Estimation of the Spatially Distributed Surface Energy Budget for AgriSAR 2006, Part I: Remote...

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 4, NO. 2, JUNE 2011 465

Estimation of the Spatially Distributed SurfaceEnergy Budget for AgriSAR 2006, Part I: Remote

Sensing Model IntercomparisonWim J. Timmermans, Juan-Carlos Jimnez-Muoz, Victoria Hidalgo, Katja Richter, Jose Antonio Sobrino,

Guido dUrso, Giuseppe Satalino, Francesco Mattia, Senior Member, IEEE, Els De Lathauwer, andValentijn R. N. Pauwels

AbstractA number of energy balance models of variablecomplexity that use remotely sensed boundary conditions forproducing spatially distributed maps of surface fluxes have beenproposed. Validation typically involves comparing model outputto flux tower observations at a handful of sites, and hence thereis no way of evaluating the reliability of model output for theremaining pixels comprising a scene. To assess the uncertaintyin flux estimation over a remote sensing scene requires one toconduct pixel-by-pixel comparisons of the output.

The objective of this paper is to assess whether the simplifi-cations made in a simple model lead to erroneous predictionsor deviations from a more complex model and under whichcircumstances these deviations most likely occur. Two models,the S-SEBI and TSEB algorithms, which have potential for op-erationally monitoring ET with satellite data are described anda spatial inter-comparison is made. Comparisons of the spatiallydistributed flux maps from the two models are made using re-motely sensed imagery collected over an agricultural test site inNorthern Germany. With respect to model output for radiativeand conductive fluxes no major differences are noted. Results forturbulent flux exchange demonstrate that under relatively dryconditions and over tall crops model output differs significantly.The overall conclusion is that under unstressed conditions andover homogeneous landcover a simple index model is adequate fordetermining the spatially distributed energy budget.

Index TermsAtmospheric modeling, hydrology, meteorology,soil, vegetation.

Manuscript received June 23, 2009; revised March 10, 2010 and November25, 2010; accepted November 30, 2010. Date of publication January 06, 2011;date of current version May 20, 2011. This work was supported by ESA Grants19558/06/NL/HE, 19569/06/NL/HE, and 19974/06/I-LG.

W. J. Timmermans is with the Faculty of Geo-information Science and EarthObservation, Department of Water Resources, University of Twente, 7500 AAEnschede, The Netherlands (e-mail: [email protected]).

J. C. Jimnez-Muoz, V. Hidalgo, and J. A. Sobrino are with the GlobalChange Unit, Image Processing Laboratory, University of Valencia, E-46071Valencia, Spain.

K. Richter is with the Faculty of Geosciences, Department of Geography,Ludwig-Maximilians-University Munich, D-80333 Munich, Germany.

G. dUrso is with DIAAT, Faculty of Agraria, University of Naples FedericoII, 80055 Portici (Naples), Italy.

G. Satalino and F. Mattia are with the Consiglio Nazionale delle Ricerche,Istituto di Studi sui Sistemi Intelligenti per lAutomazione, 70126 Bari, Italy.

E. De Lathauwer and V. R. N. Pauwels are with the Laboratory of Hydrologyand Water Management, Ghent University, B-9000 Ghent, Belgium.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2010.2098019

I. INTRODUCTION

Q UANTIFICATION of the spatial variability in hydrolog-ical and land surface states is important to water resourcemanagement, particularly in agricultural regions. During

the last few decades, the strong interconnections and feedbacksbetween hydrological variables, like soil moisture and evapo-transpiration, with regional hydrometeorology has broadenedthe interest in accurately determining energy partitioning of sur-face fluxes, and has resulted in research into the use of satelliteremote sensing [1]. Routine determination of moisture exchangebetween land and atmosphere at a spatial resolution appropriateto the underlying surface heterogeneity is only possible by acombination of remote sensing based observations and hydro-logic models. In the last decade researchers have been involvedin integrating land surface simulation, observation, and anal-ysis methods to accurately determine land surface energy andmoisture status. Examples of such studies include the GlobalLand Data Assimilation System (GLDAS) [2], the GEWEXContinental scale International Project (GCIP) [3] and more re-cently ESAs Global Monitoring for Environment and Security(GMES) initiative.

Typically, physically based hydrological process models arecalibrated, updated (data assimilation) and validated by highresolution remotely sensed observations, which require a cer-tain degree of modeling, so-called observational modeling, aswell. Since previous estimates of land-atmosphere interactionhave been limited by both a lack of observational data as well asby model dependence on computational estimates [4], multi-ob-servational and multi-model approaches have become a populartool [4][8].

Model results do not always have an unambiguous meaning;their output is a strongly model-specific quantity, essentially anindex of a particular state or exchange rate, with a dynamicrange defined by the specific process formulations of the givenmodel [9]. Large differences are seen in products generated bydifferent models, even when the models are driven with pre-cisely the same forcing [10], [11]. In order to use model resultswith confidence, there is a need to evaluate them at scales, or res-olutions, and at the domains, or area coverage, at which they areimplemented. Since ground observations are generally sparse,remote sensing observations are considered to be the only sourceof information that could be used for model evaluation [3], [12].

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466 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 4, NO. 2, JUNE 2011

Validation of these remote observations is usually done versus ahandful of ground observations which does not necessarily guar-antee correct results over the entire domain. True informationcontent of the observation model results does not necessarily liein their absolute magnitudes but more so in their variability, aslong as the nature of the modeled input is well understood [9].When this last criterion is met, naturally simpler models are pre-ferred over more complex ones with respect to operational use.

Although more sophisticated models have a greater physicalrealism, and as such should reflect the surface energy distri-bution with greater accuracy than the simpler approaches, theygenerally also require more ancillary data. Moreover, in spite ofthe simplification of reality, many authors have found, that thesimpler approaches may describe the overall surface energy bal-ance satisfactorily [13], [14]. A simple but correctly calibratedmodel might well perform better than an ill-parameterized so-phisticated model.

Therefore in the current contribution we compare two remotesensing-based observational models that have demonstrated op-erational use in flux determination, but show a rather differentlevel of complexity. The objective is to assess whether the sim-plifications made in a simple model lead to erroneous predic-tions or deviations from a more complex model and under whichcircumstances these deviations most likely occur, where the em-phasis is on the variability over the domain. Airborne and in-situdatasets from the AgriSAR 2006 campaign in Germany wereused for this purpose. In a second paper [15] it is then assessedas to which extent process model results may depend on obser-vational model results.

II. SURFACE ENERGY BUDGET MODELING

A. Model Characteristics

The model parameterization of the interaction between a landsurface and the atmosphere is known as a soil-vegetation-atmos-phere transfer scheme (SVAT). Generally it is based on the bal-ance of available energy against turbulent fluxes:

(1)

where is the net radiation, the soil heat flux, the sen-sible heat flux, and the latent heat flux, all in .Numerous SVAT schemes with varying complexity have beendeveloped in recent years for use with remote sensing data. Foran excellent overview covering the entire spectrum of remotesensing based energy models see [16]. The complexity neededfor accurate model performance is generally related to the un-derlying surface heterogeneity. For homogeneous land covera relatively simple model may suffice, whereas for more het-erogeneous land cover a more complex model, taking into ac-count the sub-pixel variability, is needed. Although in a nat-ural environment heterogeneity is present at all scales, the se-lection of a proper modeling approach over a certain domainis also resolution-driven. At a relatively high spatial resolution,as compared to the length-scale of surface heterogeneity, theneed for a more sophisticated model may be less critical. Giventhe large homogeneous fields of the area under study combined

with the high spatial resolution of the remotely sensed data, theAgriSAR campaign formed an excellent opportunity to test thishypothesis.

To assess whether simplifications made in a simple modellead to erroneous predictions or deviations from a more com-plex model, we selected two well-established models that haveoriginally been developed for using data primarily from remotesensing platforms. They are the Simplified-Surface Energy Bal-ance Index (S-SEBI) method developed by [17] and the Two-Source Energy Balance (TSEB) approach originally proposedby [18]. Using the model classification of [16] these approachesare a representative of the relatively simple spatial variabilityor index-models and the more sophisticated physically baseddual source models respectively [16]. Validation of both S-SEBI[19][21] and TSEB [22], [23] has been carried out under arange of environmental conditions. Although both model pa-rameterizations are based on (1) and both use remotely sensedsurface temperature as the primary boundary condition for de-termining the individual fluxes, the procedure to arrive at thesefluxes is rather different. The S-SEBI scheme makes use of thespatial variability in surface temperature in conjunction with re-flection and a vegetation index. As such, it does not need infor-mation on local atmospheric variables nor on landcover or aero-dynamic land surface properties. The TSEB algorithm on theother hand considers the land surface as an electrical analoguewhere the rate of exchange of a quantity between two pointsis driven by a difference in potential (temperature or concen-tration) and controlled by a number of resistances that dependon the local atmospheric environment and internal properties ofthe land surface and vegetation [16]. The main characteristic ofTSEB is that it discriminates between a soil and vegetation com-ponent, as such taking into account the sub-pixel variability.

An overview of the required inputs and characteristics of bothmodels is provided in Table I. Where applicable also the valuesof the required input as used in the current study are listed.

Since the S-SEBI and TSEB models have been described ex-tensively elsewhere, [17], [19][21] and [18], [22], [23] respec-tively, only a short summary on the basic characteristics of thealgorithms and the main assumptions and simplifications madetherein is provided in the following section.

B. S-SEBI

The basis of the S-SEBI algorithm is the simultaneous esti-mation of and fluxes using the conceptof the evaporative fraction:

(2)

S-SEBI estimates using two extreme temperatures,and both in [K], according to

(3)

where is land surface temperature and andare obtained from the image itself. This is done by

plotting surface temperature, , versus surface albedo,

TIMMERMANS et al.: ESTIMATION OF THE SPATIALLY DISTRIBUTED SURFACE ENERGY BUDGET FOR AGRISAR 2006, PART I 467

TABLE IOVERVIEW OF MODEL INPUT REQUIREMENTS AND THEIR VALUES USED (WHERE APPLICABLE)

, in such a way that and both in [K], can belinearly related to the surface albedo:

(4)(5)

where the regression variables a and b need to be obtainedby the user from the plot. They can be seen as theso-called wet and dry edges of the typically triangular shapeof the plot. More information regarding the graphical procedurecan be found in [17]. Since the main characteristic of S-SEBI isthe way and are determined, the othercomponents, and , in principle can beestimated in different ways depending on the available data.

Required remote sensing input consists of spatial informa-tion on Normalized Difference Vegetation Index (NDVI), sur-face albedo, surface emissivity and land surface temperaturein conjunction with limited ancillary in-situ data, see Table I.How these parameters are derived from observed at-surface re-flectance and emission is described in section III.C. In what fol-

lows here a description is provided how the heat fluxes havebeen computed using these parameters.

Net radiation, , is defined as the radiation bal-ance between all incoming and outgoing shortwave (SW) andlongwave (LW) radiation:

(6)where shortwave incoming radiation, , was ex-tracted from in-situ measurements and the longwave radiationparameterizations are based on Stefan-Boltzmans law:

(7)where R represents radiation and subscript represents theemitting source, being either air ( ) or the land surface (0).The Stefan-Boltzmann constant isrepresented by and is the emissivity.

The last term on the right hand side of (6) represents thereflected part of the incoming longwave radiation. The air, oratmospheric, emissivity, , was estimated from an empiricalrelation, according to [24], whereas the surface emissivity, ,was determined using the TES algorithm, originally developed


Fig. 1. Two-dimensional scatterplots of the reflectance and temperature for the two days of AHS overpass with the derived regressions for hot and wet limits.

by [25]. How this algorithm is applied here to AHS data is de-scribed in more detail in Section III-B.

Soil heat flux generally is obtained as a fraction of. In S-SEBI an empirical relation following

[26] is used, where the ratio is a function of ,and . Parameters and herein

describe the time-dependant or dynamic component in theversus relation and the is

used to approximate the radiation extinction by the canopy.The evaporative fraction principle is then used to derive the

latent and sensible heat fluxes. Combining (3)(5), the evapora-tive fraction can be obtained as

(8)

As stated before, regression variables , , andare obtained from image data by plotting LST [K] versus albedo

, Fig. 1. In Table I the values obtained from the linear regres-sion for the two flights considered in this contribution are shown.

Once is calculated, and followfrom combining (2) and (6) with the soil heat flux:

(9)(10)

If the atmospheric conditions over the modelling domain canbe considered constant and the area reflects sufficient variabilityin hydrological conditions the turbulent fluxes can be calculatedwithout any further information than the remote sensing imageitself [17]. Furthermore the model is characterized by a cer-tain degree of subjectivity involved in determining the so-calledwarm edge. However, a consistency test using multi-temporalimagery within a 10 minute time interval showed errors lowerthan 15%, [21].

C. TSEBThe main characteristic of Two-Source Energy Balance

scheme is that it discriminates between a soil and vegetationcomponent, aiming at a more physical description of heteroge-neous surfaces when dealing with radiative and aerodynamic

properties. Required remote sensing input consists of spatial in-formation on surface temperature as well as vegetation density,being and . The version implemented herebasically follows what is described as the series resistancenetwork in Appendix A of [18]. In the current version aphysically based algorithm is implemented for estimating thenet radiation, which is described in [27]. As such, the modelimplemented is described in detail in [18] and [27]; reason toonly sketch its main characteristics here and highlight thoseparts that are different from the S-SEBI algorithm.

First and most important of all, fractional vegetation cover,fCover, is used to estimate canopy and soil temperatures (and respectively) from observed radiometric surface temper-ature, , with a simple non-linear mixing model, describedby

(11)

where is the power in the Stefan-Boltzmann equation thatreasonably approximates the appropriate integral of the Planckblackbody emission function for the wavelength of the sensor[28], taken equal to 4.

The algorithm used for estimating the net radiation diver-gence requires an incident solar radiation observation in com-bination with the aforementioned component temperatures andformulations for the transmission of direct and diffuse short-wave radiation and for the transmission of longwave radiationthrough the canopy [29]. The canopy component of net radia-tion, , is then given by

(12)

and the soil net radiation component, , by

(13)

where represents transmissivity through the canopy andsubscripts SW and LW stand again for shortwave and longwave


respectively. The longwave radiation components are again de-termined from (7). Subscripts a, S and C represent the atmos-phere (or air), soil and canopy components, whereas is thereflection, or shortwave albedo. Since the reflection and absorp-tion of radiation in the visible and near-infrared wavelengths israther different for vegetation and soils, the visible and near-in-frared albedos of the soil and canopy were evaluated differ-ently before combining to give an overall shortwave albedo. Theequations for estimating the transmission and reflection of di-rect and diffuse shortwave radiation are provided in [29] andvary with landcover, soil type and LAI. Longwave transmis-sivity finally is approximated by a single exponential functiondepending on an extinction coefficient and LAI. Summation ofthe soil and canopy components yields the total net radiation,

.

The soil heat flux, , is determined as a time-de-pendant ratio, , of the soil component of the net radiation,

following [27]. Basically this ratio is taken equalto 0.3 when the overpass is within 3 hours from solar noon,linearly decreasing to zero when more then 5 hours from solarnoon.

With respect to the turbulent fluxes, a first estimate of the la-tent heat flux from the canopy, , is obtained byapplying the Priestley and Taylor approach on the canopy com-ponent of the net radiation, , following

(14)

where is the well-known Priestley-Taylor coefficient ,is the slope of the saturation vapour pressure curve

and is the psychromatic constant . This first esti-mate works reasonably well under unstressed vegetation condi-tions [18]. Evaluation of the canopy energy budget then yieldsthe canopy sensible heat flux, , by substracting

from . By using a linearized formof (11), following the procedure outlined in the Appendix Aof [18], the within-canopy air temperature, , is derivedalong with the canopy temperature, . Substitution in (11)yields , which provides the possibility of obtaining thesoil sensible heat flux, , from

(15)

where is the volumetric heat capacity of air ,r represents resistance , and subscript S stands for soilcomponent. The soil latent heat flux, , is then de-termined as a rest-term by evaluating the soil energy budget:

(16)

In case is negative, then the soil is likely to bedry and is set to zero. Under these circumstances,

is derived from the soil energy budget, (16), and an adjustedis obtained. Equation (11) provides a new estimate forwhich is then used to calculate an updated ,

following

(17)

where is the total boundary layer resistance of thecomplete canopy of leaves [18]. Evaluation of the canopy en-ergy budget yields a new estimate of by sub-stracting the updated from .

As soon as equilibrium is reached the soil and canopy com-ponents of both and are summed toobtain total sensible and latent heat fluxes. Summarizing thisgives the following parameterizations for the turbulent fluxes:

(18)

(19)

Details of the parameterization of the resistances can be foundin [18]. Required input for this parameterization are aerody-namic properties such as canopy height, displacement height,aerodynamic roughness, leaf width, as well as limited microm-eteorological observations, which are listed in Table I.

Various resistance schemes and adaptations have been pro-posed to represent the exchange of energy and mass in the soil-vegetation-atmosphere continuum [30][32]. Previous studieshave shown that the TSEB model output for turbulent fluxes isnot very sensitive to input of vegetation properties and aerody-namic characteristics [11], [33]. However, some uncertainty re-mains [18], since, apart from a relatively large input demand, acertain degree of empiricism [30], is included in these resistanceschemes.

Some subjectivity is also included in the selection of vege-tation limits of bare soil and full cover in the TSEB approach,quantified in [11]. However, in the current case this is facilitatedby the very high resolution of the imagery used here, which as-sists in a more easy identification.

Another drawback of the TSEM approach is that the timingand representativeness of the meteorological variables, such aswindspeed and air temperature, are critical. However, in the cur-rent cases the locations of the stations was chosen such thatthey represent average areal meteorological conditions, and therecording interval (10 minutes) sufficiently small to avoid thisproblem.

III. EXPERIMENTAL SETUPWithin the GMES framework a series of Sentinel SAR and

optical satellites is developed for which the AgriSAR 2006 cam-paign was established to address important specific program-matic needs. The campaign, which lasted from April throughJuly 2006, is conducted on a non-irrigated agricultural test sitein Mecklenburg-Vorpommern in North-East Germany, approx-imately 150 km North of Berlin, and is described in detail in[34].

The test site consists of a group of farms within a farmingassociation covering approximately 250 km2. Field sizes arevery large, averaging between 2 and 2.5 km , mainly containingcrops such as winter wheat, winter barley, winter rape, corn,and sugar beet. The area is extremely flat, at an altitude of ap-proximately 50 m.a.s.l.. During the campaign, three intensive


TABLE IILANDUSE, VEGETATION HEIGHT AND AERODYNAMIC PROPERTIES FOR JUNE.

BETWEEN BRACKETS VALUES FOR JULY, IF DIFFERENT FROM JUNE

observation periods (end of April, early June, and early July)took place in which several airborne radar and optical (not inApril) data acquisitions were performed. More details of the op-tical remote sensing observations and how they were used inthis study to extract required model parameters are providedin Section III-B. Simultaneous to these observations, a largenumber of ground variables were measured, ranging from landcover and soil parameters, soil moisture contents and surfaceroughness profiles to ground-based radiometric and thermal in-frared measurements. A Bowen Ratio Energy Balance (BREB)station and a Large Aperture Scintillometer (LAS) station wereinstalled in a winter wheat field during the campaign, wherethe latter was only installed during the last intensive observa-tion period. A more detailed description of these is providedin Section III-A. Finally, meteorologic data were continuouslymonitored by standard weather stations at Goermin (53.98 N,13.25 E) and Buchholz (53.94 N, 13.16 E).

A. In-Situ Observations

The ground-based data used here consisted of vegetationcharacteristics, meteorological observations as well as radiationand turbulent flux exchanges.

Field observations of canopy height were conducted on aregular basis throughout the cropping season for several fields[34]. These were used in conjunction with a landuse map, seeFig. 1, and classical assumptions [35] on aerodynamic proper-ties to obtain roughness length for momentum transport, ,and zero-plane displacement height, , all in [m]. Nominalvalues were taken for leaf-width, see Table II.

Along with the canopy height observations also estimationsof LAI and fractional vegetation cover, fCover, were made forboth overpasses. For fCover observations were carried out overall major crops, consisting of barley, corn, wheat, sugarbeet and

rape, using hemispherical photographs, whereas LAI measure-ments were made using a LICOR LAI 2000 over wheat, cornand sugarbeet [36].

Thermal radiation measurements were carried out over rape,barley, corn, wheat and grass plots. Measurements were carriedout using different broadband and multi-band field radiometers,which were fixed on masts measuring at nadir and at a 53viewing angle, as well as hand-held for measuring transects.More specific information on these measurements is providedin [37].

Meteorological observations (air temperature, relative hu-midity, air pressure and windspeed) that were needed as aninput to the surface energy balance models, were all taken at2.5 m height from the BREB station installed in a wheat field inthe center of the area [6]. Incoming shortwave radiation, ,was obtained from the Goermin weather station, which waslocated a few hundred meters from the BREB station, see alsoFig. 2. An overview of necessary ancillary meteorological inputdata for both remote sensing-based models and their valuesduring the AHS overpasses is provided in Table I.

Turbulent flux observations were obtained from the afore-mentioned BREB station as well as from the Large ApertureScintillometer (LAS) station, where also net radiation and soilheat fluxes were measured. Although the BREB and LAS sta-tions were located not very far apart, there was considerablevariation in fCover over the sites, mainly reflected in a relativelyhigh variation in soil heat flux. In Fig. 2 the daily evolution of thesurface energy fluxes measured at the BREB station are shownfor the two days of AHS overpass. Since the BREB method re-sults in unrealistic estimates of the turbulent fluxes when theBowen ratio approaches 1, all latent and sensible heat fluxesfor which the Bowen ratio lies between 1.3 and 0.7 weredeemed unreliable and were not used in the analysis. As may beclear from the figure unfortunately this occurred during the lateafternoon AHS overpass on June 6th.


Fig. 2. Goermin test site, measuring 4.9 km in North-South and 7.6 km in East-West direction, not to scale. The left panel shows the land surface temperaturefrom an AHS overpass on June 6th, whereas the right panel shows the main crop types and ground station locations.

The receiver of the scintillometer set-up was mounted at atower construction at 4.76 m above the ground and the trans-mitter at a 420 m distance in a South-Eastern direction at 4.92 mabove the ground. Details of the set-up are provided in [6]. Sincethe LAS station was only operated during the last intensive ob-servation period, only data for the July overpass was available.

B. Remote Sensing Observations and Parameter RetrievalOptical airborne data acquisitions were conducted by the

Spanish Instituto Nacional the Tecnica Aerospacial (INTA),operating their Airborne Hyperspectral Scanner (AHS) [38] onJune 6th and 10th and on July 4th and 5th to catch part of theseasonal variability. During June 10th overpass major parts ofthe area were covered by clouds and between July 4th and 5thhydrologic conditions were rather stable. Therefore, we useddata obtained from the June 6 (14:42 utc) and the July 5 (10:20utc) acquisitions. Overall soil moisture conditions were twiceas low during the July acquisition as compared to the Juneacquisitions [6]. Hyperspectral imagery in the visible, nearinfrared and thermal infrared wavelength regions was obtainedfrom an altitude of 975 m, resulting in a spatial resolution of2.4 m.

At-surface reflectances (labelled level 2b) were providedby the INTA Remote Sensing Laboratory (Spain), the or-ganisation in charge of AHS imagery acquisition, processingand quality analysis [39]. They used ATCOR4 code [40] forthe atmospheric correction of AHS VNIR bands, which isbased on MODTRAN4 and performs a scan angle atmosphericcorrection taking into account flight altitude and illuminationconditions. At-surface reflectance was validated using fieldspectroscopy acquired in-situ over different samples, showingaverage errors of around 10 percent [39].

At-sensor radiances (labelled level 1b) at AHS TIR bands71 to 80, ranging from 8.18 to 12.93 , were checked versusground observations by the Global Change Unit of the Univer-sity of Valencia, yielding an overall accuracy around 1 [K].

Imagery obtained from the Airborne Hyperspectral Scanner(AHS) was used to obtain remotely sensed parameters needed

in the two models ( , and ) for mappingsurface energy balance components.

Reflection: Surface albedo, , was computed using thefollowing very simplified approach [41]:

(20)where the weight factors 0.45 and 0.55 are retrieved fromthe solar extraterrestrial spectrum. Albedo estimations fromAHS data were compared to in-situ measurements for the Julyflights, over a single reference point (LAS station), with a biasof and a RMSE of for that particular point.At-surface reflectances from AHS bands 9 and 12 were usedfollowing

(21)

to obtain a Normalized Difference Vegetation Index (NDVI)needed in the S-SEBI approach for calculating soil heat fluxesand in the TSEB approach for determining vegetation charac-teristics.

Vegetation Characteristics: The canopy density needed in theTSEB algorithm, parameterized by fCover and LAI, are derivedfollowing a procedure described in detail in [42]. In short, a ra-diative transfer model, PROSAILH, which is a combination ofthe well-known PROSPECT [43] and SAILH [44], [45] models,is inverted to estimate LAI and fCOVER. In this study a fastlook up table (LUT) approach has been chosen because it per-mits a global search and thus avoids trapping into local minimaas occurs with optimisation methods. Moreover, it shows lessunexpected behaviour than neural networks when the spectralsignal of the surface is not well simulated. As such it presentsa good compromise between physical complexity and compu-tation time requirements and has been therefore preferred overmodels with a high parameterization load [42].

Emission: Land surface temperature and emissivity was re-trieved simultaneously from AHS TIR data (level 1b) using theTemperature and Emissivity Separation (TES) algorithm, origi-nally developed to be applied to TERRA/ASTER data [25] and


TABLE IIIDIFFERENCE STATISTICS BETWEEN GROUND OBSERVATIONS AND MODEL OUTPUT

firstly applied to AHS data in [46]. The TES algorithm is com-posed of three modules: NEM, RATIO and MMD. The NEMmodule is an iterative procedure that provides a first guess fortemperature and emissivity. The RATIO module then normal-izes the surface emissivities, providing the so-called beta spec-trum. Finally, the MMD module uses a semi-empirical relation-ship between the minimum emissivity and the spectralcontrast (MMD). AHS bands 72, 73, 75, 76, 77, 78 and 79 wereused to apply the TES algorithm, with the following relation-ship between and MMD [46]:

(22)

where bands 71, 74 and 80 were removed because of their loca-tion in absorption regions. For more details, see [37].

IV. VALIDATION

A. Parameter RetrievalAs for the surface albedo only one validation point was avail-

able, the current section will only describe the retrieval accuracyof the vegetation characteristics and the surface temperature.

Vegetation Characteristics: The LAI and fractional covervalues derived from the imagery are plotted versus field obser-vations, carried out over several different crops, in Fig. 4.

For the overpass of June 6th, the results were slightly betterthan for July 5th, which might be attributed to a less accurateatmospheric correction, due to shattered clouds. Correlation co-efficients were equal to 0.8 and 0.9 for fCover and LAIrespectively, whereas root mean squared differences (RMSD)equalled 0.25 and 0.65 respectively. It should be noted that thefield observations on fractional vegetation cover, though usingstandardized digital photographs, were analysed visually by dif-ferent groups, giving them a slightly subjective character.

Land Surface Temperature: First of all an accuracy assess-ment of the level 1b TIR bands was made in order to checkfor calibration problems. In-situ measurements collected over53 samples were used to this end, and compared to values ex-tracted from the complete AHS imagery database during the en-tire AgriSAR campaign. The results showed a minor bias of lessthan 0.2 [K] and an overall RMSD lower than 1.1 [K]. Groundobservations of land surface temperature [47] over the 53 sam-ples were compared versus LST derived using the TES algo-rithm [25]. The results showed a correlation equal to 0.9for the two days of overpass used here and are shown in Fig. 5.

Both bias and standard deviation are higher than 1 [K], pro-viding a final RMSD less than 2 [K].

In particular, for the two flights considered here, a bias of 1[K] was found for the June image and 2.3 [K] for the July image(with standard deviation values of 0.4 K in both cases), whichsuggests a better accuracy for June than in July. Like in the caseof the vegetation characteristics, this is probably due to a betteratmospheric correction of the June image.

B. Surface Heat FluxesValidation data concerning turbulent fluxes and radiation for

the time of airplane overpasses were obtained from the afore-mentioned BREB station, as well as from the LAS station in-stalled in the same wheat field [6]. In order for this ground datato be comparable with the radiation and flux estimates fromthe two models to so-called field-of-view of the sensors hadto be determined. For the radiative fluxes, and

, a weighted average of the flux output of 3 3pixels surrounding the BREB and LAS stations were taken. Forthe turbulent fluxes the procedure is slightly more complicatedand an appropriate footprint needs to be applied [48], [49]. Themethodology recently described in [50] is followed to derive thefootprint of both the BREB as well as for the LAS station.

Fig. 6 shows the model flux output of the two models versusground observation.

Although at first sight the TSEB algorithm flux output seemsto resemble more the 1:1 fit, both models do perform simi-larly versus ground observations. Although not many groundobservations were available, ground observations and model es-timates for all four flux components as well as RMSD are shownin Table III. During the overpass on June 6th, the ground obser-vations of the fluxes needed interpolation between values justbefore and after the exact overpass time. In case these valuesare excluded from the observations the S-SEBI seems to per-form better. However, with the notable exception of the soil heatfluxes, the results for both models appear to be less accurate thanwhat is generally achieved [11], [21], [22].

Although we will focus here on the spatial differences be-tween the two models we will shortly highlight potential ex-planations for the observed behavior. First of all, the quality ofthe essential input parameters was not always optimal, due touncertainty in atmospheric corrections. For example an error inthe derivation of land surface temperature and especially surfacealbedo directly influence the net radiation estimation, (6). Forother parameters that the models are most sensitive to, i.e. the


Fig. 3. Evolution of the surface energy budget components during the two days of AHS overpass as measured by the BREB station. Note that the station wasremoved at the end of the last day of overpass.

Fig. 4. Ground-based observation of LAI and fCOVER versus model-based estimates. Open symbols represent data obtained for June 6th, filled symbols representdata for July 5th.

Fig. 5. Validation of the LST product obtained with the TES algorithm. Opensymbols represent data obtained for June 6th, filled symbols represent data forJuly 5th.

line determination for S-SEBI [19], see Fig. 3, and the sur-face temperature for TSEB [11], these inaccuracies might havehad a significant impact. For example, a deviation of 2 [K] insurface temperature may invoke a deviation of 44 or 74% inH values for TSEB [11].

Secondly, there were only very limited validation points forthe two days processed. For example for the latent heat fluxonly two observations were made, reducing the robustness ofthe validation. This might even be emphasized by the fact thatof these limited observations one occurred during late afternoon,when measured turbulent fluxes started to fluctuate, potentiallyreducing the quality of this observation, as mentioned before.

Lastly, homogeneous atmospheric and meteorological inputparameters were used, representative for the modeling domain.Over highly contrasting surfaces the assumption of homoge-neous meteorological conditions might not be suitable, a pointthe TSEB algorithm is sensitive to [51]. Though minor, this mayhave influenced the June results for the TSEB approach, whendry bare fields showed a higher surface temperature than sur-rounding fields. On the other hand, homogeneous atmosphericconditions are a prerequisite for the S-SEBI approach [17], [21],an assumption that might be violated during the July image ac-quisition.

Following [3], [12], other remote sensing observations canbe a proper source of model evaluation as well. The only datasuitable for the current study are SAR-derived soil moisture ob-servations, as described in [7], though they are acquired at a


Fig. 6. Ground observation versus model estimates of the surface energy balance components for S-SEBI and TSEB. Diamonds represent , triangles show G,squares represent H and circles show .

Fig. 7. Difference maps of TSEB minus S-SEBI output for net radiation and soil heat flux over the modeling domain for the June and July overpasses.

lower resolution. After down-scaling the spatial resolution ofthe flux models output to the SAR resolution, a spatial com-parison is made between the TSEB and S-SEBI output for LEand the SAR-derived soil moisture. No SAR data was avail-able for DOY 157, but since there was no rain, data acquiredon DOY 158 is used for this purpose, as well as data from DOY186. The Pearson correlation coefficient is calculated for aboutone-third (32%) of the pixels in the area; no data was avail-able for the remaining 68%. Correlation coefficients for DOY157 were 0.16 and 0.23 for TSEB and S-SEBI respectively,whereas for DOY 186 slightly lower values were found; 0.11 forTSEB and 0.17 for S-SEBI. A possible explanation for the lesseragreement towards the end of the dry season is the disconnec-tion between upper soil moisture and deeper rooted vegetationtranspiration fluxes, as also discussed in [15]. However, deter-mining a robust means of characterizing the level of agreementbetween distinct, yet physically linked processes derived from

remotely sensed imagery, remains a considerable challenge andadditional work is clearly required to bridge the gap betweenqualitative and quantitative analysis before the utility of thesedata can be fully exploited [12].

V. MODEL INTER-COMPARISON

Ground observation stations are often placed in areas withrelatively homogeneous footprints in order to facilitate a properinterpretation of the observations. These sites are as such notrepresentative of unique or extreme circumstances that are ofspecial interest [52]. Therefore in the current contribution wefocus on the spatial inter-comparison between the two models.This is done for both days by creating difference maps, obtainedby subtracting S-SEBI output from TSEB output for all fourfluxes, as well as by analysing two-dimensional plots of S-SEBIoutput versus TSEB output. Because for both models there is a


Fig. 8. Two-dimensional plots of S-SEBI and TSEB output for net radiation and soil heat flux over the modeling domain for the June and July overpasses.

strong linkage between and and be-tween and , they are discussed sep-arately. However, a first observation is that for all four fluxesdifferences seem related to landuse and that differences for theradiative or conductive fluxes are considerably smaller than forthe turbulent fluxes.

A. Radiative/Conductive Fluxes

Maps showing the discrepancies between the S-SEBI andTSEB output for net radiation and soil heat flux are provided inFig. 7, where black represents a difference of ,indicating a larger value for S-SEBI output than for TSEB andwhite represents positive differences of 150 , indi-cating a lower value for S-SEBI output than for TSEB.

Although the differences are related to landuse, they are smallin magnitude; areal average differences were ranging from 25to 30 for net radiation whereas for soil heat fluxesaverage differences were less then 10 . Highest differ-ences are noted over bare soil and rape, which are both char-acterized by a high reflectance. S-SEBI uses measurements ofreflectance and TSEB uses nominal reflectance values, whichexplains the differences in estimates over these landuseclasses.

However, in 80% of the area model differences in net radia-tion estimates for both days are less then 50 , whereasfor the soil heat fluxes differences were less than 50 in97% of the area. This is reflected in the two-dimensional scat-terplots in Fig. 8, where the majority of the points are scatteredaround the 1:1 line. Two remarkable features that are noted from

the graphs are firstly the relatively flat response for deter-mination in TSEB on June 6th as compared to the S-SEBI esti-mates and secondly the bi-modal response for , andto a lesser extent also for .

The bi-modal response can be easily explained by the occur-rence of bare soil fields, which yield a distinctively higher soilheat flux than for vegetated fields. This phenomenon is morepronounced for the June image, since in July almost all the fieldswere under cultivation. The bare soil fields yield a lower net ra-diation due to a higher reflection of shortwave radiation of thesoil, reason why also in the net radiation a slightly bi-modalresponse is seen. The relatively flat response of for TSEBon June 6th is more difficult to explain. Most probably the di-rect use of surface albedo by S-SEBI causes a stronger spatialvariation than the LAI and fCOVER used in TSEB. The TSEBmodel uses nominal values for soil and canopy reflection de-pending on landcover. A better approach might be to derivecomponent albedos using a similar approach as presented by[42] who retrieved crop characteristics by inverting a radiativetransfer model. However, in general there is a quite strong re-semblance in response for both the as well as the G fluxestimates between both models.

B. Turbulent FluxesThe turbulent fluxes show larger discrepancies, as may be

noticed from Fig. 9, where the S-SEBI output is subtractedfrom the TSEB output to produce difference maps. Negativedifferences mean that S-SEBI output is larger than TSEBoutput and are represented by dark tones. Positive deviations,represented by lighter tones, indicate that S-SEBI output islower then TSEB output. Since there is a clear inverse relation


Fig. 9. Difference maps of TSEB minus S-SEBI output for the turbulent fluxes [W m-2] over the modeling domain for the June and July overpasses.

Fig. 10. Two-dimensional plots of S-SEBI and TSEB output for the turbulent fluxes [W m-2] over the modeling domain for the June and July overpasses.

between and , especially for S-SEBI,a positive discrepancy between the two models foris followed by a negative discrepancy in .

For the turbulent fluxes the differences seem related tolanduse. This is also noted in the two-dimensional scatter-plotsin Fig. 10, where clearly a stronger correlation between TSEBand S-SEBI is seen for the July case, although a relatively large

bias is noted. Although the June case shows a considerablespread, in general there is a better agreement between thetwo models. This is especially true for the latent heat fluxeswhere a major part of the area approaches the 1:1 line. Whatis noted is that when latent heat fluxes are relatively high(indicating the crops are not under stressed conditions), there isa better model agreement for the turbulent fluxes. On the other


TABLE IVMODEL OUTPUT FOR TURBULENT FLUXES AVERAGED PER LANDCOVER

hand, when latent heat fluxes are relatively low (indicatingdry conditions and high sensible heat fluxes), the differencesbetween the model output is grouped. Averaging turbulent fluxmodel output per natural landcover type, see Table IV, revealssignificant differences which are more pronounced during theJune overpass. These differences are largest over landcovertypes with relatively tall crops, in other words over landcoverwith a high roughness, .

In general there is a stronger correlation between the modelsfor the latent heat flux than for the sensible heat. Most probablythis is due to the fact that for TSEB the latent heat flux is cal-culated as a residual term (when crops are under water-limitedconditions). As such it incorporates effects of the net radiationand soil heat flux estimation, similar as in S-SEBI, whereas thesensible heat flux is calculated in a more direct way from tem-perature differences mainly.

For the July case there is a larger bias between the two modelsthan for the June image. This might be partly explained by theatmospheric conditions, which were far from uniform; a prereq-uisite for the S-SEBI algorithm. This also hampered the atmo-spheric correction. Since TSEB uses nominal values for reflec-tion this may explain the large overall bias for the July image.Moreover, a lower accuracy of the land surface temperature re-trieval, has potentially a large impact on TSEB output [11], [33].

Since the turbulent fluxes are inversely related from here on-wards we will use the evaporative fraction as defined in (2) toanalyse some of the observed differences. In order to do so thecorrelation between the observed difference in and the spa-tial inputs of both models have been examined, Table V.

It appears that the surface temperature has the highest overallcorrelation to the observed difference followed by the vegetationcharacteristics fCOVER, LAI and NDVI, which show rathersimilar behaviour. Thereafter the canopy aerodynamic charac-teristics, , and and to a lesser extent the leafwidth,all in [m], appear to have an effect on the difference in modeloutput as well. Since these are linked through the vegetation

TABLE VCORRELATION BETWEEN DISCREPANCY BETWEEN TSEB AND S-SEBI

EVAPORATIVE FRACTION AND RS-BASED INPUTS

height, , they show a similar behaviour. They appear tohave a considerable influence in the June image, whereas forthe July overpass this is negligible. The contrary holds true forthe surface temperature which has a larger impact in July thanin the June case. Surface albedo appears to have the least influ-ence on observed differences between evaporative fraction es-timates. This is reflected in Fig. 11 where ,and are plotted versus differences .

It is noted that in general differences in are highest athigher , especially for July, and at higher , espe-cially for June, and at lower , especially for July.The other parameters show a relatively flat response with respectto the difference in . The difference in model output at high

and and at low is a known phenom-enon when comparing two-source models with one-source orindex models. Two-source models, designed for use over partly


Fig. 11. Relation between discrepancy between TSEB and S-SEBI evaporative fraction and relevant spatial inputs.

vegetated areas, are especially effective when the two sources,soil and canopy, show very different radiometric behavior andatmospheric coupling [53][55], which is especially the caseunder drier, or hotter, conditions.

As previously mentioned the larger bias between the twomodels for the July case may be due to the non-uniform at-mospheric conditions, invoking unfavorable circumstances forboth models. Another aspect which might explain the largerdifferences for the July case is the contrast in the spatial input.The contrast is much less for the July case than for the Junecase, especially for albedo and , see Table VI, whichmight pose a problem in the determination of the regressionvariables in (8). Although the algorithm is not extremely sen-sitive to the values of these variables [21], it may have causedpart of the discrepancy in model estimates of for July.

When conditions for both models are favorable, i.e. an accu-rate retrieval for TSEB and spatial hydrologic contrastand uniform atmospheric conditions for S-SEBI, the bias be-tween the two models is less. This is the case for the June image.The observed differences between the two models for this caseoriginate from the differences in aerodynamic properties, espe-

TABLE VISPATIAL STANDARD DEVIATION IN RELEVANT RS-BASED INPUTS

cially , assigned in the TSEB algorithm, since the other spa-tial inputs show a rather flat response with respect to the differ-ence in . For high values of the roughness length especiallylarge discrepancies, up to 40 or 50%, are noted.

VI. SUMMARY AND CONCLUSIONAn inter-comparison has been made between two distinc-

tively different surface energy balance models over an agricul-


tural site. The S-SEBI model is an index method requiring min-imal ancillary data, but requiring spatial hydrologic contrast anduniform atmospheric conditions. The TSEB model is a two-source energy balance approach with a high physical realism,making it potentially applicable over any landscape, but requiresconsiderable ancillary input. Though not excellent, both modelsperformed similarly versus ground observations of energy bal-ance components for the two days examined.

When examining spatial differences for all four fluxes the dif-ferences are related to landuse with differences for the radiativeor conductive fluxes being considerably smaller than for the tur-bulent fluxes. The net radiation and soil heat fluxes showed com-parable responses for both days, although TSEB output for netradiation showed a relatively flat response. This is attributed tothe use of nominal values for soil and canopy reflectances. Largedifferences between the two models were noted for the turbulentflux estimation, showing opposite results between the two days.Due to an insufficient number of ground validation observationsof turbulent fluxes, no firm conclusion could be drawn on abso-lute model performance.

However, when the basic input requirements for both modelsare satisfied, the main discrepancies between the turbulent fluxestimates originate from differences invoked by a variationin canopy aerodynamic properties. This effect is more pro-nounced under dry conditions over tall crops. When appliedover relatively homogeneous areas and also when applied undernon-stressed conditions model outputs are more in agreement.The recommendation from this paper is thus that under suchconditions rather simple index methods are adequate for deter-mining turbulent flux exchange at the earths surface, providedthe model requirements are satisfied.

ACKNOWLEDGMENT

The authors would like to thank Gabrille De Lannoy, DavyLoete, and Ingo Keding for their help in the installation andmaintenance of the Bowen ratio and soil moisture stations,and Ard Blenke, Remco Dost, Joris Timmermans, and KitsiriWeligepolage for their assistance in the installation and main-tenance of the LAS station.

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Wim J. Timmermans received the M.Sci. degreein civil engineering from Delft University of Tech-nology, Delft, The Netherlands.

He is currently affiliated as a researcher at theWater Resources Department of the Faculty ofGeo-Information Science and Earth Observation(ITC), Twente University, Enschede, The Nether-lands, where he is working on remote sensing basedmonitoring of hydrological processes. His researchinterest lies in seeking a better understanding ofsurface hydrology over a wide spectrum of spatial

and temporal scales. He is focusing on the coupling of the Earths surfaceand its atmosphere, with implications for surface hydrology, meteorology, andclimate.

Juan-Carlos Jimnez-Muoz received the Ph.D.degree in physics from the University of Valencia,Spain, in 2005.

He is currently a Research Scientist in the GlobalChange Unit of the Department of Earth Physicsand Thermodynamics at the University of Valencia.His main research interests include thermal remotesensing and temperature/emissivity retrieval.

Victoria Hidalgo received the Bachelor degree inphysics from the University of Valencia, Spain, in2007. Currently, she is a Ph.D. student working in theGlobal Change Unit of the University of Valencia.

Her current research focuses on thermal remotesensing, more specifically land surface temperature,emissivity, and evapotranspiration retrieval.

Katja Richter received the Ph.D. degree from theUniversity of Natural Resources and Life SciencesVienna, Austria, in 2009.

From 2007 to 2009, she was with the Departmentof Agricultural Engineering and Agronomy atthe University of Naples Federico II, within thePLEIADeS project, addressing the use of Earth Ob-servation based technologies for the determinationof irrigation water requirements. Currently, she iswith the Department of Geography, Ludwig-Maxi-milians Universitaet Munich, Germany, where she

is working within the EnMAP Core Science Team (ECST) for agriculture. Inthis context she focuses on the development of fast and efficient methods forthe retrieval of biophysical variables from the future German hyperspectralsatellite mission.


Jose Antonio Sobrino has been Professor of physicsand remote sensing at the University of Valencia,Spain, since 1994, and is presently heading theGlobal Change Unit. He is the coordinator of severalremote sensing research projects at the national andinternational levels. He is a member of the Earth Sci-ence Advisory Committee (ESAC) of the EuropeanSpace Agency (ESA). He is Director of the SpanishJournal of Remote Sensing. His research interestsinclude atmospheric correction in the visible andinfrared domains, the retrieval of emissivity, surface

temperature, and evapotranspiration rates from satellite imagery, and thedevelopment of remote sensing methods for land cover dynamic monitoring.

Guido DUrso was born in Salerno, Italy, in 1961.He received the Ph.D. degree from the AgriculturalUniversity of Wageningen, The Netherlands.

He is a Full Professor of agricultural hydraulics, ir-rigation management and remote sensing at the Uni-versity of Naples Federico II. He is a Civil Engineerat the University of Naples. He has coordinated sev-eral international research projects in the field of re-mote sensing for water management, as documentedby more than 80 publications on scientific journal,congress proceedings, and specialized books. His re-

search activities are focused on three main areas: development of Earth Obser-vation interpretation techniques for water management and land surface pro-cesses, distributed agro-hydrological models for water management and irriga-tion, and in-situ and remote active microwave sensing of soil water content.Among other activities, he has been Chair of the International Symposium Re-mote Sensing Europe, International Society of Optical Engineers (SPIE), in20072008, Chair of the Working Group of International Commercial Agri-cultural Engineering (CIGR) on Earth Observation for Land and Water Engi-neering, and Representative of Italian Cooperation, Ministry of Foreign Affairs,to the Steering Committee for the International Project FAO-ITALY Informa-tion Products for Nile Basin Water Resources Management.

Giuseppe Satalino received the Laurea degree (cumlaude) in computer science from the Universityof Bari, Italy, in 1991. In the same year, he was aSummer Student at the European Organization forNuclear Research (CERN), Geneva, Switzerland,where he worked on applications of neural networksto high energy physics.

From 1993 to 1996, he was grant-holder ofAlenia and Consiglio Nazionale delle Ricerche(CNR). Since 1996, he has been with the Instituteof Intelligent Systems for Automation (ISSIA) of

the CNR, Bari, Italy. He worked in several national and international researchprojects in the field of image processing, data classification and remotesensing applications. He participated in conducting several SAR sensors andground radar experiments for studies concerning the use of remote sensingfor agricultural and hydrologic applications. His main research field concernsdata classification techniques and methods for the retrieval of geo-physicalparameters from SAR and optical remote-sensed data.

Francesco Mattia (M99SM08) received theLaurea degree in physics and the Master degreein signal processing from the University of Bari,Italy, in 1990 and 1994, respectively. He receivedthe Ph.D. degree from the University Paul Sabatier,Toulouse, France, in 1999.

From 1991 to 1994, he was a grant-holder of theItalian National Council of Research (CNR) and ofthe European Commission at the Institute for RemoteSensing Applications of the Joint Research Centre,Ispra, Italy. From 1995 to 2003, he was a research

scientist at the Institute for Information and Space Technology (ITIS) of CNR,Matera, Italy. In 2003, he joined the Institute for Intelligent Systems and Au-tomation (ISSIA) of CNR, Bari, Italy, where he is presently a senior researchscientist. During 1996, 1997, 1998, and 1999, he was a visiting scientist at theCentre dEtudes Spatiales de la BIOsphere (CESBIO), Toulouse, France. His sci-entific interests include the direct and inverse modeling of microwave scatteringfrom land surfaces and the use of information derived from Earth observationsensors to improve land surface process models. On these themes, he has beeninvolved as CI or PI in several national and international scientific proposals con-cerning SAR sensors aboard ERS-1/2, ENVISAT, ALOS and COSMO-SkyMedsatellites.

In 2007, Dr. Mattia was co-organizer of the Fifth International Symposiumon Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Ap-plications held in Bari, Italy.

Els De Lathauwer received the degree of Bio-En-gineer in Soil and Water Management from GhentUniversity, Belgium, in July 2008. Her master thesisdealt with the determination of precipitation reduc-tion factors using radar images. In September 2008she began her Ph.D. research at the Laboratory of Hy-drology and Water Management at Ghent University.

Her research focuses on the determination ofspatially distributed soil physical parameter valuesthrough a combination of remote sensing and hydro-logic modeling.

Valentijn R. N. Pauwels received the M.Sci. degreeof Engineer in Agricultural Sciences from Ghent Uni-versity, Belgium, in 1994. In 1999 he received thePh.D. degree in civil engineering and operations re-search from Princeton University, Princeton, NJ.

From 1999 through 2004, he was a postdoctoral re-search associate at the Laboratory of Hydrology andWater Management, Ghent University, at which hebecame an Associate Professor in 2005. His major re-search interest is hydrologic model development andoptimization.

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