Agricultural and Forest Meteorology 106 (2001) 215–231

Modification of DAISY SVAT model forpotential use of remotely sensed data

Peter van der Keura,∗, Søren Hansena, Kirsten Scheldeb, Anton Thomsenba Department of Agricultural Sciences, The Royal Veterinary and Agricultural University, Laboratory for Agrohydrology and Bioclimatology,

Agrovej 10, DK-2630 Taastrup, Denmarkb Department of Crop Physiology and Soil Sciences, Danish Institute of Agricultural Sciences,

Research Centre Foulum, P.O. Box 50, DK-8830 Tjele, Denmark

Received 30 April 1999; received in revised form 7 August 2000; accepted 19 August 2000

Abstract

The SVAT model DAISY is modified to be able to utilize remote sensing (RS) data in order to improve prediction ofevapotranspiration and photosynthesis at plot scale. The link between RS data and the DAISY model is the developmentof the minimum, unstressed, canopy resistancermin

c during the growing season. Energy balance processes are simulated byapplying resistance networks and a two-source model. Modeled data is validated against measurements performed for a winterwheat plot. Soil water content is measured by time domain reflectometry. Crop dry matter content and leaf area index aremodeled adequately. Modeled soil water content, based on a Brooks and Corey [Brooks, R.H., Corey, A.T., 1964. Hydraulicproperties of porous media. Hydrology Paper no. 3, Colorado University, Fort Collins, CO, 27 pp.] parameterization, from 0 to20, 0 to 50 and 0 to 100 cm is calibrated satisfactorily against measured TDR values. Simulated and observed energy fluxes aregenerally in good agreement when water supply in the root zone is not limiting. With decreasing soil moisture content duringa longer drought period, modeled latent heat flux is lower than observed, which calls for both improved parameterizations forenvironmental controls and for a improved estimation of thermin

c parameter. © 2001 Elsevier Science B.V. All rights reserved.

Keywords:Crop energy balance; Remote sensing; Minimum canopy resistance; DAISY model

1. Introduction

Spatially distributed information on land surfacecharacteristics can be retrieved by means of remotesensing from satellite or other platforms and has beenused extensively in land use mapping, e.g. crop man-agement, and subsequently stored in GeographicalInformation Systems. Another potentially powerfulapplication of remote sensing data (RS data) is pro-

∗ Corresponding author. Tel.:+45-3528-3560/ext. 3544;fax: +45-3528-3384.E-mail addresses:pvdk@ruc.dk (P. van der Keur), sha@kvl.dk(S. Hansen).

viding the link between measuring spatially varyingbiophysical properties and hydrological modeling(Tenhunen et al., 1999; Waring and Running, 1999).Soil moisture and vegetation development, usuallyhighly variable in both time and space and verydifficult to quantify at larger scales, exert strong con-trol on the surface energy balance and hydrologicalprocesses. These facts make the use of RS data inmodeling such processes very attractive. Models at-tempting to address landscape level processes needto be deliberately designed to use remotely sensedvariables (Wessman et al., 1999).

Historically, basically three approaches have beenadopted for coupling evapotranspiration to remote

0168-1923/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved.PII: S0168-1923(00)00212-4

216 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

sensing data (see, e.g. Ottlé et al. (1996) for a review).The first method is based on conversion of observedthermal radiance into surface temperature, which isthen used to calculate sensible heat flux. The latentheat flux is then calculated from the surface energybalance as a residual of the net radiation, estimatedground heat flux and the RS estimated sensible heatflux (Hatfield, 1983; Moran et al., 1989; Kustas, 1990).The second method relies on estimation of surfaceenergy fluxes from either remotely sensed vegetationindex data and surface radiant temperature (Tuckeret al., 1981; Gillies et al., 1994) or from surface bright-ness temperatures as measured with a microwave ra-diometer (Njoku and Patel, 1986). Surface brightnesstemperature can be used to infer the soil moisturecontent of the upper few centimeters of the soil profile(e.g. Wang et al., 1989) and used as a boundary condi-tion for the calculation of the surface evaporation ratewhere soil evaporation is the dominant component ofthe latent heat flux (Sellers, 1991). Alternatively, ac-tive microwave RS (radar) can be used to infer top soilmoisture content or in combination with passive mi-crowave RS (Chauhan, 1997). The third method, andthe one pursued in this study, relies on the ability toinfer information on the photosynthetic capacity andthe minimum canopy resistance (rmin

c ) from spectralvegetation indices (e.g. Asrar et al., 1984; Monteith,1977; Sellers, 1985, 1987; Sellers et al., 1992a,b).Specifying the correct change in minimum canopy re-sistance with time is crucial and incorporates changesin both leaf area index and stomatal resistances (Dol-man, 1993). This link is here taken as the point ofdeparture for the use of remotely sensed data in mod-eling evapotranspiration processes in soil–vegetation–atmosphere–transport schemes (SVATS) models, atvarious spatial scales. No direct means is yet availableto monitor minimum stomatal resistance from space,but subtle shifts in the reflectance spectrum in visiblewavelengths that relate to diurnal changes in photo-synthetic efficiency also mirror changes in stomatalresistance (Gamon et al., 1992). In this study, however,focus is on the unstressed stomatal resistancermin

c , i.e.minimum canopy resistance, that is upscaled throughLAI. It can be inferred by RS data, and therefore in-herently contains information on plant physiologicalstatus throughrmin

c and LAI. Sellers (1991) summa-rizes the limitations of all three approaches to convertsatellite sensed data to the desirable surface param-

eters including problems with sensor calibration,atmospheric/geometric correction, conversion of radi-ance to surface parameters and finally conversion ofsurface parameters to biophysical quantities.

The soil–plant–atmosphere system model DAISY(Hansen et al., 1991) was prepared to accommodateuse of remotely sensed, initially ground based, datafor simulation of evapotranspiration. In the presentapproach, simulated actual evapotranspiration was ei-ther at potential rate and estimated empirically fromstandard meteorological data (e.g. Makkink, 1957) orless than potential rate being controlled by the extrac-tion of soil water by plant roots (Hansen et al., 1991).This method precluded the incorporation of remotelysensed data in the model in the sense proposed inthis paper. Instead, an energy balance approach basedon a two-source resistance network, allowing sparsecanopy cover, is added to the model. Stomatal resis-tance is part of this resistance network and regulatesthe amount of water available through stomata path-ways for plant transpiration and intake of carbon diox-ide for photosynthesis. Thus, in summary, unstressedstomata resistance scaled to the canopy level by LAIcan be related to both RS data, i.e. spectral vegetationindices, and actual canopy resistance and has the po-tential to provide a link between SVAT modeling andRS data.

The purpose of this paper is to describe the methodfollowed to prepare the DAISY model for RS data in-put as envisaged within the framework of the Danishfunded RS-MODEL/earth observation project. Thetwo-source model, allowing for sparse canopy cover(Shuttleworth and Wallace, 1985; Shuttleworth andGurney, 1990), is added to the DAISY model struc-ture and modeled surface energy fluxes, soil moisturecontent and crop development are validated againstexperimental data from a winter wheat plot underDanish conditions.

2. Model concepts

2.1. DAISY model description

The soil–plant–atmosphere system model DAISY(Hansen et al., 1991) is a deterministic, one-dimensio-nal, mechanistic model for the simulation of cropproduction and water and nitrogen balance in the root

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 217

zone. The model includes sub-models for evapotran-spiration, soil water dynamics based on the Richard’sequation, water and nitrogen uptake by plants, soilheat flow due to conduction and convection. Soil min-eral nitrogen dynamics are based on the convection–dispersion equation and nitrogen transformation inthe soil is simulated as mineralization–immobilizationturnover (MIT), nitrification and denitrification. Thecrop model simulates plant phenological develop-ment, gross and net photosynthesis, growth andmaintenance respiration and root penetration and rootdistribution. The model considers root, stem, leaf andstorage organs. Of special interest in this study is thesimulation of leaf area index, which only depends onsimulated leaf dry matter and the development stateof the plant. The crop model takes water and nitrogenstress into account. In addition, the model includesa module for agricultural management practice. Themodel is described in detail elsewhere (Hansen et al.,1991; Petersen et al., 1995). The model has beenvalidated in a number of studies (de Willigen, 1991;Jensen et al., 1994; Diekkrüger et al., 1995; Svendsenet al., 1995; Smith et al., 1997).

2.2. SVAT resistance network approach adapted toDAISY

The model was extended to include a canopy re-sistance approach for simulation of surface energyfluxes, latent and sensible heat flux, and thereby im-plementing a regulatory mechanism for vapor flowfrom the canopy to the atmosphere which can belinked to remotely sensed data.

During periods in the early growing season and afterharvest, the contribution of bare soil evaporation can-not be ignored, thus a ‘sparse crop canopy’ approachwas adopted, i.e. a modified Shuttleworth–Wallacemodel (Shuttleworth and Wallace, 1985; Shuttleworthand Gurney, 1990). DAISY simulated water and heatflow in an underlying soil profile was utilized topredict bare soil evaporation, i.e. no soil resistanceexpressions were applied.

2.3. Two-source model approach

Early in the growing season and after harvest ofthe agricultural crop, energy fluxes calculated by theone-source ‘big leaf’ model (Monteith, 1965), assum-

ing fully developed canopy cover, are contaminatedby contributions from the bare soil. The two-sourcemodel applied in this study allows for partitioning ofincoming solar energy into a soil and a vegetative frac-tion divided by leaf area index, thus compensating forthe shortcomings of the simplified one-source modelunder sparse crop cover conditions. Shuttleworth andWallace (1985) derived a resistance network typemodel which allowed for energy partitioning andapproached a closed canopy as well as bare soil con-ditions in the two limiting cases when LAI comesnear to full canopy value and zero, respectively. LaterChoudhury and Monteith (1988) developed a similarmodel and included the interaction of evaporationfrom the soil and foliage expressed by changes inthe saturation vapor pressure deficit of air in thecanopy. Shuttleworth and Gurney (1990) extendedthe model by Shuttleworth and Wallace (1985) with arelationship between surface temperature and canopybehavior in sparse canopies as part of an effort tocouple multi-source models to remote sensing data.The studies mentioned here all use soil resistances fordescribing vapor flow from the soil to the atmosphere.Numerous expressions have been derived for soil re-sistances as function of water content or soil vapordensity and diffusivity (see Bastiaanssen (1996) for areview). In this study, use of empirical soil resistanceformulations was circumvented by the coupling ofthe two-source model to the DAISY model. In thismore physically based method soil evaporation wascalculated by the Richard equation as upward drivenwater flow towards the soil surface, i.e. exfiltration,based on the evaporative demand determined by thepotential evaporation as a function of the energyavailable through the LAI function and restricted bythe hydraulic properties of the upper soil layer.

The sparse crop energy balance is described asfollows, refer to Fig. 1.

Sensible heat flow between mean source height andreference height

Ha = ρcpTc − Ta

raa(1)

Sensible heat flow between leaf surface and meansource height

Hl = ρcpTl − Tc

rac(2)

218 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

Fig. 1. Schematic representation of resistance network and energyfluxes for the two-source SVAT component of the DAISY model.Aerodynamic resistancesras, rac and raa as well as energy fluxesλE andH with indices s, l and a are defined between mean sourceheight and soil, leaf, and reference height, respectively.Ts, Tc andTa are defined as temperatures at soil surface, mean canopy heightand reference height, respectively.

Sensible heat flow between soil surface and meansource height

Hs = ρcpTs − Tc

ras(3)

Latent heat flow between mean source height andreference height

λEa = λec − ea

raa(4)

Latent heat flow between leaf surface and meansource height

λEl = λe∗l − ec

rsc + rac(5)

Latent heat flow between soil surface and meansource heightλEs is calculated in DAISY as the sumof upward flowing water from the soil matrix belowand evaporation from eventual ponded water. It is as-sumed that the lateral flux between the soil and themean canopy source height is negligible, i.e. water re-leased at the soil surface is transported without lateralloss to the mean canopy height node. In Eqs. (1)–(5),Ta, Tc, Tl andTs (K) are temperatures of air, in-canopyat mean source height, leaf and soil surface, respec-tively, and ea, ec, es (kg m−3) are water vapor con-tents at reference height, canopy mean source heightand soil surface. It is assumed that stomatal air space

in the leaves is at 100% humidity, thereforee∗l is sat-urated vapor pressure at the leaf surface. All fluxesare in W m−2. Resistancesraa, ras, rac andrsc (s m−1)are defined in Eqs. (17), (18), (22) and (24), respec-tively, and λ is latent heat of vaporization of water(J kg−1). Conservation of fluxes through the canopyyield Eqs. (6) and (7)

Conservation of sensible heat

Ha −Hl −Hs = 0 (6)

Conservation of latent heat

λEa − λEl − λEs = 0 (7)

Conservation of energy for the plant canopy leadsto

λEl +Hl = Al (8)

and for the soil surface

λEs +Hs = As (9)

Furthermore, as mentioned above, it is assumed thatthe stomatal air space in the leaves is at 100% andthat this humidity (kg m−3) is a function of the leafsurface temperature

e∗l = e∗a +∆(Tl − Ta) (10)

where∆ is the slope of the saturation function forwater vapor atTa.

Al andAs in Eqs. (8) and (9) is energy that must betransmitted as sensible and latent heat into the air fromthe plant canopy and the soil surface, respectively,expressed in Eqs. (11) and (12)

Al = Rn(1 − e−kL) (11)

and

As = R−kLn −G (12)

whereRn is net radiation (W m−2), G is ground heatflux (W m−2), L is leaf area index andk is an extinctionparameter (k = 0.5). Rn is net radiation described as

Rn = (1 − α)Si + Ld − Lu (13)

whereSi is global radiation (W m−2), α albedo andLd and Lu downwards and upwards longwave radia-tion (W m−2), respectively, in which net long radia-tion Ld −Lu is a function ofTa andea. Net radiation

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 219

is thus estimated from standard meteorological data:global radiationSi , air temperatureTa, vapor pressureea and from relative duration of sunshinensun. Thelongwave component is calculated by means of theBrunt (1932) equation (Rosenberg et al., 1983)

Ld − Lu = σ(T 4a (b1 − b2

√(ea))(b3 + b4nsun)) (14)

where air temperatureTa is in K, ea in kPa, bi areconstants (b1 = 0.53, b2 = 0.0065, b3 = 0.1 andb4 = 0.9) andσ is the Stefan–Boltzmann constant.The relative duration of sunshinensun is derived byrewriting the Prescott (1940) formula

Si = (ap + bpnsun)Sex (15)

whereSi is measured global radiation andSex is theextraterrestrial radiation, a function of latitude andtime of year, andap and bp are site specific coeffi-cients. For the Danish Foulum location at 56◦30′Nan average of the (daily) values found at DeBilt(52◦N, Kohsiek, 1971, unpublished) and Matanuscka-Anchorage, Alaska (61◦N, Baker and Haines, 1969)were used (references from Brutsaert (1982), p. 135),i.e. ap = 0.21 andbp = 0.50. The phase differencein calculated extraterrestrial radiation and measuredglobal radiation at solar and local time, respectively,is accounted for.G is calculated fromTs and a DAISYcalculated upper soil temperature,T0

G = khTs − T0

1z(16)

where1z is depth of the upper soil layer andkh is ther-mal conductivity of that layer returned by the DAISYmodel.

Eqs. (1)–(3) are substituted in Eq. (6), Eqs. (4) and(5) in Eq. (7) and Eqs. (11) and (12) in Eqs. (8) and (9),respectively. Then a linear system of five equations(Eqs. (6)–(10)), and five unknowns,Ts, Tc, Tl , ec ande∗l is obtained and solved using a Gauss–Jordan elim-ination. For each time step the involved resistancesin the linear equation system are computed usingEqs. (17)–(29). Then, sensible and latent heat fluxescan be calculated by back substitution in Eqs. (1)–(5).

2.4. Network resistances

In unstable (temperature lapse) conditions, verticalmotions are enhanced by buoyancy, effectively reduc-ing the aerodynamic resistanceraa (Thom, 1975). This

can be accounted for by applying a stability correctionfactor based on the Businger–Dyer profiles (Dyer andHicks, 1970; Dyer, 1974; Businger, 1988). Eq. (17)including the stabilityψ functionsψm andψh, de-fined for unstable conditions in Paulson (1970) and forstable conditions in Webb (1970), is applied by, e.g.Dolman (1993)

raa= 1

kufln

(zref − d

h− d

)+ ψ∗

m − ψ∗h

h

nK(h)

×(

exp

(−n

(1 −

(z0 + d

h

)))− 1

)(17)

whereh is vegetation height (m),d is displacementheight (m),z0 is roughness length (m),uf is frictionvelocity (m s−1), zref is reference height (usually 2 m)and n is an eddy decay coefficient with a typicalvalue of about 2.5.K(h) is an eddy diffusion coeffi-cient and defined in Eq. (19). The stability functionsψ∗

m and ψ∗h are defined asψ∗

m = ψm(zref/Lo) −ψm(h/Lo)andψ∗

h = ψh(zref/Lo) − ψh(h/Lo) inwhich Lo is the Obukhov stability length. Stabilityconditions between canopy mean height and referenceheight are here evaluated by the Richardson numberRi(Thom, 1975), unstable whenRi < 0 and stable whenRi > 0. It is commonly assumed (similarity hypoth-esis), that under fully forced convection conditions asingle canopy aerodynamic resistance term,raa, ratherthan separate aerodynamic resistances for vapor andheat flow,rav and rah, can be used to calculate bothlatent and sensible heat fluxes, i.e.raa = rav = rah(Thom, 1975). Nichols (1992) derivedrav andrah sep-arately from measured latent and sensible heat fluxes,respectively, by the Bowen ratio method above 0.75 mhigh sparsely vegetated shrubs in west central Nevada,USA. It was found thatrav generally was one order ofa magnitude higher thanrah. Under the conditions atFoulum for 1997 it is assumed that the similarity hy-pothesis is valid until derived values from eddy covari-ance measurements become available for verification.

Resistance between soil surface and canopy air,ras,is derived by Choudhury and Monteith (1988)

ras = heαk

αkK(h)(e−αkzs

0/h − e−αk(d+z0)/h) (18)

and the eddy diffusion coefficientK(h)

K(h) = κ2u(h− d)

ln((zref − d)/z0)(19)

220 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

whereαk is attenuation coefficient of eddy diffusiv-ity through sparse canopy set to 2.0,zs

0 (=10−2 m) isroughness length for the soil surface,κ is the von Kár-máns constant (=0.41),u is wind speed at referenceheight and the rest previously defined. In common withChoudhury and Monteith (1988) and Shuttleworth andGurney (1990)d and z0 are calculated as functionsof leaf area index derived from second-order closuretheory (Shaw and Pereira, 1982), yielding Eqs. (20)and (21) in whichcd is the mean drag coefficient fora leaf, set to 0.05 (Shuttleworth and Gurney, 1990)

d = 1.1h ln(1 + (cdL)0.25) (20)

and

z0=zs

0 + 0.3h(cdL)0.25 for 0 ≤ cdL ≤ 0.2

0.3h1 − d

hfor 0.2< cdL ≤ 1.5

(21)

The aerodynamic resistance between leaves and meansource height is defined by Jones (1983) and Choud-hury and Monteith (1988)

rac = αu

2aL

(w

u(h)

)0.5 1

1 − e−0.5αu(22)

whereu(h) is wind speed ath

u(h) = u

(ln((h− d)/z0)

ln((zref − d)/z0)

)(23)

and w (=0.01 m) is average leaf width,a (=0.01m s−1/2) is a constant andαu (=3.0) is attenuationcoefficient for wind speed.

Finally, canopy resistancersc is parameterized fol-lowing Dickinson (1984), Jarvis (1976) and Noilhanand Planton (1989) by using four constraint functionsF1 to F4 and taking into account the physiology of thevegetation as applied by, e.g. Bougeault (1991) andTourula and Heikinheimo (1998)

rsc = rmins

L(F1F2F3F4)

−1 (24)

whereF1 is a function related to solar radiation andhere parameterized following Dolman et al. (1991)

F1 = Si(T + Si)−1

1000(100+ T )−1(25)

whereT is taken to be 250 W m−2 as optimized foroats in Dolman (1993).

The response of stomata to changing ambient hu-midity has been the subject of some controversy(Monteith, 1995a; Lhomme et al., 1998) and it hasbeen proposed that stomata respond to the rate oftranspiration rather than air humidity per se (Mott andParkhurst, 1991). However, as there are uncertaintiesas how to upscale alternative constraint formulationsas proposed by Monteith (1995b) from leaf to canopy,the Lohammar et al. (1980) environmental function isapplied based on the findings that in many species, thestomatal resistance increases as the relative humiditydecreases, i.e. as the leaf-to-air water vapor concen-tration difference increases (Turner, 1991), thus

F2 = (1 + ζ(e∗a − ea))−1 (26)

using the value of 0.57 kg−1 m3 for ζ , applied byVerma et al. (1993) for tall grass.

Although many parameterizations of stomatal resis-tance neglect the influence of ambient air temperature(see Lhomme et al. (1998) for a review) it has earlierbeen stated, e.g. Dickinson (1984), that stomatal re-sistance usually shows a decrease with increasing airtemperature to a maximum value and then an increaseat still higher ambient temperatures. This temperatureoptimum varies with species and can be increased bygrowth at high temperatures and vice versa.F3 relatedto air temperature (Dickinson, 1984)

F3 = 1 − ξ(Tref − Ta)2 (27)

whereξ = 0.0002 K−2 (Jarvis, 1976).Tref is a ‘refer-ence temperature’ (Noilhan et al., 1991) or optimumtemperature as explained above (Turner, 1991) set to298 K by Dickinson (1984).

Stomatal resistance increases as the soil dries, wheresoil water status influences stomatal conductance ei-ther through its influence on leaf water potential or bychanges in the level of phytohormones produced byroots in response to soil dehydration. These processesare represented by theF4 function taking account ofwater stress (Bougeault, 1991)

F4 = θ − θwilt

θc − θwilt(28)

where θc and θwilt are soil water content at ‘fieldcapacity’ (at 2.0 pF) and ‘wilting point’ (at 4.2 pF),respectively, and estimated from soil hydraulic prop-erties as described later in Section 4. The root zone

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 221

soil moisture contentθ is calculated as an averageof DAISY simulated water content at nodes within0–100 cm.

The minimum resistancerminc , i.e. rmin

s /L in Eq.(24) is the parameter of interest for linking energybalance modeling, e.g. latent heat flux (evapotranspi-ration), to remote sensing data. However, as such datais not yet available for this study, minimum canopyresistance is estimated fromrc in Eq. (29) (Allenet al., 1989; FAO, 1990)

rc = rday

0.5L= 200

L(29)

whererday is the average daily (24 h) stomatal resis-tance of a single leaf. Sellers et al. (1992a,b) estimatedrminc to be between 40 and 120 s m−1 for crops, cor-

responding to LAI values from 5 to 1.7, respectively,in Eq. (29).

3. Study area and 1997 field campaigns

Field scale data for the RS-model project wascollected at selected sites with agricultural crops atthe Research Centre Foulum (RCF, 56◦30′N, 9◦36′E,altitude 45 m above sea level) in Jutland, Denmark.For the plot-scale study here focus was on a winterwheat crop. Height of the crop throughout the grow-ing season varied from 0.31 m (16 May), 0.41 m (26May), 0.70 m (9 June), 0.80 m (24 June), 0.87 m (30June) and 0.85 m until 8 August. Schjønning (1992)investigated the soils surrounding RCF and found formost of the profiles a rather homogeneous distributionof texture with depth. Soil content of clay generallyincreases from 7% in the topsoil to about 10–15% inthe deepest part of the profile. The fine sand fraction(20–200mm) was found to make up about half theparticles in all depths. Meteorological data includingradiation, precipitation, air humidity, air temperatureand wind profiles, as well as water and carbon dioxidefluxes by means of eddy covariance equipment havebeen monitored at a winter wheat and spring barleysite. Canopy related measurements such as spectralreflectance, leaf angle distribution, cover fraction,leaf area index, biomass and water content werespecifically designed for accommodating the studyof combining crop modeling and remote sensing. Inaddition soil moisture at various levels was measured

by means of time domain reflectometry (TDR) usinghorizontal and vertical probes. Data on soil tempera-ture and ground heat fluxes were also collected.

3.1. Meteorological data

During the 1997 experiment all relevant meteoro-logical data was measured locally at the winter wheatsite, see Fig. 2, and was used as forcing data for theDAISY model. However, during the ‘spin-up’ periodprior to the 1997 campaign meteorological data from anearby climate station (distance approximately 1 km)at the Foulum Research Centre is applied. Since thecrop in question is a winter wheat, the ‘spin-up’ periodwas from June 1996 to approximately the beginning ofApril 1997 when some of the field campaign measure-ments were initiated. Missing local values from theclimate mast at the winter wheat site were replaced bydata from the nearby Foulum climate station. On-siteprecipitation data, available during the period of inter-est, was used for simulation purposes. Applied timestep was 1 h.

3.2. Soil moisture measurements

Continuous soil profile measurements of water con-tent by TDR using single horizontal probes at 5, 10,15, 20, 30, 40 and 50 cm on half hourly basis werecompiled for the period 14 May to 8 August and av-eraged to hourly values. Soil water content by verti-cally inserted TDR probes for depths 0–20, 0–50 and0–100 cm, were also available for the same period. Theapplied TDR system includes a 1502B/C Tektronix ca-ble tester (Tektronix Inc., Beaverton, OR) operated bya portable PC, a TSS 45 Tektronix multiplexer, inter-face electronics (Thomsen and Thomsen, 1994), andtwo wire TDR probes. Measured dielectric constantsby the Tektronix cable tester were converted to volu-metric water content by the Topp et al. (1980) equa-tion. The software used for the analysis of TDR tracesis discussed by Thomsen (1994).

3.3. Energy flux measurements

Eddy covariance measurements at the winter wheatsite were made using an open path system comprisingan Ophir IR-2000 Optical Hygrometer and a METEKUSA-1 1D ultrasonic anemometer operating at 20 Hz.

222 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

Fig. 2. Daily averaged meteorological variables at winter wheat plot from April to September 1997.

Instruments were mounted on a mast at a height of2.5 m.

The eddy covariance and meteorological station wasplaced close (50 m) to the northern edge of the longand flat field of winter wheat. Fetch conditions to the Nand NE of the station were considered inadequate dueto the presence of a farm surrounded by tall trees (min-imum distance 135 m) and a forest edge (minimumdistance 290 m). Therefore, a bad fetch sector was de-fined comprising 129◦ to the N and NE of the sta-tion. The positive fetch sector, comprising 231◦, wasto the W, S and SE of the station. Borders of the fieldwere windbreaks (3–5 m high) situated at minimumand maximum distances of 200 and 750 m from thestation, respectively. Fetch conditions were adequatethroughout the day on 12 June, 19–21 June, 25–29June, 9–23 July, and 5–7 August 1997. The closureof the energy balance (Rn − B −G− H − λE ∼ 0)was generally not good. A regression of heat fluxes(H + λE) versus available energy (Rn − G), usingall available 30 min concurrent measurements, yielded(H+λE) = 0.71(Rn−G)+5 W m−2 (R2 = 0.87). A

similar regression that included only data on days witha good fetch as defined above, produced (H + λE) =0.70 (Rn − G) + 5 W m−2 (R2 = 0.90), indicatingthat the closure problems were not related to fetchrequirements. This leaves some 30% of the availableenergy to be accounted for by photosynthesis, canopyenergy storage and measurement errors. The compo-nents whose order of magnitude could be evaluatedwere analyzed as described below.

The net radiometer (REBS Q∗7.1, Seattle, WA)was new at the start of the measurement periodand the factory calibration was applied. Net radia-tion measured at the winter wheat field was in thesame order of magnitude as net radiation measuredabove short green grass at the nearby (3 km) Foulummeteorological station. We found a similar agree-ment between soil heat flux observed at the winterwheat and at the grass cover. Even if the energy bal-ances at the two different surface covers were notlikely to be identical, we have confidence that wheatnet radiation and soil heat flux measurements weresound.

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 223

The measurements of latent heat flux were evalu-ated by comparing to changes in water content mea-sured using the automated TDR station. Over periodswith no rainfall, the difference from start to end inwater content in the top 50 or 100 cm soil (eight repli-cates of each probe length) equals the amount of wa-ter lost as soil evaporation and crop transpiration tothe atmosphere. We assume, in agreement to modelpredictions, that there is no significant drainage fromthe soil profile during such dry intervals. During theperiod 10–13 June 1997, the accumulated amount ofwater lost to the atmosphere according to the eddycorrelation measurements was 11 mm, while the watercontent decline recorded by the 50 and 100 cm TDRprobes corresponded to 11 and 13 mm of water, re-spectively. During the equally short dry period 18–20June 1997, latent heat loss was equivalent to 9 mm ofwater, while the 50 and 100 cm TDR probes recordedwater deficits of 9 and 12 mm, respectively. 9–23 July1997 was a long dry spell. Observed water depletionduring this period in the 0–50 and 0–100 cm soil depthwas 49 and 69 mm, respectively. Accumulated latentheat loss during the dry spell was equivalent to 45 mmof water. Since the root zone of the wheat crop proba-bly exceeded 50 cm in July, and since we assume theTDR technique to be accurate in estimating relativechanges in soil water content, the results indicate thateddy covariance estimates of latent heat flux could beunderestimated in July.

4. DAISY model simulations

The model was set up to simulate on hourly basisfrom 1 June 1996 to 31 December 1997. Meteoro-logical forcings (global radiation, air temperature, airhumidity, precipitation and windspeed) were retrievedfrom the RCF climate station using standard equip-ment. During the campaign period local forcing datawere used whenever available as explained previously.The soil profile was partitioned in 20 compartmentswith discretization size varying from 2.5, 5 and 10 cmin the upper 75 cm to 10, 15, 20, 30 cm in the lower75–200 cm. The hydraulic properties were parameter-ized following the Brooks and Corey (1964) modelfor soil water characteristics and the Mualem (1976)model for unsaturated conductivity. No soil profilemeasurements were conducted at the winter wheat site

for laboratory analyses, so hydraulic properties wereestimated from previously performed profile analysesfrom adjacent locations. Brooks and Corey parameterswere determined from an average of three soil profilesat four depth intervals: 0–32.5, 32.5–52.5, 52.5–100,and 100–230 cm. In the absence of locally measuredhydraulic data and aware of the fact that large spa-tial variations in soil physical characteristics probablyoccur, Brooks & Corey parameters were adjusted toobtain good agreement between simulated and TDRmeasured soil moisture content for 0–0.2, 0–0.5 and0–1.0 m. The saturated hydraulic conductivity is cal-culated as a logarithmic average of the three profilesfor each horizon. Surface albedo for calculation of netradiation in Eq. (13) is estimated from measured in-coming and reflected radiation in 640–660 nm (red)using Skye SKR 1800 equipment during June, July andmost of August. The mean value is 0.2 with a slightdecreasing tendency from June to August. The winterwheat crop has been sampled several times during thegrowing season for crop development measurementslike leaf area index, dry matter content and canopyheight. LAI measurements for green, semigreen andyellow leaves by means of scanning in the laboratoryhave been supplemented by LAI2000 (Li-Cor) datafor total LAI. Winter wheat was sown in September1996 and harvested in August 1997. Application ofboth inorganic and organic (pig slurry) fertilizer arein accordance with recommended amounts (Plantedi-rektoratet, 1997/1998).

4.1. Simulation results

4.1.1. Net radiationSimulated net radiation using the Brunt equation

(14) and calculated relative sunshine duration usingEq. (15) for the period 13 June to 7 August yieldedreasonably good agreement during day-time, whereassimulated night-time values generally were too lowcompared to the local net radiometer data (REBSQ∗7, REBS, Seattle, WA). Schelde et al. (1998) ob-served a similar bias for a bare soil in Denmark.Recently, significant discrepancies in estimates ofRnbetween different (12) REBS net radiometers underdifferent conditions were observed and analyzed by,e.g. Kustas et al. (1998), who found significant dif-ferences. Halldin and Lindroth (1992) compared sixnet radiometers and observed differences in output

224 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

Fig. 3. Net radiation estimated using the Brunt equation vs. mea-sured net radiation.

ranging from 6 to 20%. In this study the normal-ized root mean square error (NRMSE) defined as√((Rsim

n − Robsn )2/n), i.e. RMSE, divided by the

mean ofRobsn is 0.36 for day- and night-time val-

ues and 0.25 for day values only, defined bySi >10 W m−2, for the period 13 June to 7 August onhourly basis. Sensitivity analyses suggest that the bestagreement as compared to the Q∗7 measurements(NRSME= 0.31, Fig. 3) is obtained when the param-eternsun in Eq. (14) is equal to zero, corresponding tofull cloudiness, during night-time and estimated fromEq. (15) for day-time periods. The latter approachwas therefore adopted for this study.

4.1.2. Crop development simulationDAISY simulated crop development was evaluated

against measured leaf area index and dry matter con-tent. Modeled LAI showed a too fast development inMay compared to measured green LAI (GLAI) as de-termined in the laboratory (Fig. 4a, below). DuringJune and July modeled LAI was slightly higher thanmeasured GLAI. Simulated total dry matter contentcompared very well to measured total dry matter, i.e.green and dead material (Fig. 4a, top). Sub samplesof dry matter content fractioned after stem, leaf andears (Fig. 4b) were also in good agreement with mod-eled data. Simulated nitrogen content (not shown here)appeared to be close to values sampled from a fieldnearby with the same crop and fertilizer treatment.

Fig. 4. (a) Simulated (—) and measured (d) total winter wheat drymatter content and green LAI. (b) Simulated (—) and measured(d) winter wheat fractions.

4.1.3. Soil water modelingFor the purpose of this study, which is to enable the

DAISY model to be linked to remotely sensed data byadding a resistance network approach as previouslydescribed, modeled soil moisture content for the 0–20,0–50 and 0–100 cm levels must satisfactorily matchTDR measured soil water content with vertically in-serted probes (Fig. 5). However, since the objective of

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 225

Fig. 5. Volumetric soil moisture content measured by time domainreflectometry (d) and simulated by the DAISY model (—) for 10June to 30 July 1997.

this study is not modeling of soil water dynamics, ithas not been attempted to optimize agreement betweensimulated and TDR measured soil moisture content foreach horizontal level, nor to test model performancefor other periods than the calibration period.

4.1.4. Energy balance modelingSimulation of energy fluxes was performed for the

period of 13 June to 7 August, when eddy covariancedata was available. Bare soil evaporation contributionto latent heat flux is assumed to be negligible underfull canopy conditions, but constitutes an increasingpart with decreasing LAI. The relative importance ofthe rmin

c parameter, amenable to RS data, is closerexamined by substitution ofrmin

c − 50%, rminc and

rminc + 50% in Eq. (24) for computation of canopy re-

sistancersc for subsequent use in Eq. (5) for simulationof latent heat flux from leaf surface to mean sourceheight. This is demonstrated in Fig. 6 (lower graph),wherersc, moderated by the environmental constraintfunctions in Eqs. (24)–(28), is modeled for 18 and19 June. From Fig. 6 (upper graph) it is clear thatgiven the same environmental constraints, the valueof rmin

c , as potentially sensed by RS data, is importantfor a correct modeling of latent heat flux throughrscin Eq. (24). However, it must be borne in mind thata correct specification of stress functions, which maybe site-specific to a high degree, are at least equally

Fig. 6. Simulated (—) and measured (d) latent heat fluxes (λE)for different canopy resistance (rsc) values. Lower graph:rsc (—)as calculated from minimum canopy resistance,rmin

c in Eq. (24),rsc from rmin

c + 50% (m) and rsc from rminc − 50% (.). Upper

graph: largest simulatedλE for smallestrsc value and vice versa.

important in this respect. Fig. 7a and b show modeledlatent heat flux and sensible heat flux against mea-sured eddy covariance data as well asrmin

c and canopyresistancersc. At night stomata close and the canopyresistance may be very high, therefore night-time val-ues are truncated in Fig. 7a and b (top). The periods17–22 June and 17–22 July were chosen to illustratedifferences in simulated energy fluxes when no waterstress occurred and under stress, respectively, and bothperiods with sufficient fetch (see Section 3.3). Duringthe first period in June, it is clear that during day-timehoursrsc is very close tormin

c , indicating effectivelyno environmental stress, i.e.Fi close to unity. After aprolonged drought in July (Fig. 2),rsc becomes muchhigher thanrmin

c during day-time hours, mainly as aresult of an increasing regulation by the constraintfunctions, particularly the influence ofF4 in Eq. (28).Fig. 8 supports the notion that a larger discrepancybetween measured and modeledλE occurs when soilwater content in the root zone decreases. Observedlatent heat flux as measured by the eddy covarianceequipment is generally in good agreement with themodeled data in the first period with no water stress.In the second period from 17 to 22 July, energy fluxesare simulated less well, particularly when consideringthat measured latent heat flux is expected to be un-

226 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

Fig. 7. (a) Simulated (—) and measured (d) energy fluxes for17–22 June. Upper graph: stressed (.) and unstressed (- - -) canopyresistance,rsc and rmin

c , respectively. Note that the very highstressed canopy resistance values during night-time are truncated.(b) Simulated (—) and measured (d) energy fluxes for 17–22 July.Upper graph: stressed (.) and unstressed (- - -) canopy resistance,rsc andrmin

c , respectively. Note that the very high stressed canopyresistance values during night-time are truncated.

derestimated. The low simulated fluxes are due to anincreased control by the environmental functions, asmentioned before. Although the hydraulic parametersin theF4 function were derived from actual field dataand therefore physically based, there is little doubt thatthis function overestimated the water stress effect andneeds improving or an alternative parameterization.

Fig. 8. Simulated (—) and measured (d) latent heat fluxes andsimulated 0–50 cm soil moisture content, SMC (- - -). The periods17–22 June (no water stress) and 17–22 July (water stress) arerepresented by top and bottom graph, respectively.

4.2. Potential link to remote sensing data

Correct estimation of minimum canopy conduc-tance is especially important when the DAISY modelis incorporated into a distributed hydrological modelfor simulation of hydro-ecological processes at thelandscape level. Remote sensing data is crucial inaccomplishing this. The canopy resistancersc is lin-early proportional to the upscaled unstressed canopyresistancermin

c and an accurate estimation of thedevelopment ofrmin

c is thus needed as well as acorrect parameterization of the environmental con-straint functions, for modeling energy fluxes. It hasbeen mentioned earlier that thermin

c parameter canbe estimated by means of vegetation indices and thatcould then lead to improved transpiration modelingprovided that a more robust and physically basedparameterization for constraint functions is obtained(e.g. Jacobs, 1994; Monteith, 1995a,b; Baldocchi andMeyers, 1998). From the data presented in this studyit is clear that transpiration modeling is sensitive to acorrectrmin

c level for an agricultural crop during thegrowing season. Equally clear is the need for develop-ment of appropriate stress functions. However, withinthe interest sphere of distributed hydrologic modelingat the landscape level or larger, there is an obvious

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 227

need to sense plant physiological development at thisscale and use this information in simulation of surfaceenergy fluxes.

5. Concluding remarks

The soil plant system simulation model DAISYextended by a two source resistance network formodeling energy balance processes for agriculturalcrops was presented. A two-source approach explic-itly accounts for bare soil evaporation and is thereforemore generally applicable under different vegetationstages. The resistance approach for modeling sur-face energy fluxes provides a link to remotely senseddata through the unstressed canopy resistancermin

cupscaled by means of LAI. A simplified represen-tation of rmin

c was applied as no remotely senseddata is yet available. A Jarvis–Stewart formulationfor coupling the (actual) canopy resistance tormin

cby means of environmental constraint functions wasimplemented. Crop dry matter content and leaf areaindex are modeled adequately. Modeled soil watercontent, based on a Brooks & Corey parameterization,from 0 to 20, 0 to 50 and 0 to 100 cm were cali-brated satisfactorily against measured TDR values.Simulated and observed energy fluxes were generallyin good agreement when water supply in the rootzone was not limiting. With decreasing soil moisturecontent during a longer drought period, modeled la-tent heat flux was lower than observed, calling forboth improved parameterization of environmentalcontrols and for an improved estimation of thermin

cparameter. The latter can potentially be estimated atlandscape scales by means of remote sensing data,which should lead to improved modeling of surfaceenergy fluxes especially in areas where no groundbased information on vegetation development isavailable.

List of symbols

Al available energy at leaf surface(W m−2)

As available energy at soil surface (W m−2)b1, b2, coefficients in Brunt equation

b3, b4

cd mean drag coefficient for a leafcp specific heat of air at constant

pressure (J kg−1 K−1)d displacement height of vegetation (m)ea (measured) water vapor content at

reference height (2 m) (kg m−3)e∗a saturated water vapor content at reference

height (2 m) (kg m−3)ec water vapor content at mean source

height (kg m−3)e∗l saturated water vapor content at leaf

surface (kg m−3)Fi , i = 1, environmental constraint functions of. . . ,4 Jarvis–Stewart type equation forrsc

G ground heat flux (W m−2)H sensible heat flux (W m−2)Ha sensible heat flux from mean source to

reference height (W m−2)Hl sensible heat flux from leaf to mean

source height (W m−2)h vegetation height (m)k extinction coefficient in Beer’s law

type of equationkh thermal conductivity of soil

(W m−1 K−1)L leaf area index (m2 m−2)LAI leaf area index (m2 m−2)Ld downwards longwave radiation (W m−2)Lo Obukhov stability length (m)Lu upwards longwave radiation (W m−2)n eddy decay coefficientnsun relative duration of sunshine

(in Brunt equation)Ri Richardson numberRn net radiation (W m−2)raa aerodynamic resistance between canopy

and reference height (s m−1)rac boundary layer resistance of vegetative

elements in canopy (s m−1)rah aerodynamic resistance for sensible heat

flow (s m−1)ras aerodynamic resistance between soil

surface and mean source height (s m−1)rav aerodynamic resistance for vapor flow

(latent heat) (s m−1)rc canopy resistance as defined in

Allen et al. (1989) (s m−1)

228 P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231

rminc minimum canopy resistance (s m−1)

rday average daily (24 h) stomatal resistance ofsingle leaf (s m−1)

rsc bulk stomatal resistance of the canopy(s m−1)

rmins minimum stomatal resistance (s m−1)

Si global radiation (W m−2)T coefficient inF1 (W m−2)Ta air temperature at reference height

(measured) (K)Tc canopy temperature at mean source

height (K)Tl leaf temperature (K)Tref reference/optimum temperature in

F3 (K)Ts soil temperature (‘skin’ temperature)

(K)Tsurf ‘bulk’ surface temperature (K)T0 soil temperature at depth1z simulated

by DAISY (K)u windspeed at reference height

(measured) (m s−1)u(z) windspeed at heightz (m s−1)w average leaf width in equation forrac (m)x parameter in Businger–Dyer equations

argumentY psychrometric ‘constant’ (h Pa K−1)z0 canopy roughness length (m)zs

0 roughness length for the soilsurface (m)

zref reference height (2 m) (m)

Greek letters

α albedoαk attenuation coefficient of eddy diffusivity

through sparse canopyαu attenuation coefficient of wind speed∆ rate of change of absolute humidity with

temperature (kg m−3 K−1)1z soil depth (m)ζ coefficient inF2 (kg−1 m3)κ von Karmans constantλ latent heat of vaporization of water

(J kg−1)λE latent heat flux (W m−2)λEa latent heat flux from mean source to

reference height (W m−2)

λEl latent heat flux from leaf to meansource height (W m−2)

λEs latent heat flux from soil to mean sourceheight (W m−2)

ψh stability correction function forsensible heat

ψ∗h derivedψh

ψm stability correction function formomentum

ψ∗m derivedψm

ρ density of air (kg m−3)σ Stefan Boltzmann’s constant (W m−2 K−1)θ soil water content simulated by DAISY

(cm3 cm−3)θc soil water content at ‘field capacity’

(cm3 cm−3)θwilt soil water content at ‘wilting point’

(cm3 cm−3)ξ coefficient inF3 (K−2)

Acknowledgements

The RS-Model research program within the frame-work of the Earth Observation Program is fundedby the Danish Space Board Committee, the DanishAgricultural and Veterinary Research Council and theDanish Technical Research Council. Per Abrahamsen(Danish Informatics Network in the Agricultural Sci-ences) is greatly acknowledged for his help on DAISYprogramming and technical advice.

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