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International Journal of Applied Earth Observation and Geoinformation 13 (2011) 536–544

Contents lists available at ScienceDirect

International Journal of Applied Earth Observation andGeoinformation

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apid response flood detection using the MSG geostationary satellite

imon Richard Proud ∗, Rasmus Fensholt, Laura Vang Rasmussen, Inge Sandholtepartment of Geography and Geology, University of Copenhagen, Øster Voldgade 10, DK-1350, Copenhagen, Denmark

r t i c l e i n f o

rticle history:eceived 10 March 2010ccepted 6 February 2011

a b s t r a c t

A novel technique for the detection of flooded land using satellite data is presented. This new methodtakes advantage of the high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager(SEVIRI) aboard the Meteosat Second Generation (MSG) series of satellites to derive several parameters

eywords:eteosat Second Generation

loodingRDFnisotropy

that describe the sensitivity of land surface reflectivity to variation in solar position throughout the day.Examination of these parameters can then yield information describing the nature of the surface beingviewed, including the presence of water due to flooding, on a 3-day basis. An analysis of data gatheredduring the 2009 flooding events in West Africa shows that the presented method can detect floods ofcomparable size to the SEVIRI pixel resolution on a short timescale, making it a valuable tool for large

EVIRI scale flood mapping.

. Introduction

Floods are the most frequent of natural disasters, affecting morehan one billion people and killing in excess of 100,000 between002 and 2010 (EM-DAT, 2011a), with similar statistics beingeported for the previous decade (Jonkman, 2005; Alcántara-Ayala,002). Despite this, few techniques exist for the rapid detectionnd monitoring of flooded land. Typically such techniques, wherehey do exist, are based on local knowledge, news reports and gov-rnmental information, such as the EM-DAT and Dartmouth Floodbservatory databases, or require in situ monitoring of water con-itions using gauging stations placed at numerous intervals along aiver’s course. The latter method produces good quality results buts expensive, which is a particular concern in the developing world,

here the majority of people affected by such flooding events live. Its therefore important to develop global techniques for flood detec-ion, particularly as it is predicted that climate change may lead to

ore frequent and more severe flooding in the future (Kleinen andetschel-Held, 2007; McGranahan et al., 2007).

Limited flood mapping from space has been achieved by exam-ning changes in the Normalised Difference Vegetation or Waterndices (NDVI and NDWI respectively) due to the presence of

ater on the land surface (Sanyal and Lu, 2004; Jain et al., 2005;cFeeters, 1996). But NDVI is designed to monitor vegetation, and

o is unsuitable for flood mapping if very sparse or dense vege-

ation is present (Beget and Di Bella, 2007). Recently the Globalisaster Alert and Coordination System (GDACS) has implemented

method to allow flood monitoring on a daily or bi-daily basis

∗ Corresponding author. Tel.: +45 35 32 25 84; fax: +45 35 32 25 01.E-mail address: srp@geo.ku.dk (S.R. Proud).

303-2434/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.jag.2011.02.002

© 2011 Elsevier B.V. All rights reserved.

from space using microwave sensors such as NASA’s AMSR-E (DeGroeve et al., 2006; Brakenridge et al., 2007). This method pro-vides a spatial resolution of 100 km2, meaning that small floodsare not visible. Countering this, its use of microwave radiationallows water to be visible despite cloud cover – something thatis a severe problem when examining the surface within the Visi-ble or Near InfraRed (VNIR) wavelengths. A recent study of flooddetection using data from AMSR-E over Namibia produced positiveresults, with the majority of flood events being correctly identifiedand mapped (De Groeve, 2010). Additionally, satellite constella-tions such as COSMO-SkyMed allow for the analysis of flood eventsat high spatial resolution (Boni et al., 2008; Pierdicca et al., 2010;Hahmann et al., 2008). There has also been much work in integrat-ing satellite measurements into hydrological models, including themeasurement of precipitation and soil moisture (Sandholt et al.,2003a; Ottlé and Vidal-Madjar, 1994; Chen et al., 2005), but thesetypically focus on producing higher quality hydrological modelsrather than on the detection and mapping of flooded land. It hasbeen shown (Sandholt et al., 2003b) that optical sensors such asAVHRR can be useful in the detection of flooded land, although caremust be taken when employing such approaches to minimise theeffects of vegetation and other surface features. With the advent ofsensors such as the Spinning Enhanced Visible and InfraRed Imager(SEVIRI) aboard Meteosat Second Generation (MSG) that producedata every 15 min (Aminou, 2002), it is now possible to gain cloud-free VNIR observations of land surfaces much more rapidly thanbefore. It has been shown that the land surface can be viewed onmultiple occasions on a better than 3-day timescale with SEVIRI

(Fensholt et al., 2007). SEVIRI records the top of atmosphere (ToA)reflectance in three VNIR spectral bands that are named channels 1,2 and 3. These are centred on wavelengths of 635, 810 and 1640 nmrespectively, providing a pixel spacing as good as 3 km/pixel.

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S.R. Proud et al. / International Journal of Applied Ea

ue to the dynamic and transient nature of flooding events, it isital to examine them with an instrument capable of rapid datacquisition. The MSG series of satellites fulfils this requirementell, due to its frequent imaging and fixed position relative to the

arth’s surface.The Earth’s surface is not equally reflective under all illumina-

ion conditions. Consequently, as the Sun moves across the sky overhe course of a day, the reflectance of the land surface will vary –n some cases by more than an order of magnitude (Coulson, 1966;riebel, 1978). The type of land cover present on the surface and

he wavelength of light being used have a large effect upon the sizef this diurnal variation.

This study shows that analysing the variation in surfaceeflectance as a function of the sun’s position allows for exami-ation of the properties of the land and, in particular, the ability toap areas that are flooded at a given time. As an example of this

ew technique a comparison is made to traditional flood detectionethods for the severe floods that occurred during mid-2009 inest Africa. These floods resulted in widespread damage, particu-

arly in the city of Ouagadougou, Burkina Faso, where the floodingffected more than 150,000 people. The severity of these floodsakes them useful for testing the SEVIRI flood detection method.

. Methodology

.1. Modelling diurnal reflectance trends

For most remote sensing applications, the variation in surfaceeflectance as a function of solar position reduces the accuracy of aata set and must therefore be minimised (Meyer et al., 1995). Toacilitate this, various models have been produced that use a Bidi-ectional Reflectance Distribution Function (BRDF) to describe theeflectance variation as a function of the illumination and view-ng conditions. One such model, originally designed for use withhe MODerate resolution Imaging Spectroradiometer (MODIS), isnown as the MODIS direct broadcast BRDF algorithm (Lucht et al.,000; Schaaf et al., 2002). It utilises a BRDF that models theeflectance as a series of three mathematical expressions, knowns kernels, that each describe a particular scattering method. Theelative strength of each of the scattering modes is strongly depen-ant upon the type of land being observed and hence it is possibleo gain information about the land surface by using the BRDF (Gaot al., 2003; Diner et al., 2005). Water, in particular, displays a veryistinct set of scattering patterns and these can therefore be usedo identify areas that are wholly or partially submerged.

This BRDF algorithm combines three kernels in order to calculatehe surface reflectance, R, at a wavelength � for a given solar zenithngle �, viewing zenith angle ϑ and relative azimuth angle �:

(�, ϑ, �, �) = fiso(�)Kiso + fvol(�)Kvol(�, ϑ, �)

+ fgeo(�)Kgeo(�, ϑ, �) (1)

he three kernel values are represented by K, with Kiso beingqual to 1, whilst Kvol and Kgeo are functions only of the solarnd viewing geometry and do not depend upon the land surfaceeflectance itself. The properties of the land surface are accountedor by the BRDF parameters, fiso, fvol and fgeo, that describe the sur-ace reflectance as a function of the different scattering modes:sotropic, volumetric and geometric. The isotropic parameter mea-ures the reflectance of the surface that is constant, no matter whathe geometrical conditions, and can be thought of as the reflectancehat would be measured if both the sun and sensor were nadir to

he target. The volumetric parameter describes scattering withinbjects such as tree canopies. Finally, the geometric parameter rep-esents the reflectance that can be modelled by scattering from aeries of discrete surface objects, such as buildings. By examining

servation and Geoinformation 13 (2011) 536–544 537

a time series of surface reflectance data and with knowledge of theSun’s position, it is possible to invert this model and hence derivethe values for each of the three parameters. Typically these param-eter values are then used to normalise reflectances to a commonset of viewing and illumination conditions, enabling the compari-son of data gathered in different locations and at different times ofyear. However, within this study the parameter values themselvesare used as the basis for the flood detection method – the actualreflectances are discarded.

2.2. Data used in this study

To test the ability of the BRDF parameters to detect flooded landa data source that supplies a large number of images within a shortspace of time is required. Previously, the BRDF has been calculatedfor instruments such as MODIS that provide a relatively high spa-tial resolution but can only collect, at best, one or two images ofa particular region each day (Justice et al., 1998). To calculate theBRDF a large number of observations is required, and thus datagathered over many days must be combined in order to success-fully retrieve the BRDF parameter values. Typically 8 or 16 days areused to produce one BRDF (Schaaf et al., 2002), which results ina highly accurate set of parameters but due to the long timescale,transient events such as flooding or fire may not be detected. Toovercome this, data from the MSG satellites can be used to generatethe parameter values on a timelier basis. The high temporal reso-lution of SEVIRI means that up to 60 sunlit observations of an areacan be recorded each day – more than enough to generate the BRDFparameters. However, tests showed that a 3-day acquisition timewas required. Using a shorter acquisition time resulted in much ofthe image being obscured by cloud, whilst a 3-day period allowedthe land surface itself to be examined with only a few unprocessedpixels due to cloud cover. This is still a substantial improvementon the MODIS 16 day timescale and should enable most flood-ing events to be detected. As the SEVIRI pixel resolution is at best3 km/pixel, localised flooding may not be visible, although majorflooding events will still be seen. At worst, no flooding events cov-ering an area of less than 9 km2 will be detected as flooded, althoughdepending upon the land cover for a pixel smaller floods may wellbe detected. Additionally, the spatial resolution decreases as pixelsfurther from the subsatellite point are examined. In the extremi-ties of Africa, such as Egypt and South Africa, resolution is closer to5 km/pixel. During normal operation the SEVIRI views an area cov-ering Africa, Europe and the Arabian Peninsula, but for the purposesof this study only data from a portion of this scene, known as theWest Africa subset, was examined. This subset covers the area from19◦ 34′N, 19◦ W to 4◦ 36′N, 8◦ 15′ E and many severe floods haveoccurred within this subset over the past several years. Between2002 and 2011 a total of almost 450,000 people were affected by 9separate flooding events in Burkina Faso, with particularly severefloods occurring in July 2007, September 2009 and July 2010 (EM-DAT, 2011b). Neighbouring countries have also been hit by largescale flooding. In the previous decade Mali experienced 11 eventsthat affected a total of more than 180,000 people, whilst 7 largefloods occurred in Niger – affecting more than 470,000 people. Forthe decade ending in 2009, West Africa as a whole experienced105 major floods that resulted in around 1150 deaths and affectednearly 3.5 million people.

Before ingestion into the BRDF algorithm, the SEVIRI top-of-atmosphere reflectances were masked to remove areas affected bycloud cover by applying the EUMETCAST MPEF cloudmask that isdistributed along with the raw SEVIRI data. The reflectances were

then corrected for atmospheric effects by using a modified ver-sion of the Simplified Method for Atmospheric Correction (SMAC)(Rahman and Dedeiu, 1994; Proud et al., 2010). Data was collectedbetween 0600 and 1800 UTC each day, a time span that covers

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538 S.R. Proud et al. / International Journal of Applied Earth Observation and Geoinformation 13 (2011) 536–544

F le imi the NiR rea in

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ig. 1. Locations of study areas and test sites in West Africa overlaid on a Blue Marbn Burkina Faso, with precipitation stations highlighted by circles and Region ‘C’ is

iver (1), a grassland scene dominated by low-lying vegetation (2), a water-filled a

lmost the entire sunlit period for West Africa. These images, alongith information about the position of the Sun for each image, were

hen fed into the ‘inverse mode’ of the BRDF algorithm to producehe BRDF parameters. In turn, the parameters were fed back into the

odel, this time operating in ‘forward mode’, to produce simulatedeflectances for each time slot. The relative difference between theeasured and modelled reflectances gives a good indication as to

he success of the BRDF algorithm at modelling the angular depen-ence of the land surface reflectivity. As such, any time slot in whichhe relative difference exceeds 10% is ignored and the parameterse-calculated with the remaining data. The final result is a BRDFodel (made up of the Iso, Geo and Vol parameters) on a three day

imescale that accurately describes the angular sensitivity of theand surface reflectance to the three scattering modes describedreviously. The three-day BRDFs are produced at the Universityf Copenhagen on a semi-operational basis, beginning on Januaryst of each year, meaning that 122 BRDFs are produced annually.roduction is not linked to flooding events, so if flooding is visibleithin a 3-day period then it may have occurred on any of the threeays that make up that BRDF. All raw data are archived, however,o to accurately determine the start of flooding events it is possibleo reprocess the data using different acquisition dates.

.3. Study areas

To determine the parameter values that can signify the presencef flooded land, a pixel on the Sokoto River in Nigeria was chosens a test site, and is labelled as point 1 in Fig. 1. At this location,he Sokoto River is situated on a flood plain approximately 10 kmcross, and the river is braided into many smaller waterways. Theood plain is therefore clearly visible to MSG, but the river itself

s smaller than the 3 km MSG pixel size, and so will only partiallynfluence the measured reflectance. The pixel is dry for much of theear but between late July and October the river fills with water and,n 2009, burst its banks – flooding a large area. The pattern of diurnaleflectance in the dry, wet and flooded seasons was examined and

ompared to other pixels nearby that represent known land coverypes. Point 2 in Fig. 1 is a typical grassland pixel, whilst point 3 is

good example of a bare soil area and point 4 is a pixel covered byater.

age from NASA’s Earth Observatory. Region ‘A’ is the Inner Niger Delta, Region ‘B’ isger River upstream of Niamey. The test pixels 1–4 are, respectively, on the SokotoLake Volta (3) and a sparsely vegetated scrub land pixel (4).

Furthermore, three regions were defined for use in a compar-ison between the MSG flood mapping technique and a variety ofother methods, described in the following paragraphs, that havebeen used to detect the presence of flooded land. All three regionsexperienced one or more severe floods in 2009 and are labelled asA, B and C in Fig. 1.

Region A is located in Mali, and covers from the Inner Niger Deltain North-Central Mali to the Burkina Faso border in the East. Thearea (13–16◦N) belongs to the central and northern parts of theSahel and experiences a typical semi-arid climate. The rainy sea-son extends from June to September (Roncoli et al., 2007), with Julyand August being the wettest months. Annual rainfall ranges from300 mm/year in the northern part of the region to 500 mm/year inthe South (Nicholson, 2005). However, the rainfall regime is charac-terised by great variability in both time and space, even within shortdistances rainfall may be very different (Rasmussen et al., 2001).Due to the presence of the Inner Niger Delta in this region floodingis a common occurrence – most frequently in August, September,December and January as a result of both local rainfall and theamount of water transported from other areas by the Niger Riveritself (Diarra et al., 2004). For this site a comparison was made todata gathered by the MODIS instrument at 500 m resolution aver-aged over an 8-day period, substantially longer than the MSG 3-daytimescale. As most flooding in the Inner Niger Delta is caused bythe river rather than precipitation, there was at least one clear-skyopportunity every 8 days that was usable to examine the land sur-face with MODIS. The technique developed at the Dartmouth FloodObservatory (Brakenridge and Anderson, 2006) was used to pin-point the areas of land covered by water, with the areal extent ofsurface water being output. This method compares the NDVI fromMODIS reflectance data during a specific 8-day period to that gath-ered at other times. By comparison to data gathered in previousyears it is possible to map the normal extent of a river during aparticular time period and compare it to the current extent. If theextent is significantly larger compared to previous years then it islikely that flooding is occurring.

Area B constitutes the easternmost part of Burkina Faso, stretch-ing from the Centre region in the North to the Centre-East regionin the South – close to the border with Togo. The area (10–13◦N)is situated in the southern part of the Sahel and experiences a

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rth Observation and Geoinformation 13 (2011) 536–544 539

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Table 1BRDF parameters for the Sokoto pixel in three sets of conditions: dry season, wetseason and flooded land.

Case Channel 1 Channel 2 Channel 3

Iso Vol Iso Vol Iso Vol

S.R. Proud et al. / International Journal of Applied Ea

emi-arid climate with a short rainy season, largely limited to theonths of July, August and September. The annual rainfall ranges

rom 500 mm/year in the North of the region to 800 mm/year in theouth and the general trend seen elsewhere in the Sahel towardsn increased annual precipitation in the last decade is also evidentn this region (Nicholson, 2005). The region differs from Mali as itontains no significant river system, meaning that any flooding isrimarily due to large amounts of precipitation within a short time.ecause of this, there is frequent cloud cover during the floodingvents and it is not possible to employ the MODIS instrument asas used for Area A. Instead, precipitation data from 3 sites spread

cross Area B was averaged and used as a proxy, as major rain eventsre typically accompanied by large flooding on a scale visible toSG (Bracken et al., 2008; Messager et al., 2006). The locations of

he three precipitation stations are marked by circles in Fig. 1.Area C is a 70 × 22 km corridor along the Niger river upstream of

he city of Niamey in Niger. In this area river flow data on the Nigeras compared to the MSG flood map. Data from a river station neariamey was provided by the Niger Basin Authority, allowing theolume of water passing through the station (the river discharge) toe compared to the flooded land mapped by MSG. Sudden increases

n the discharge can signal a flooding event, as the river may note able to contain the increased volume of water within its banks.his means that a dependence of the flood area upon the differen-ial of water discharge should be noticeable when examining dataathered in Area C (Usachev, 1983; Smith et al., 1996).

. Detection of flooded land

Within this study we examine the BRDF parameter values pro-uced from diurnal trends in land surface reflectance producedy MSG. The physical basis for this approach to flood detec-ion is straightforward, as water displays a very different diurnaleflectance curve to other land cover types. Because of this, theRDF parameters will vary dependant upon whether or not water

s present within a pixel. The isotropic parameter values specifyhe reflectance of the land surface and, as is expected, at timeshen flooding occurs the land will display a markedly different

eflectance to that seen outside of flooding events. By introducinghe volumetric parameter a measure of the variability in reflectances gained. The variation in the geometric parameter is small in rela-ion to the land surface type, and so is not used as an indicator ofood extent. The following analysis examines this approach, andhows that waterlogged areas of land display very different diurnaleflectance trends to dry areas. Because of this, they therefore show

different value for the isometric and volumetric BRDF parameters,eaning that a combination of the isotropic and volumetric values

nables a clear signal of flooding events to be generated.Fig. 2(a) shows the spectral characteristics of the Sokoto river

n the 4th April 2009: the dry season. At this time, the riverbeds almost completely dry and much of the vegetation has diedack. This leads to diurnal reflectance trends that very closelyatch those for bare soil, as shown in Fig. 2(d), that contain low

eflectances in the morning and evening with a reflectance peakear midday. Channel 3 displays much higher values than chan-els 1 and 2 due to the strong reflectance of bare soil in the near-IRJacquemoud et al., 1992). For a dry river the isotropic and volu-

etric parameters are shown in row 1 of Table 1. The isotropicarameter is substantially higher than the volumetric value, andypically somewhat higher than the maximum surface reflectance.uring the wet season the river fills with water, changing the diur-al reflectance trend, as shown in Fig. 2(b) for the 15th August 2007.

hannel 3 reflectances have decreased since the dry season, with aeak of around 0.15, and the distinctive shape visible in the dryeason has been replaced by almost constant reflectance valueshroughout the day. Nevertheless, a small midday peak is visible

Dry 0.284 0.019 0.436 −0.021 0.602 0.021Wet 0.195 0.055 0.453 0.070 0.522 0.167Flood 0.201 0.018 0.421 0.046 0.375 0.092

for all channels, and in the morning there is a reflectance increase.Channel 1 displays a slight ‘bowl’ shape in which the morning andevening reflectances are higher than those near midday. This is typ-ical of grassland (Fig. 2(e)) and water (Fig. 2(f)). For channels 2 and3 water and grass display opposing trends, with water showing aslight bowl shape and grass presenting a small midday increase. Acombination of these two reflectance trends results in the almostflat reflectance trend visible during the wet season, and thereforesignifies that the pixel contains both water and grassland. For wetseason conditions the parameters are located in row 2 of Table 1.The channel 2 and 3 isotropic parameters are now higher than thecorresponding reflectances, and the volumetric parameters in allthree channels are positive and non-negligible. The Sokoto riverbroke its banks in late August 2009 and the new diurnal trendis shown in Fig. 2(c). Channel 3 is now very low, and displays atrend almost identical to the water pixel in Fig. 2(f). Channel 2 isbroadly similar to the normal wet season conditions, but is some-what flatter in the early morning, and channel 1 now resembles itsdiurnal trend present in Fig. 2(a). The large peak visible at 07.30is due to cloudiness, not a surface feature. The change in channel3 reflectance trend is a good indicator of a substantial amount ofwater being present. The reversion of the channel 1 trend to thatfor bare soil indicates large amounts of sediment in the water, asat this wavelength there is little difference in reflectance betweenwet and dry soil (Jacquemoud et al., 1992), unlike for channels 2and 3. For this case the parameter values are in row 3 of Table 1.The return of the channel 1 volumetric parameter to a value lowerthan 0.05 is a useful flooding indicator. The isotropic parametersin channels 2 and 3 have now swapped over, with channel 2 beinghigher than channel 3.

The BRDF parameters for the Sokoto Pixel are shown for 2009in Fig. 3(a). It shows that the channel 1 volumetric parameter isfrequently less than 0.05, so it alone cannot be used as a floodingindicator. However, it is also clear that at the time of the floods thechannel 1 isotropic parameter was low, whilst for channel 2 it washigh. Similarly, during the flooded period the channel 3 isotropicparameter became lower than that for channel 2. By producing twonew indices, each known as a Water Index (WI), that are a combi-nation of these parameters it is possible to pinpoint times when theSokoto is flooded:

WI32 = Iso3 − Vol3Iso2 − Vol2

(2)

WI21 = Iso2 − Vol2Iso1 − Vol1

(3)

where Ison and Voln are the isotropic and volumetric parametersfor channel n. The isotropic parameter describes the majority ofreflectance variation caused by flooding, but by including the vol-umetric terms in the indices the accuracy of flood detection wasincreased. Even though the volumetric parameter is typically smallcompared to the isotropic term it contains much information aboutthe shape of the reflectance trend – and hence the presence of

flooded land. This is demonstrated by the effects of the Vol2 param-eter on the Sokoto reflectance trend. Fig. 3(b) shows the WI32 andWI21 values for the Sokoto pixel. A strong flood signal is seen attimes when WI32 is less than 0.9, WI21 is greater than 2.45 and

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0

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ig. 2. Diurnal reflectance trends for a variety of land cover types. (a) Dry season ri

ol1 is less than 0.05, and these values can be used as thresholds tondicate flooding.

By automated comparison of the two index values, WI32 andI21, to their thresholds across a whole image, pixels that are

ooded should be highlighted, enabling the large scale detectionf flooded land. To examine the validity of this automated floodetection, several test areas were defined in West Africa, allow-

ng analysis of the BRDF parameters in 2009. For all three test areashere is a strong relationship between the MSG derived flood extentnd that measured by other sources.

. Results and validation

.1. Comparisons to polar orbiting satellite data

The Niger Delta study region, Area A, shows a correlation of.882 between the flood extent measured by the MSG parame-

er method and that from MODIS, meaning that there is a goodt between the two techniques. Fig. 4(a) shows the variation in theapped flood area as a percentage of the entire 83,000 km2 area of

he region. Both MSG and MODIS show little flooded land during the

) wet season river, (c) flooded River, (d) bare Soil, (e) grassland, and (f) water.

dry season with the exception of day 163 when MSG shows a spikethat is caused by cloud contamination. From day 200 the beginningof the wet season is seen. Flooding becomes visible in the MSG dataon a small scale (approximately 1–2% of all observed pixels). TheMODIS data lags behind MSG due to its longer compositing period,but a gradual increase in water-covered land is also visible. On day241 there is a dramatic increase in the area of flooded land that cor-responds both to the arrival of water from upstream on the Nigerand a series of heavy rainstorms in the preceding days. A peak isthen seen on day 247, with the flood waters gradually receding afterthis day. The MODIS data show a similar trend, but the peak flood-ing occurs on day 258, 11 days later than MSG. Again this is due tothe long compositing time for MODIS, with the actual peak flood-ing occurring some time within the previous 8 days. This highlightsone of the primary drawbacks of using MODIS as a flood detectiontool. Using one day data means clouds are an issue, but in the 8-day data the exact times at which flooding occurs becomes unclear.

The shorter compositing time of MSG helps overcome this limita-tion. The actual areas detected as flooded by both methods closelymatch, although an exact comparison is hampered by the differ-ent temporal and spatial scales of MSG and MODIS. Over the entire

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b Burkina Faso, from May 30 th until October 27 th.A large spike is visible on day 245 , signifying the se-rious floo ding experience d in Ouaga dougo u on thatday. A previous, bu t less well reported, floo ding eventin other parts of Burkina Faso is visible on day 225 .

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c A 70 x22 km portion of the Niger up stream fromthe city of Niamey showing the corr elation betwee nfloo ded area and the discharge of the Niger River, asmeasured by a station near Niamey. Sud den jumps inthe discharge are acc ompanied by a corr espond ing risein the MSG floo ded area.

Fig. 4. Comparison of the total size of the MSG flooded area to a variety of other flooddetection techniques for several test sites in West Africa during 2009. A percentageis used rather than areal extent in (a) due to the differing spatial resolutions ofMODIS and MSG. (a) The Inner Niger Delta in Mali. The flooded area is calculated asa percentage of the whole region, and the MODIS Area is relative to the water levelon 1st January. As MODIS is on an 8-day timescale there can be a lag between thetwo data-sets, depending on when in the 8-days the flooding occurred. (b) BurkinaFaso, from May 30th until October 27th. A large spike is visible on day 245, signifyingthe serious flooding experienced in Ouagadougou on that day. A previous, but lesswell reported, flooding event in other parts of Burkina Faso is visible on day 225. (c)

n each figure denote the extent of a period in which the Sokoto was known, fromocal sources, to have flooded. (a) MSG BRDF Parameter Values for channels 1, 2 and, and (b) MSG Water and Vegetation Index Values.

ear 85% of flooded pixels are detected by both MSG and MODIS.uring the wet season this is reduced to 82%, primarily due to thebscuring influence of clouds in the MODIS images. Overall the MSGime series fits well with the MODIS equivalent, although cloudi-ess can cause unexpected changes in the area extent of flooded

and. Additionally, MODIS retains a better spatial resolution thanSG (one MSG pixel contains at least thirty six 500 m MODIS pixels)

ut is let down by the long compositing time due to the infrequentverpasses of the Aqua and Terra satellites that carry the MODISnstrument. By using the MSG BRDF parameters instead there is

gain in resolution within the temporal domain but a loss in thepatial domain. As flooding events usually occur very rapidly thisncreased temporal resolution can be most useful in flood detectionnd assisting those who have been affected by the flooding. Thever-estimation in flooded land by MSG evidenced within parts ofig. 4(a) also highlights another important point. The MSG BRDFata is capable of being used to detect flooding events that are ofmaller spatial extent than the 9 km2 pixel size. Comparison withhe MODIS data shows that the MSG flood flag is raised, even if only5% of the pixel is classed as inundated by MODIS. This means thathe flood area will be overestimated somewhat as the entire pixelill be classified as flooded. By performing a more detailed pixel

xamination it may be possible to extract a more accurate area forhe flooded land on a subpixel scale. For instance, by comparisonf the BRDF parameters at the time of flooding to those from aon-flood period it may be possible to produce an estimate of the

raction of the pixel affected by flooding.

.2. Comparison of flooded area

Here the differences between the MSG and MODIS methods in

etecting flooded land are examined. Fig. 5 shows the mapped floodxtent for the Inner Niger Delta (Area A in Fig. 1) on the 19th ofeptember 2009, which is a typical scene from this time of yearn terms of flood extent – but is atypical in that it is one of the

A 70 × 22 km portion of the Niger upstream from the city of Niamey showing thecorrelation between flooded area and the discharge of the Niger River, as measuredby a station near Niamey. Sudden jumps in the discharge are accompanied by acorresponding rise in the MSG flooded area.

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F detecM . (b) T

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ig. 5. A comparison of the graphical flood extent from both the MODIS and MSG

ODIS Channel 1 reflectances, with the Inner Niger Delta in the center of the image

ew days in which the majority of the area is cloud-free within onef the two daily MODIS overpasses. Fig. 5(a) shows the Channel 1eflectance from the MODIS sensor aboard Aqua at 250 m resolu-ion, and it is noticeable in the North of the image (around 16◦N)hat there is significant cloud contamination within the data, visibles very bright areas. This is also noticeable close to 14◦N, 3◦ 30′W,s well as in other smaller areas. The MODIS cloudmask was addedo the data in order to remove these areas from further analysis.ig. 5(b) shows the flood classifications by both MSG and MODIS, inhich the MODIS data has been downscaled to the 3 km MSG spa-

ial resolution. In total 8822 pixels within the scene are identified ason-flooded by both the MSG and MODIS methods, whilst 161 areetected as flooded by both methods. The MSG method highlights54 pixels as flooded that MODIS does not highlight, and 54 pixelsre flagged by MODIS as flooded – but not by MSG. All but one ofhe 54 pixels flagged only by MODIS are on the edge of a regionn which the MODIS data is cloud contaminated. The remaining 1ixel was inspected manually and does indeed show a flooded areahat is not detected by the MSG method. Of the 154 pixels that only

SG flags as cloudy, 129 are in areas for which the MODIS data wasasked as cloudy, and therefore no flooding indicator was present.

6 out of the remaining 25 pixels seem to be false detections bySG, and all occur in areas that are covered by dense vegetation

hat is close to the normal flow area of the river, indicating that theethod may require some additional work in order to be successful

n such conditions. A possible solution to this problem would be themplementation of a database of seasonal NDVI and BRDF parame-er values for each pixel. These could then be subtracted from the

easured data, which would leave only the residuals between nor-al and current pixel conditions – thus providing a clearer measure

f any flood signal that may be present. The down-side of this addi-ion would be that the method no longer relies solely on the current

SG data, but will also require historical information.The final 9 pixels flagged as flooded only by MSG were examined

n the MODIS data and do appear to be flooded. These pixels are

lso densely vegetated, but in this case the vegetation has raisedhe NDVI above the threshold used within the MODIS method tondicate flooding – meaning that the MODIS flood map erroneouslyists these pixels as non-flooded.

tion methods on the 19th of September 2009 for Area A, the Inner Niger Delta. (a)he MSG and MODIS classification of flooded land for the Inner Niger Delta.

This indicates that overall the MSG method is good at detectingthe spatial extent of flooded land when compared to the MODISmethod, with 86.56% of the 186 clear-sky pixels flagged as floodedby MSG also being flagged by MODIS. An additional 4.84% of pixelsbeing successfully detected as flooded by MSG, but not by MODIS,and 8.60% of pixels being false positives within the MSG data. Thesefalse positives are highly correlated with land cover type, and somodification of the parameter values, or the inclusion of NDVI orland cover maps into the method, may well help to rectify the falsedetections.

4.3. The relationship between MSG flooding and precipitation

For Area B the MSG data is compared to that from precipita-tion stations, and the results are shown in Fig. 4(b). The correlationbetween these two flood indicators is 0.58, significantly less thanthe MODIS to MSG correlation within Area A. This is caused by anumber of precipitation events that do not result in any floodedland in the MSG flood map. Such rainfall is evident between days170 and 180. It is possible that these rainfall events do not producea peak in flooded area as the land is very dry, enabling absorptionof large amounts of water and thus produce no significant flood-ing. Conversely, there is a flooding peak on day 206 that does notcorrespond to a rainfall peak. This flooding peak is caused by theMSG map showing significant flooding at the southern edge of theexamination area, a location not covered by any of the precipitationgauges used within this study. Examination of precipitation mea-surements from northern Ghana – slightly outside the Southerlyextent of Area B – shows a peak in rainfall around this day, however.Additionally, analysis of monthly data from the Tropical RainfallMonitoring Mission (TRMM) shows a large amount of rain fell insouthern Burkina Faso at this time. The primary flooding eventsof 2009 are both noticeable in the MSG and precipitation data.The first occurs on day 226, whilst the second is the more widelyknown floods on day 244 that inundated much of the Burkinan

capital, Ouagadougou. For both flooding events there are peaksin the MSG mapped area and in the precipitation, particularly forthe mid-August flood. It is therefore likely that the MSG mappedflood locations do correspond to actual floods within the region,

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lthough the addition of more precipitation measurements cov-ring a wider extent would increase the confidence in MSG floodapping techniques in the Burkina Faso area. This highlights one

f the primary problems with using ground based measurementsor flood detection: Reduced spatial scale. Ground measurementsan be very useful, and also highly accurate, when examining smallreas that are prone to flooding but they are less useful on a region-ide scale. Lack of data from some areas may mean that flooding

vents are not recorded, whereas the large view afforded by MSGllows flood monitoring that, whilst slightly less accurate, covers auch broader area.

.4. MSG detection of flooding resulting from river flow

For the final area, C, along the Niger river the discharge and MSGooded area are shown in Fig. 4(c). Examination shows that twoapid increases in river discharge that could signal a flood eventre noticeable. The first occurs between days 227 and 229 whenhe discharge increases from 550 to 900 m3/s. At the same timehe area of flooded land increases from 86 to 350 km2 and contin-es to increase for 6 days before peaking at 440 km2, during whichime the Niger discharge remained almost constant. The floodedrea then shrinks until a minimum of 156 km2 on day 156. Thepparent decrease in area is because of the land surface character-stics changing due to long term water coverage, altering the BRDFarameters and no longer producing the flood signal. Another rapid

ncrease in river discharge occurs between days 255 and 263 andgain there is a corresponding rise in the flooded area, although theeak flooded area is reached before the peak discharge. The BRDFarameter based flood mapping method is best at detecting excep-ional areas of flooded land rather than areas that are normallyater covered (such as lakes), primarily due to the large amounts of

ediment present in flood water. As with the August floods the MSGooded area drops despite high discharge levels being retained,gain due to the transformation of surface reflectance characteris-ics. This area shows that it is possible to map flooding in a way thatlosely matches the known state of a river, and it is clear that theSG technique is more suited to highlighting transient, short term,

ooding events rather than longer term waterlogged landscapes.

. Conclusion and perspectives

This study has shown that it is possible to derive a map of floodedand based upon the BRDF parameters produced from data gatheredy the MSG-SEVIRI instrument. While not yet ready for operationalse, the results derived from the BRDF parameters show a good fito flooding events that have been detected through other means.he MSG based BRDF method shows numerous advantages overther techniques, however. The high rate at which the satellite cap-ures land surface images can provide a much faster mapping ofood events than is possible with other space instruments, such asODIS. Additionally, the fact that the satellite provides a continen-

al scale view simultaneously means that flood mapping is possiblever a wide area. This is not achievable using ground based detec-ion procedures such as precipitation measurements and river flowata, as is illustrated in the case of the Burkina Faso study area.lood mapping using instruments such as AMSR-E is confined to

coarse spatial resolution (De Groeve, 2010). MSG’s 9 km2 resolu-ion, whilst able to map smaller flooding events than AMSR-E, is stillf insufficient quality to map small local scale flooding. However,s has been shown in other studies (Hervé et al., 2007), combining

he MSG data with those from other sources, such as MODIS data at00 m resolution (Brakenridge and Anderson, 2006) or data fromhe COSMO-SkyMed series of satellites (Caltagirone et al., 2002),ould avoid this issue and enable the high temporal resolution of

servation and Geoinformation 13 (2011) 536–544 543

MSG to be fused with the high spatial resolution of other sensors.Additionally, by a detailed investigation of known flooded areas,more accurate knowledge of the BRDF parameters associated withflooded land can be gained – allowing adjustment of the thresholdvalues and increasing the accuracy of flood detection. By combin-ing the BRDF parameter values with a land cover database it wouldbe possible to derive optimal thresholds on a per-pixel basis. Thismay remove some of the false positives that are visible in the timeseries, particularly for the Inner Niger Delta area. This BRDF basedflood detection scheme is also capable of detecting subpixel flood-ing, even if only 25% of a pixel is flooded. This enables more floodingevents to be detected, but naturally leads to an over-estimation inthe flooded area. Finally, although this technique has been demon-strated in West Africa it is equally applicable in all areas coveredby high temporal resolution satellite instruments. The Americas arecovered by the GOES series of satellites, whilst the Indian Subconti-nent and surrounding area is viewed by the CCD instrument aboardINSAT-3A. By using these satellites in addition to MSG it is possibleto gain worldwide flood mapping for the tropical regions.

Acknowledgements

The authors would like to thank the anonymous reviewers fortheir useful and detailed comments that they provided during thereview process. The Niger Basin Authority (NBA) are thanked forproviding the Niger river discharge data to the African MonsoonMultidisciplinary Analyses (AMMA) project, from where it wasretrieved for use in this study. Furthermore, they would like tothank UNOSAT, the Dartmouth Flood Observatory and ReliefWebfor providing background information on the 2009 West Africafloods. H. Nieto is thanked for his assistance in producing the floodmaps used within this paper. C. Schaaf, Q. Zhang and the BRDF groupat Boston University are thanked for their assistance in modifyingthe MODIS BRDF algorithm to function with MSG data.

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