Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

Environ Monit Assess (2011) 181:431–444DOI 10.1007/s10661-010-1839-z

Sediment grain size estimation using airborne remotesensing, field sampling, and robust statistic

Elena Castillo · Raúl Pereda ·Julio Manuel de Luis · Raúl Medina ·Javier Viguri

Received: 3 May 2010 / Accepted: 5 December 2010 / Published online: 20 January 2011© Springer Science+Business Media B.V. 2011

Abstract Remote sensing has been used since the1980s to study parameters in relation with coastalzones. It was not until the beginning of the twenty-first century that it started to acquire imagerywith good temporal and spectral resolution. Thishas encouraged the development of reliable im-agery acquisition systems that consider remotesensing as a water management tool. Neverthe-less, the spatial resolution that it provides is notadapted to carry out coastal studies. This articleintroduces a new methodology for estimating themost fundamental physical property of intertidalsediment, the grain size, in coastal zones. Thestudy combines hyperspectral information (CASI-

E. Castillo (B) · R. Pereda · J. M. de LuisDepartment of Geographic Engineeringand Techniques of Graphical Expression,Group of Cartography, Geodesyand Photogrammetry, Civil Engineering School,University of Cantabria, 39005, Santander, Spaine-mail: [email protected]

R. MedinaDepartment of Sciences and Techniques of the Waterand Environment, Civil Engineering School,University of Cantabria, 39005, Santander, Spain

J. ViguriDepartment of Inorganic Chemical and ChemicalEngineering, E.T.S.I. Industrialesy Telecommunicaciones, University of Cantabria,39005, Santander, Spain

2 flight), robust statistic, and simultaneous fieldwork (chemical and radiometric sampling), per-formed over Santander Bay, Spain. Field dataacquisition was used to build a spectral libraryin order to study different atmospheric correctionalgorithms for CASI-2 data and to develop algo-rithms to estimate grain size in an estuary. Tworobust estimation techniques (MVE and MCDmultivariate M-estimators of location and scale)were applied to CASI-2 imagery, and the resultsshowed that robust adjustments give acceptableand meaningful algorithms. These adjustmentshave given the following R2 estimated results: 0.93in the case of sandy loam contribution, 0.94 forthe silty loam, and 0.67 for clay loam. The robuststatistic is a powerful tool for large dataset.

Keywords Remote sensing · Intertidal sediment ·Airborne sensors · Grain size · Estimation ·Robust statistic

Introduction

Coastal zones and estuaries often act as recepta-cles of the contamination originated either by lo-cal or diffuse sources, forming deposits and poten-tial contamination of the sea. Sediments constitutean essential component of the aquatic ecosystems;in addition, they constitute an indicator of theinteractions between the natural and the anthro-

432 Environ Monit Assess (2011) 181:431–444

pogenic environments, as they are considered tobe the last drain of polluting agents introducedboth by natural processes as much as anthro-pogenic ones (Salomons and Brils 2004; Alvarez-Guerra et al. 2008).

In order to answer the increasing legal andsocial demands for a greater environmental pro-tection of the aquatic resources, the scientificcommunity has developed a variety of methodsto evaluate the sediment quality (DelValls et al.2004; Alvarez-Guerra et al. 2007a, b). The usesof environmental quantitative quality standardsderived from sediment quality guidelines are be-coming an important part of the protection andmanagement of marine ecosystems (CCME 2001).In this context, action levels (ALs) have beenestablished as criteria for dredged material man-agement in different decision frameworks.

The initial tiers of sediment quality assess-ment frameworks and the use of ALs to eval-uate options management of dredged sedimentsinvolve the compilation of sediment physicochem-

ical data; the properties such as grain size, min-eralogy, and organic matter are related to thesediment retention capacity, to the changes in thestream hydrodynamic and morphology, as well asmaterial to the dredged treatment or beneficialuses (Bolam et al. 1995; Robinson et al. 2005).

Remote sensing appears to be a feasible al-ternative (Van der Wal and Herman 2007) as itconstitutes a synoptic tool that provides globaland quantitative information of the area understudy. Both satellite and airborne remote sensingtechniques have become useful and effective toolsto carry out coastal and oceanic pursuits (Folving1984; Bartholdy and Folving 1986; Doerffer et al.1989; Yachts et al. 1993; Populus et al. 1995a, b).

Study area and airborne sensor

The airborne sensor selected for monitoring hasbeen CASI-2 because its spatial resolution (be-tween 2 and 10 m) is suitable and it is possible toidentify optimum time windows, and the experi-

Fig. 1 Location of the study area and the field of work. (a) CASI airborne sensor. (b) Portable field spectroradiometer usedin the study ASD-FR. (c) Sediment sampling

Environ Monit Assess (2011) 181:431–444 433

ment would provide best chances of success, eventhough the working bandwidth is not the optimal(400–750 nm) because it only provides informa-tion in the part of the electromagnetic spectrumcorresponding to the visible and the very nearinfrared (VNIR). In this case, we have selected36 spectral bands to use and one space resolutionof 2 m.

The study area is located around the San-tander Bay, located in the Cantabric Sea along thenorthern coast of Spain (Fig. 1). Several touristactivities take place in the area, such as beachswimming, fishing, and sailing. Industrial activi-ties, mainly metallurgical and chemical factories,are distributed in the eight municipalities with250,000 inhabitants (35% of the Cantabria region)and a population density of 1,134 inhabitants persquare kilometer.

It is also a rich ecosystem, where much fishingand shell animal collection is performed. Severalrivers discharge into the Bay; the most importantrivers support the Boo, Solia, and San Salvador es-tuaries in the southern portion of the Bay and theCubas Estuary in the western portion of the Bay.

The mining activity around the Santander localBay was related to the exploitation of iron oresduring the last century, generating huge amountsof muddy iron-enriched wastes that were directlydischarged into the Solía and San Salvador estuar-ies. Additionally, there were some minor exploita-tion of Pb and Zn in the eastern part of the Bay(Ortega 1986).

Because of the mining and industrial and com-mercial port-related activities, significant dredg-ing and refilling has been carried out in the Bayhistory since 1837, leading to a reduction of 87%of the natural coast.

The main goal of the field work is to corre-late satellite and real-time spectral ground mea-surements and to collect soil samples (Fig. 1) atspecified points for developing algorithms on theestimation of the sediment and the bottom.

Methodology

The methodology used to reach our aim has thefollowing parts: the first one would be to knowthe spectral answer of different land bodies. The

second one, to dispose of imagery with an accept-able atmospheric correction to be able to applythe algorithms from the spectral library gener-ated from the knowledge of different land bodiesand to determine the error on the plans obtainedfrom satellite imagery. The last step is to applyrobust statistic techniques to CASI-2 informationand evaluate the results in comparison to classicalestimation techniques.

Several studies have been done for the deter-mination of the sediment textures, applying re-mote sensing techniques to multispectral satelliteimages in the VNIR as well as in the mean infrared(SWIR) (e.g., Bartholdy and Folving 1986; Yachtset al. 1993; Thomson et al. 1998; Ryu et al. 2004;Van der Wal and Herman 2007), to multispectralairborne images (Rainey et al. 2003), and evento hyperspectral airborne images (Smith et al.2004; Deronde and Houthuys 2006) combinedwith particle size in situ measurements. However,the carried out experiences by the authors previ-ously indicated, as far as distribution and nature ofintertidal sediments is concerned, were subject tothe available material at that moment, which wascharacterized to have a poor temporal, spatial,and spectral resolution.

Traditionally, the way to characterize thezones, based on the sediment characteristics,was achieved by the use of supervised clas-sification techniques relying upon different algo-rithms (Foody et al. 1995; Thomson et al. 1998),as well as unsupervised classifications methodsand the classic analysis of principal components(PCA) (Doerffer and Murphy 1989; Adams et al.1995). Any at rate, all the previously describedqualitative methods generate information of theregion of study, and they even study the separa-bility between classes but they do not arrive at thedetermination of the quantitative information ofthe element.

On the other hand, more advanced techniquesas the ones based on spectral mixtures allow thedetection of subtle differences within the samespectral class. However, the information providedby these methods continues to be qualitative, andits application is usually focused on the detectionof temporal changes or on the pursuit of certainphenomenon. The regression models are anotherof the habitually used techniques for the esti-


mation of the sediment properties. This type ofmethod’s precise in situ information is later ana-lyzed in the laboratory (Pirie et al. 2005; ViscarraRossel et al. 2006) and in the field (Hakvoort et al.1998; Sullivan et al. 2005). The results obtainedare good for multispectral grain size estimation(Yachts et al. 1993) or hyperspectral information(Yachts et al. 1993; Smith et al. 2004; Çelebi et al.2007). However, they present the disadvantage ofnot using all the hyperspectral information avail-able, which is why it is not known if its contribu-tion is or is not valid. One of the last advances inthe quantitative determination of large particlesof intertidal sediment comes from Rainey et al.(2003), where a technique generates precise inter-tidal sediment maps from ATM airborne sensorimages, presenting R2 values of 0.84.

It is necessary to emphasize that the joint ap-plication of statistical techniques of robust estima-tion (King 1978; Campbell 1980; Rousseeuw andLeroy 1987; Rousseeuw and van Zomeren 1990;Campbell 2002; Huber 2003; Verboven and Hu-bert 2004) with information coming from airbornesensors (CASI, Daedalus, AHS, etc.) is a field thathas not been broached, and that can contributewith very good results for the detection of thepresence of anomalous data.

Most of the used statistical techniques in thedigital treatment of images, in order to be applied,depend on the model distribution supposed for

the variation in observation. However, this sup-posed model is no more than an idealized ap-proach of reality; therefore, even if the truth isnear the idealized one, the results obtained withboth models can be very dissimilar (García Pérez2006). The need for and effectiveness of robustmethods has been described in many articles andbooks.

The material used for applying robust statisticwas a MATLAB Library for Robust Analysis,developed by the research groups on robust sta-tistics at the Katholieke Universiteit Leuven andthe University of Antwerp. It contains implemen-tations of several robust procedures that are resis-tant to outliers in the data. Some other algorithmswere implemented by the authors in R and IDLlanguage.

Field measurements: ASD-FR

First of all, we have to know the spectral responseof some representative soil samples (Fig. 2). Si-multaneous with the CASI airborne sensor flight,a field radiometric campaign made it possible toobtain a database with different soil types. Theradiometric measures were made with an ana-lytical spectral devices–full resolution (ASD-FR)spectroradiometer, equipped with optical fiber ca-bles. The ASD-FR was provided by the Centre ofStudies and Research of Public Works (CEDEX),

Fig. 2 In situ reflectancemeasured by the ASD-FRspectroradiometer,covering the same rangeof wavelengths as theCASI sensor CASI in (a)little dense grass (T1),dense grass (T2), very drysand (T3), very densegrass (T4), asphalt (T5),and bare ground (T6)


belonging to the Spanish Ministry of Public Worksand Economy.

The main variable, remote sensing reflectance,was obtained following NASA’s protocols(Fargion and Mueller 2000) and applying thecorrections for specular reflections (Mobley1999).

A sampling protocol was designed for the ASD-FR field measurements, initially marking on theMTN-25, of the National Geographic Institute,the locations of the points (Fig. 1). Such points arelocated in the map; it is necessary to locate thembased on their typology (little dense grass, densegrass, very dry sand, asphalt, and naked ground).However, in land, the measurement protocol issimple and it is limited to measure the descendentsolar irradiance E0 (spectralon) and the answer ofthe type of ground (target).

To perform the radiometric measurement inwater, the observation zenith angle is very impor-tant, since the observed radiance is the sum ofthe upwelling radiance from water plus a specularcomponent. In order to minimize this componentwe made the measurements with a viewing zenithangle of 45◦.

The data collected by the spectroradiometerin the field sampling (Fig. 2) constituted the in-put parameter in the 6S atmospheric correctionmodel applied to these images. The atmosphericcorrection was developed very successfully withthe essential cooperation of the Catalonian Car-tographic Institute. The election of this modelresided on the fact that it is not based on anysatellite or specific sensor, and because it was de-signed to be applied in short wavelengths and theatmospheric scattering is better corrected com-pared to other models (Gordon and Clark 1981;Vermote et al. 1996, 1997; Law et al. 1998).

Figure 2 has been adjusted to the same spectralrange that is capable of catching CASI-2 (405–958 nm). The ASD-FR spectroradiometer pro-vided information between 350 and 2,500 nm.

Laboratory measurements: ASD-FR

In order to complete the information acquired bythe CASI-2 airborne sensor and to determine theinfluence that certain sediment-related variables

have over reflectance, a set of laboratory experi-ments were developed; in these experiments, per-formed under controlled conditions, the variationof reflectance with the moisture were measuredfor several sizes of sediment particle (sandy loamof the Pedreña-Cubas Estuary, silty loam of theBoo Estuary, clay loam of the San Salvador Estu-ary, and transition of the Solia Estuary) of the Bayof Santander (Fig. 1).

Similar to field measurements, the laboratorysampling was executed following a protocol pre-viously established in a cabinet so that eachmeasurement captured by the ASD-FR spectro-radiometer was configured to be the average offour series of 25 points each. The battery of theinstrument had a 2-h work autonomy, and for thefocus, as well as the laser, the field of view wasof 45◦.

Once collected, samples were vacuum packedin order to preserve the conditions of observationand moisture of the sediment until their analysisin the laboratory and a gray panel of visible andinfrared 25%. In the visible and the VNIR, theradiant flux is relatively high, which allows toobtain a spectral resolution from 3 to 5 nm anda sampling interval smaller than 2 nm. In theseconditions the signal-to-noise ratio of the multi-sensors is small, this one being the most suitablesystem of measurement. In the rest of the regions,there is an increase of the signal-to-noise ratio,which is the reason why the use of sweeping detec-tors is recommended, as they offer a very superiorsignal-to-noise ratio.

In Fig. 3, there appears the reflectance mea-sured in the laboratory with the variable moisturepercentage.

Chemical sampling and analyses

In order to carry out a chemical sampling andanalyses (grain size, moisture, pH, etc.), a sam-pling strategy was applied in the Santander Bay.This was divided in zones depending on natural,geographic, and anthropogenic variables. Ninecontrol points were defined from the west to theeast side of the Bay and four zones correspondingto the rivers: the Boo Estuary, the Solia Estu-ary, the San Salvador Estuary, and the CubasEstuary, obtaining 16 sample stations covering


Fig. 3 Laboratoryreflectance measuredby the ASD-FRspectroradiometer in theBoo Estuary, covering allthe range of wavelengthswith different percentagesof moisture: 0% (S1),2.3% (S2), 11.2% (S3),28% (S4), and 40% (S5)

an estuary area of 80 km2. The zones (Fig. 1)were marked on the basis of points of spillage ofpollutants.

Every station, positioned using a global posi-tion system of double frequency, was sampled ina simultaneous way to the accomplishment of the

flight in order to obtain surface sediments (0 to1 cm in depth). The surface sediments were col-lected by a metal paddle, were homogenized, andthe samples were immediately transferred to cleanglass jars capped with aluminum foil, and kept inthe dark at 4◦C until analysis.

Fig. 4 Grain sizeclassification of thesurface sediment samplesused in Santander Bay


Sediment moisture was determined throughthe standardized UNE-EN 12880 method (ITUNITES 2001), drying the sample in a stove untila constant weight at 110◦C. The determinationof the particle size (dp) was made by humid sift,agreeing with the ISO/DIS 11277 normative (ISO1994). Sieves used in the study had 203 mm and a2,000 μm diameter, and 63 μm of mesh light.

The sediments were classified as gravels (dp >

2,000 μm), sands (63 μm < dp < 2,000 μm),and silt and clay (dp < 63 μm). The gatheredsuspension of particles smaller than 60 μm waslater used for the analysis of particles smaller than2 μm (clays), using Mastersizer X equipment,from Malvern Instruments (Worchestershire,UK). The sediment grain size classification,American texture triangle, of the studied sedi-ment samples is shown in Fig. 4.

Results and discussion

In order to generalize the obtained results in acertain area of study and extrapolate them toother regions of interest, it is necessary to knowthe parameters that influence in the captured sig-nal, as well as their influence in the obtainedanswer. Although there are previous studies thatrelate the moisture of determined ground with itsreflectance (Angstrom 1925; Bowers and Hanks1965; Hoffer and Johannsen 1969; Weidong et al.

2002) for different spectral regions and differentground types, the results are very dependent ofthe place location, becoming a necessity in thedetermination of this relationship for each case ofstudy.

The relationship between reflectance and mois-ture has been studied in three types of sediments(the Pedreña Estuary, the Boo Estuary, and theSan Salvador Estuary), characterized by the pres-ence of the highest values of sand, silt, and clay,respectively. Sediment samples have also beenanalyzed from the Solia Estuary, which present in-termediate values of three sizes. All the sedimentsunder study were dried in a stove at differentdegrees, obtaining moisture between 0% and 46%based on the sediment type.

The adjustment of the maximum reflectancedata captured by spectroradiometer in the lab-oratory (Rmax), with the moisture (Moisture_percentage), for different grain sizes is exponen-tial (Fig. 5). This equation shows

0.066 + 0.205 · e10.770(Moisture_percentage) = Rmax

with an R2 value of 0.94.In the same way, it is possible to obtain a 3D

adjustment function that relates the percentage ofmoisture present in the sediment (H), the size ofthe grain (Y) expressed in percentage (percentageof sand, silt, and clay, respectively), and the Rmax

observed by the ASD-FR in the region between

Fig. 5 Exponentialadjustment obtainedbetween the percentageof moisture present inthe sediment and themaximum reflectivityobserved by thespectroradiometer fordifferent types of grain


408 and 950 nm (CASI region). The adjustmentequation is

Rmax = a + bY + ec+d H

a = 0.066, b = 0.00005, c = 0.202, d = 11.302for the percentage of sanda = 0.059, b = 0.00034, c = 0.200, d = 9.816for the percentage of silta = 0.089, b = 0.00063, c = 0.188, d = 8.963for the percentage of clay

with R2 values of 0.92 in the case of sand, an R2 of0.95 for silt, and an R2of 0.96 for clay.

Analysis of data of the remote sensor by meansof classic methods of estimation

With the aim to compare the results obtainedon applied robust statistics with those obtainedby means of classic statistics a series of classicmethods of data analysis was applied (analysis ofsimple bands, spectral quotients, PCA, and trans-formation minimum noise fraction).

The analysis of simple bands is the oldestmethod of all those that were analyzed in thiswork and it is based on the existing relation be-tween variables in each one of the bands of theCASI airborne sensor. On the other hand, thespectral quotient is a technique widely used inthe remote sensing field, as it allows eliminatingdistortions present in the data, as they can be theones derived from the angle of observation andthe topographic effects.

The PCA is the first step of the data analysis,followed by an analysis of discriminates and con-glomerates, or by other multivariable techniques.The determination of the main components isnothing more than a particular case of projectionpursuit. In this technique the measure of interestresides in the total variance, in the way of thatthe projected data contain the biggest possibleproportion of variance. In this case, five compo-nents were considered because they explain 99%of the covariance of the data. These componentswere correlated to the particle sizes of the calibra-tion samples, obtaining correlations of 93.71% forthe percentage of sand present in the sediment,

95.26% for silt, and 72.68% for clay with respectto the PCA1 component.

Finally, the methods of transformation basedon signal-to-noise rotation (MNF) are used todetermine the inherent dimension of the images,to segregate the noise in the data, and to reducethe computational cost of the following processes.The MNF transformation is composed of twoconsecutive PCAs. Within the scope of spectralprocessing, the inherent dimension of the data isdetermined by the examination of the final eigen-values from the associate images.

Considering the experiments developed byother authors and with the objective of establish-ing a defined criteria, it is considered that thegenerated equations are very good if the R2 valueis greater than 0.90; good if it is between 0.90and 0.75; acceptable if it has values between 0.75and 0.60; and also being acceptable even if the R2

values are between 0.60 and 0.45, whenever it ispossible to argue the causes that have influencedthese results. For the remaining cases, it is consid-ered that it is a bad adjustment. In our particularcase, both the R2 and the percentage values werecalculated.

Analysis of data of the remote sensor by meansof robust estimation methods

The objective pursued by the robust estimationmethods is the search of techniques that arenot influenced by the presence of anomalousdata. Traditionally, the conventional covariancemethod most used has been the one of princi-pal components based on an empirical matrix ofvery sensitive to the presence of anomalous data(outliers).

In the scope of the robust statistic, firstMaronna and then Campbell (Campbell 1996a, b),they proposed the use of compatible equivalentM-estimators of dispersion. However, these esti-mators have the peculiarity of where they do notresist the presence of many anomalous data. Morerecently, estimators like the minimum covari-ance matrix determinant (MCD) have been used(Shadish et al. 2002), as well as the S-estimators(Davies 1987; Rousseeuw and Leroy 1987).

The objective of the robust PCA is to deter-mine a linear combination of original variables


Fig. 6 Adjustment obtained for a percentage of sand and first MVE robust component and b percentage of the silt and firstMVE robust component

that will contain the greater amount of possibleinformation in the case of outliers, and second, todetermine which are the anomalous data and ofwhat type. In this article, two robust methods wereapplied based on autovectors from the robustdispersion matrix, allowing the estimation of theMCD and the S-estimators based on the ellipsoidof minimum volume (MVE).

The robust principal component analysis basedon the ellipsoid of MVE starts from suppositionof normality, the center of the ellipsoid being thepopulation average where the sample is extracted.On the contrary, the estimator of location MCDintends to reduce the determinant of the sampledmatrix of covariances classic S, and it is character-

ized for being a method that has greater efficiencythan the MVE despite its smaller breakthroughpoint. These algorithms have been implementedusing Matlab 6.5, R.2.0.0, and IDL 6.0.1 platforms.

The adjustments made for the sand and silt per-centages (chemical sampling and analyses for cali-bration) with the first MVE robust component arelinear (Fig. 6). The correlations were of 97.45%for the percentage of sand present in the sediment,96.29% for silt, and 79.23% for clay with respect tothe MCD1 component and 93.69%, 94.27%, and71,28% with the MVE1 component.

It is necessary to highlight that within the areaof study the percentage of clay is small, not ex-ceeding 15%, joined to the fact that there was a

Fig. 7 Adjustment obtained for a percentage of sand and first robust component MCD and b percentage of the silt and firstMCD robust component


verification of the adjustment of equations usingfour of the result validation samples, for both theclassic and robust cases.

The obtained values of adjustment are of 0.89,0.91, and 0.65 with the first component of PCA;0.88, 0.91, and 0.54 for sand, silt, and clay with thefirst MVE component; and 0.93, 0.94, and 0.77 forthe MCD first component.

The application of the MCD method to thecorrelation of CASI airborne sensor values andthe in situ values of size of particle allows a prelim-inary estimation of the particle size distribution inthe intertidal sediments of the Bay of Santander(Fig. 7). The obtained mapping of the five classesof sizes of the area under study constitutes a usefultool for the decision making in the accomplish-ment of field samplings, their location, intensity,and regularity; the degree of interest will be thesubject of the sampling objective (determination

of sediment quality, study of dredging alterna-tives, and analysis of management alternatives forfinal use) and of the characterization necessities ofthe extracted samples.

Therefore, although the degree of mixture ofthe obtained size classes is high, the northeastzone of the Bay of Santander, formed by the sandybar of El Puntal and the mouth of the Cubasriver, remains classified as Sandy and Franco-Sandy (Fig. 8); this zone is strongly influencedby the entering sea currents with high sand con-tribution, and that regularly makes the object ofdredging for securing the navigability toward theport. The southern zone of the Bay of Santander,with the mouths of the rivers San Salvador andSólia, also share this category of particle sizes.In contrast, the intermediate zones between theseriver mouths in the east and western areas arecharacterized for being muddy, although with an

Fig. 8 Result of applyingthe robust principalcomponent analysis basedon the MCD estimator

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increasing sandy contribution toward the north,coincident with the seawater entrance by the leftmargin of the Bay.

It is necessary to take into account thatthe results obtained in applying robust statis-tics methods improve the classical results even ifatmospheric, geometric, and radiometric correc-tions were applied to the data prior to principalcomponent transformations or if the work wasplanned with great care.

Summary

From the results obtained by the robust esti-mation methods, it is possible to conclude thatthe adjustments made with MCD first componentare very good, improving any one of the resultsobtained by conventional methodologies. On theother hand, the obtained results using the MVEare closer to those ones obtained by a classicanalysis of principal components considering bothcases as good.

In all the studied cases and for each one ofthe applied methods, each one of the percentageof sand, silt, and clay were considered separately,as it is possible to conclude that this is the mostoptimal way to execute the work.

Robust statistical methods can improve the re-sults obtained using classical PCA.

Acknowledgements This research was funded by theCartography, Geodesy, and Photogrammetry Area. Wewould like to thank to Prof. Dr. Rafael Ferrer and Prof. Dr.Raul Medina for their generous supports during the labo-ratory analysis, and the CEDEX, Ministry of the SpanishGovernment, for support during the field campaign.

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Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

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