clear that the wealth of remote sensing information that can be produced and disseminated in general and also during a flood disaster is much under-utilized by end users. As an example, the reader should refer to the commentary article on the Texas flood disaster of 2015 by Schumann et al. (2016).

This underutilization is due to a number of reasons, most of which relate to the relative novelty of these data: (a) limited time and personnel capacity to understand, process, and handle new types of geospatial datasets; (b) limited near-real time data accessibility, bandwidth, and sharing capacity; (c) incompatibility between user platforms and geospatial data formats; (d) data availability may be simply unknown and/or data latency may be inadequate; and (e) limited understanding by scientists and engineers about end-user information product and timing needs.

To address this frequently encountered mismatch between data availability and end-user needs, the scientific community should seek to collaborate closer with end users, as advocated by Hossain et al. (2016). A step in the right direction would be to build a “one-stop-shop” (i.e., data portal) dedicated to the remote sensing of floods and, more broadly speaking, river-related variables (Schumann & Domeneghetti, 2016). In a first instance, this could be data and products on water level and flood area.

On the one hand, scientists and product developers could collect and synthesize knowledge on this platform as well as data from past, present, and for future EO missions while sharing algorithms, accuracy assessments, and documentation. On the other hand, decision makers could pull data and products from this portal and request tailored information layers as needed for their operations. Other flood-related information that could be made available alongside remote sensing data are output layers from models such as those produced by the GFMS for flood wave lead times using satellite rainfall, by Sampson et al. (2015) for flood hazard return period layers, or indeed as proposed by Dottori et al. (2016) for the Global Flood Awareness System (GloFAS).

Figure 17 illustrates a great example of the appealing complementarity between flood hazard model simulations and remote sensing information on flooding. Although this exists at the moment only for the whole of Australia, recent advances in high performance cloud computing, large-scale flood hazard modeling, and online high-end data analytics platforms geared towards interoperability should enable such efforts to be extended to the global scale.

Remote Sensing of FloodsClick to view larger

Figure 17. Number of times water was detected between 1987 and 2014 by Landsat-5 and -7. Frequently observed water (such as permanent lakes and dams) is shown in purple and blue, down through green to infrequently observed water (such as floods) in yellow, and finally to very low percentages in red. © Geoscience Australia. Overlay map of ~17,000 km2 showing maximum inundation depth over a 40-year simulation period (Schumann, Andreadis, & Castillo, 2013a) downscaled onto the 90 m SRTM-DEM on top of the 28-year Landsat observations (predicted correct [flooding]: 89.6%; area in error: 10.9%).

Certainly, another desirable add-on feature would be to use model forecasts, complemented and verified by social media, to pinpoint target regions for satellite image acquisition and delivery of flood products. Product formats should allow high interoperability and thus integrate seamlessly with end-user operation systems and platforms and, ideally, should also provide relevant, simple, and timely information, at low bandwidth, that can be accessed by anyone from anywhere at any time on any device.

Such a clearing house would not only organize and structure data availability better, thereby clarifying existing confusion over data and products; it would also create innovation and improve added value of the different data products. In addition, the platform could be used for algorithm development and testing since EO data can be more easily exploited. Ultimately, methodological frameworks and best practice guidelines can be defined and established. At the same time, the end-user community should have the opportunity to leave feedback on data and products, which should in turn be used to improve the different types of information disseminated.

Despite the notable progress in recent years in remote sensing technology, data processing, and interoperability, the lack of a clear framework to date for a systematic selection and extraction of actionable information, as well as the lack of a coordinated dissemination process of such information, seriously limits the use of remote sensing in practical and operational applications, especially during flood disasters. Consequently, much effort from the scientific community is needed to change the current situation and make products not only more accessible but also more credible and thus more valuable to the many potential end users worldwide.

Outlook

There is now a general consensus among space agencies to strengthen the support that satellites can offer in relation to floods. Especially during flood disasters and for response operations, there is a strong need for near-real time (NRT) acquisition and processing of satellite data, so derived products can assist field organizations in a timely manner. This is particularly true when other information is scarce or not easily accessible.

However, remotely sensed data often require calibration, pre- and post-processing, and some form of validation, all with the common end goal to increase information content and credibility. In addition, the many ancillary and auxiliary geospatial data that may be available, such as field data collected or flood hazard forecasts, make timely dissemination of information challenging. Consequently, of the many products that are produced from remote sensing, most are either not in the desirable format, or end users such as emergency managers may simply be unaware of their existence.

Despite the many challenges and non-trivial issues, remote sensing has clearly shifted flood science and applications from a data-poor to a data-rich environment in the past 15 years. This shift is embodied in the upcoming NASA/CNES Surface Water Ocean Topography (SWOT) mission, which is the first satellite mission dedicated to hydrology. The satellite carries a Ka-band radar planned for continuous operation, and as such it will be of significant advantage over most radar satellites currently operating. The proposed revisit time would be 21 days, with more frequent sampling at mid-latitudes (Biancamaria, Lettenmaier, &Pavelsky,2015). The mission, with an expected launch date of 2021, would allow invaluable data gathering for hydrology at the global scale. Information about water bodies (lakes and rivers), including floods, would be collected at an average pixel spacing of 50 m at each satellite overpass.

SWOT as well as the many current and planned remote sensing platforms, onboard aircrafts, and satellites, provide datasets with great potential for enhanced monitoring, measuring, and mapping of floods, improving flood models through new data assimilation techniques, and scaling of processes within models (Schumann & Moller, 2015). This situation will lead to innovative ways in which new data may not only advance science (e.g., by providing more accurate flood forecasts), but also better assist end users in their decision making.

Having said that, both the science and end-user communities need to collaborate closely to address the many challenges that lie ahead. Of particular importance is the development of more computationally efficient and robust operational image mapping algorithm for floods and flood damage assessment, which would ideally be independent of image properties such as spatial resolution, spectral signature, or viewing angle, and can be applied in a variety of environments. Also in this context, more research and applied case studies are needed to ultimately demonstrate to decision makers that utilizing satellite data should be an integral part of flood disaster management and relief operations. Here, a “one-stop-shop,” dedicated to remote sensing of surface water measurements including floods, as outlined earlier, is on the top of many people’s wish list.

Last but not least, as noted by Schumann (2015) in an editorial on remote sensing of floods, with a proliferation of free Earth Observation data now and in the near future, there is a need not only to understand the limitations and errors of the data and methods but also to develop more sophisticated data processing algorithms, as well as robust frameworks for handling the many heterogeneous geospatial data sets and for effective information management and transfer across networks. This becomes even more pressing as communities will see more data and products coming from emerging technologies in fast-growing sectors, such as micro- and nano-satellites, and unmanned aerial vehicles (UAVs), which are already becoming a widespread reality.

Conclusion

In recent years, there has been a significant increase in the number and types of satellite instruments that can be used to map floods and infer information about flooding. Of course, the ability to monitor floods with sensors mounted on aircraft and satellites has been known for decades. The availability of aerial photography and early launches of satellites allowed investigation of the potential value to map and monitor floods, and to support flood management applications. Over the years there has been much stimulating research in this area, and significant progress has been achieved in fostering our understanding of the ways in which remote sensing can support and advance flood modeling, even flood forecasting, and assist flood disaster response operations.

This article reviewed the utility of remote sensing, mostly from satellites, to map and monitor floods. Examples of applications in different landscape settings and at various spatial and temporal scales have been illustrated. Many current and upcoming satellite missions are collecting data that can inform directly or indirectly about water bodies and flood inundation processes. This data proliferation has shifted the research and application fields in the area of remote sensing of (flood) hydrology from a data-poor (pre-2000) to a data-rich (post-2000) environment. Consequently, innovative methods and products from these data have been developed over the years, which have led not only to better understanding of flood processes at various spatial and temporal scales and better flood forecasts, but also to global initiatives and applications that utilize and promote remote sensing for improved decision-making activities, particularly in developing nations.

Global-scale initiatives and end-user oriented applications are now becoming established and also recognized by many nations that are in need of assistance because traditional ground-based monitoring systems are sparse and in decline. The value remote sensing can offer is growing rapidly, and many challenges lie ahead. New sensor technologies, for instance light-weight, small satellites and drones, now add many terabytes of new data every day, and as a result, innovative and powerful online data analytics platforms are being offered to retrieve actionable information from these data.

For remote sensing of floods, the grand challenge now lies in ensuring sustainable and interoperable use as well as optimized distribution of remote sensing products and services for science and end-user applications, as well as for operational flood disaster assistance.

Suggested Readings

Alsdorf, D. E., Rodriguez, E., & Lettenmaier, D. P. (2007). Measuring surface water from space. Reviews of Geophysics, 45(2).Find this resource:

Bates, P. D., Horritt, M. S., Smith, C. N., & Mason, D. (1997). Integrating remote sensing observations of flood hydrology and hydraulic modelling. Hydrological Processes, 11, 1777–1795.Find this resource:

Bates, P. D., Neal, J. C., Alsdorf, D., & Schumann, G. J.-P. (2014). Observing global surface water flood dynamics. Surveys in Geophysics, 35(3), 839–852.Find this resource:

Carbonneau, P. E., & Piégay, H. (2012). Fluvial remote sensing for science and management. Chichester, U.K.: Wiley-Blackwell.Find this resource:

Di Baldassarre, G., & Schumann, G. (2011). Recent advances in mapping and modelling flood processes in lowland areas. Physics and Chemistry of the Earth, Parts A/B/C, 36(7–8), 221–334.Find this resource:

Hossain, F. (Ed.). (2016). Earth science satellite applications: Current and future prospects. Cham, Switzerland: Springer International Publishing.Find this resource:

Hossain, F., Serrat-Capdevila, A., Granger, S., Thomas, A., Saah, D., Ganz, D., et al. (2016). A global capacity building vision for societal applications of Earth observing systems and data: Key questions and recommendations. Bulletin of the American Meteorological Society, 97(7), 1295–1299.Find this resource:

Marcus, W. A., & Fonstad, M. A. (2008). Optical remote mapping of rivers at sub-meter resolutions and watershed extents. Earth Surface Processes and Landforms, 33, 4–24.Find this resource:

Paron, P., Di Baldassarre, G., & Shroder, J. F., (2015). Hydro-meteorological hazards, risks and disasters. Boston: Elsevier.Find this resource:

Schumann, G. J.-P. (2015). Remote sensing in flood monitoring and management [Special issue]. Remote Sensing, 7(12).Find this resource:

Schumann, G. J.-P., Bates, P. D., Horritt, M. S., Matgen, P., & Pappenberger, F. (2009). Progress in integration of remote sensing derived flood extent and stage data and hydraulic models. Reviews of Geophysics, 47.Find this resource:

Schumann, G. J.-P., Frye, S., Wells, G., Adler, R., Brakenridge, R., Bolten, J., et al. (2016). Unlocking the full potential of Earth Observation during the 2015 Texas flood disaster. Water Resources Research, 52(5), 3288–3293.Find this resource:

Schumann, G. J.-P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts A/B/C, 83–84, 84–95.Find this resource:

Smith, L. C. (1997). Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological Processes, 11(10), 1427–1439.Find this resource:

Yan, K., Di Baldassarre, G., Solomatine, D. P., & Schumann, G. J.-P. (2015). A review of low-cost space-borne data for flood modelling: Topography, flood extent, and water level. Hydrological Processes, 29(15), 3368–3387.Find this resource:

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Schumann, G. J.-P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts A/B/C, 8384, 84–95.Find this resource:

Schumann, G. J.-P., Neal, J. C., Mason, D. C., & Bates, P. D. (2011). The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics: A case study of the UK summer 2007 floods. Remote Sensing of Environment, 115, 2536–2546.Find this resource:

Schumann, G. J.-P., Neal, J. C., Voisin, N., Andreadis, K. M., Pappenberger, F., Phanthuwongpakdee, N., et al. (2013b). A first large scale flood inundation forecasting model. Water Resources Research, 49, 6248–6257.Find this resource:

Simard, M., Pinto, N., Fisher, J., & Baccini, A. (2011). Mapping forest canopy height globally with spaceborne LiDAR. Journal of Geophysical Research, 116(G4).Find this resource:

Tourian, M. J., Sneeuw, N., & Bárdossy, A. (2013). A quantile function approach to discharge estimation from satellite altimetry (ENVISAT). Water Resources Research, 49(7), 4174–4186.Find this resource:

Tourian, M. J., Tarpanelli, A., Elmi, O., Qin, T., Brocca, L., Moramarco, T., et al. (2016). Spatiotemporal densification of river water level time series by multimission satellite altimetry. Water Resources Research, 52(2), 1140–1159.Find this resource:

Yan, K., Di Baldassarre, G., Solomatine, D. P., & Schumann, G. J.-P. (2015). A review of low-cost space-borne data for flood modelling: Topography, flood extent, and water level. Hydrological Processes, 29, 3368–3387.Find this resource: