AFM_2001_106_215-231

Agricultural and Forest Meteorology 106 (2001) 215–231

Modification of DAISY SVAT model for potential use of remotely sensed data Peter van der Keur a,∗ , Søren Hansen a , Kirsten Schelde b , Anton Thomsen b a

Department of Agricultural Sciences, The Royal Veterinary and Agricultural University, Laboratory for Agrohydrology and Bioclimatology, Agrovej 10, DK-2630 Taastrup, Denmark b 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 of evapotranspiration and photosynthesis at plot scale. The link between RS data and the DAISY model is the development of the minimum, unstressed, canopy resistance rcmin during the growing season. Energy balance processes are simulated by applying resistance networks and a two-source model. Modeled data is validated against measurements performed for a winter wheat plot. Soil water content is measured by time domain reflectometry. Crop dry matter content and leaf area index are modeled adequately. Modeled soil water content, based on a Brooks and Corey [Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology Paper no. 3, Colorado University, Fort Collins, CO, 27 pp.] parameterization, from 0 to 20, 0 to 50 and 0 to 100 cm is calibrated satisfactorily against measured TDR values. Simulated and observed energy fluxes are generally in good agreement when water supply in the root zone is not limiting. With decreasing soil moisture content during a longer drought period, modeled latent heat flux is lower than observed, which calls for both improved parameterizations for environmental controls and for a improved estimation of the rcmin 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 surface characteristics can be retrieved by means of remote sensing from satellite or other platforms and has been used extensively in land use mapping, e.g. crop management, and subsequently stored in Geographical Information Systems. Another potentially powerful application 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 varying biophysical properties and hydrological modeling (Tenhunen et al., 1999; Waring and Running, 1999).

Soil moisture and vegetation development, usually highly variable in both time and space and very difficult to quantify at larger scales, exert strong control on the surface energy balance and hydrological processes. These facts make the use of RS data in modeling such processes very attractive. Models attempting to address landscape level processes need to be deliberately designed to use remotely sensed variables (Wessman et al., 1999).

Historically, basically three approaches have been adopted for coupling evapotranspiration to remote

0168-1923/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 8 - 1 9 2 3 ( 0 0 ) 0 0 2 1 2 - 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 observed thermal radiance into surface temperature, which is then used to calculate sensible heat flux. The latent heat flux is then calculated from the surface energy balance as a residual of the net radiation, estimated ground heat flux and the RS estimated sensible heat flux (Hatfield, 1983; Moran et al., 1989; Kustas, 1990).

The second method relies on estimation of surface energy fluxes from either remotely sensed vegetation index data and surface radiant temperature (Tucker et al., 1981; Gillies et al., 1994) or from surface brightness temperatures as measured with a microwave radiometer (Njoku and Patel, 1986). Surface brightness temperature can be used to infer the soil moisture content of the upper few centimeters of the soil profile (e.g. Wang et al., 1989) and used as a boundary condition for the calculation of the surface evaporation rate where soil evaporation is the dominant component of the latent heat flux (Sellers, 1991). Alternatively, active microwave RS (radar) can be used to infer top soil moisture content or in combination with passive microwave RS (Chauhan, 1997). The third method, and the one pursued in this study, relies on the ability to infer information on the photosynthetic capacity and the minimum canopy resistance (rcmin ) from spectral vegetation indices (e.g. Asrar et al., 1984; Monteith, 1977; Sellers, 1985, 1987; Sellers et al., 1992a,b).

Specifying the correct change in minimum canopy resistance with time is crucial and incorporates changes in both leaf area index and stomatal resistances (Dolman, 1993). This link is here taken as the point of departure for the use of remotely sensed data in modeling evapotranspiration processes in soil–vegetation– atmosphere–transport schemes (SVATS) models, at various spatial scales. No direct means is yet available to monitor minimum stomatal resistance from space, but subtle shifts in the reflectance spectrum in visible wavelengths that relate to diurnal changes in photosynthetic efficiency also mirror changes in stomatal resistance (Gamon et al., 1992). In this study, however, focus is on the unstressed stomatal resistance rcmin , i.e. minimum canopy resistance, that is upscaled through LAI. It can be inferred by RS data, and therefore inherently contains information on plant physiological status through rcmin and LAI. Sellers (1991) summarizes the limitations of all three approaches to convert satellite sensed data to the desirable surface param-

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

The soil–plant–atmosphere system model DAISY (Hansen et al., 1991) was prepared to accommodate use of remotely sensed, initially ground based, data for simulation of evapotranspiration. In the present approach, simulated actual evapotranspiration was either at potential rate and estimated empirically from standard meteorological data (e.g. Makkink, 1957) or less than potential rate being controlled by the extraction of soil water by plant roots (Hansen et al., 1991).

This method precluded the incorporation of remotely sensed data in the model in the sense proposed in this paper. Instead, an energy balance approach based on a two-source resistance network, allowing sparse canopy cover, is added to the model. Stomatal resistance is part of this resistance network and regulates the amount of water available through stomata pathways for plant transpiration and intake of carbon dioxide for photosynthesis. Thus, in summary, unstressed stomata resistance scaled to the canopy level by LAI can be related to both RS data, i.e. spectral vegetation indices, and actual canopy resistance and has the potential to provide a link between SVAT modeling and RS data.

The purpose of this paper is to describe the method followed to prepare the DAISY model for RS data input as envisaged within the framework of the Danish funded RS-MODEL/earth observation project. The two-source model, allowing for sparse canopy cover (Shuttleworth and Wallace, 1985; Shuttleworth and Gurney, 1990), is added to the DAISY model structure and modeled surface energy fluxes, soil moisture content and crop development are validated against experimental data from a winter wheat plot under Danish conditions.

2. Model concepts 2.1. DAISY model description The soil–plant–atmosphere system model DAISY (Hansen et al., 1991) is a deterministic, one-dimensional, mechanistic model for the simulation of crop production and water and nitrogen balance in the root

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

zone. The model includes sub-models for evapotranspiration, soil water dynamics based on the Richard’s equation, water and nitrogen uptake by plants, soil heat flow due to conduction and convection. Soil mineral nitrogen dynamics are based on the convection– dispersion equation and nitrogen transformation in the soil is simulated as mineralization–immobilization turnover (MIT), nitrification and denitrification. The crop model simulates plant phenological development, gross and net photosynthesis, growth and maintenance respiration and root penetration and root distribution. The model considers root, stem, leaf and storage organs. Of special interest in this study is the simulation of leaf area index, which only depends on simulated leaf dry matter and the development state of the plant. The crop model takes water and nitrogen stress into account. In addition, the model includes a module for agricultural management practice. The model is described in detail elsewhere (Hansen et al., 1991; Petersen et al., 1995). The model has been validated in a number of studies (de Willigen, 1991; Jensen et al., 1994; Diekkrüger et al., 1995; Svendsen et al., 1995; Smith et al., 1997).

2.2. SVAT resistance network approach adapted to DAISY The model was extended to include a canopy resistance approach for simulation of surface energy fluxes, latent and sensible heat flux, and thereby implementing a regulatory mechanism for vapor flow from the canopy to the atmosphere which can be linked to remotely sensed data.

During periods in the early growing season and after harvest, the contribution of bare soil evaporation cannot be ignored, thus a ‘sparse crop canopy’ approach was adopted, i.e. a modified Shuttleworth–Wallace model (Shuttleworth and Wallace, 1985; Shuttleworth and Gurney, 1990). DAISY simulated water and heat flow in an underlying soil profile was utilized to predict bare soil evaporation, i.e. no soil resistance expressions were applied.

2.3. Two-source model approach Early in the growing season and after harvest of the agricultural crop, energy fluxes calculated by the one-source ‘big leaf’ model (Monteith, 1965), assum-

217

ing fully developed canopy cover, are contaminated by contributions from the bare soil. The two-source model applied in this study allows for partitioning of incoming solar energy into a soil and a vegetative fraction divided by leaf area index, thus compensating for the shortcomings of the simplified one-source model under sparse crop cover conditions. Shuttleworth and Wallace (1985) derived a resistance network type model which allowed for energy partitioning and approached a closed canopy as well as bare soil conditions in the two limiting cases when LAI comes near to full canopy value and zero, respectively. Later Choudhury and Monteith (1988) developed a similar model and included the interaction of evaporation from the soil and foliage expressed by changes in the saturation vapor pressure deficit of air in the canopy. Shuttleworth and Gurney (1990) extended the model by Shuttleworth and Wallace (1985) with a relationship between surface temperature and canopy behavior in sparse canopies as part of an effort to couple multi-source models to remote sensing data.

The studies mentioned here all use soil resistances for describing vapor flow from the soil to the atmosphere.

Numerous expressions have been derived for soil resistances as function of water content or soil vapor density and diffusivity (see Bastiaanssen (1996) for a review). In this study, use of empirical soil resistance formulations was circumvented by the coupling of the two-source model to the DAISY model. In this more physically based method soil evaporation was calculated by the Richard equation as upward driven water flow towards the soil surface, i.e. exfiltration, based on the evaporative demand determined by the potential evaporation as a function of the energy available through the LAI function and restricted by the hydraulic properties of the upper soil layer.

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

Sensible heat flow between mean source height and reference height Ha = ρcp

Tc − Ta raa

(1)

Sensible heat flow between leaf surface and mean source height Hl = ρcp

Tl − Tc rac

(2)

218

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

in the leaves is at 100% humidity, therefore el∗ is saturated vapor pressure at the leaf surface. All fluxes are in W m−2 . Resistances raa , ras , rac and rsc (s m−1 ) are defined in Eqs. (17), (18), (22) and (24), respectively, and λ is latent heat of vaporization of water (J kg−1 ). Conservation of fluxes through the canopy yield Eqs. (6) and (7) Conservation of sensible heat Ha − Hl − Hs = 0

(6)

Conservation of latent heat Fig. 1. Schematic representation of resistance network and energy fluxes for the two-source SVAT component of the DAISY model.

Aerodynamic resistances ras , rac and raa as well as energy fluxes λE and H with indices s, l and a are defined between mean source height and soil, leaf, and reference height, respectively. Ts , Tc and Ta are defined as temperatures at soil surface, mean canopy height and reference height, respectively.

Sensible heat flow between soil surface and mean source height Hs = ρcp

Ts − Tc ras

(3)

Latent heat flow between mean source height and reference height λEa = λ

ec − ea raa

(4)

Latent heat flow between leaf surface and mean source height λEl = λ

el∗ − ec rsc + rac

(5)

Latent heat flow between soil surface and mean source height λEs is calculated in DAISY as the sum of upward flowing water from the soil matrix below and evaporation from eventual ponded water. It is assumed that the lateral flux between the soil and the mean canopy source height is negligible, i.e. water released at the soil surface is transported without lateral loss to the mean canopy height node. In Eqs. (1)–(5), Ta , Tc , Tl and Ts (K) are temperatures of air, in-canopy at mean source height, leaf and soil surface, respectively, and ea , ec , es (kg m−3 ) are water vapor contents at reference height, canopy mean source height and soil surface. It is assumed that stomatal air space

λEa − λEl − λEs = 0

(7)

Conservation of energy for the plant canopy leads to λEl + Hl = Al

(8)

and for the soil surface λEs + Hs = As

(9)

Furthermore, as mentioned above, it is assumed that the stomatal air space in the leaves is at 100% and that this humidity (kg m−3 ) is a function of the leaf surface temperature el∗ = ea∗ + ∆(Tl − Ta )

(10)

where ∆ is the slope of the saturation function for water vapor at Ta .

Al and As in Eqs. (8) and (9) is energy that must be transmitted as sensible and latent heat into the air from the plant canopy and the soil surface, respectively, expressed in Eqs. (11) and (12) Al = Rn (1 − e−kL )

(11)

and As = Rn−kL − G

(12)

where Rn is net radiation (W m−2 ), G is ground heat flux (W m−2 ), L is leaf area index and k is an extinction parameter (k = 0.5). Rn is net radiation described as Rn = (1 − α)Si + Ld − Lu

(13) (W m−2 ),

α albedo and where Si is global radiation Ld and Lu downwards and upwards longwave radiation (W m−2 ), respectively, in which net long radiation Ld − Lu is a function of Ta and ea . Net radiation

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

is thus estimated from standard meteorological data: global radiation Si , air temperature Ta , vapor pressure ea and from relative duration of sunshine nsun . The longwave component is calculated by means of the Brunt (1932) equation (Rosenberg et al., 1983) p Ld − Lu = σ (Ta4 (b1 − b2 (ea ))(b3 + b4 nsun )) (14) where air temperature Ta is in K, ea in kPa, bi are constants (b1 = 0.53, b2 = 0.0065, b3 = 0.1 and b4 = 0.9) and σ is the Stefan–Boltzmann constant.

The relative duration of sunshine nsun is derived by rewriting the Prescott (1940) formula Si = (ap + bp nsun )Sex

(15)

219

can be accounted for by applying a stability correction factor based on the Businger–Dyer profiles (Dyer and Hicks, 1970; Dyer, 1974; Businger, 1988). Eq. (17) including the stability ψ functions ψ m and ψ h , defined for unstable conditions in Paulson (1970) and for stable conditions in Webb (1970), is applied by, e.g.

Dolman (1993)

1 zref − d h ∗ ln − ψh∗ + ψm raa = kuf h−d nK(h)

z0 + d −1 (17) × exp −n 1 − h

Ts − T0 (16) 1z where 1z is depth of the upper soil layer and kh is thermal conductivity of that layer returned by the DAISY model.

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 and el∗ is obtained and solved using a Gauss–Jordan elimination. For each time step the involved resistances in the linear equation system are computed using Eqs. (17)–(29). Then, sensible and latent heat fluxes can be calculated by back substitution in Eqs. (1)–(5).

where h is vegetation height (m), d is displacement height (m), z0 is roughness length (m), uf is friction velocity (m s−1 ), zref is reference height (usually 2 m) and n is an eddy decay coefficient with a typical value of about 2.5. K(h) is an eddy diffusion coefficient and defined in Eq. (19). The stability functions ∗ and ψ ∗ are defined as ψ ∗ = ψ (z /L ) − ψm m ref o m h ψm (h/Lo ) and ψh∗ = ψh (zref /Lo ) − ψh (h/Lo ) in which Lo is the Obukhov stability length. Stability conditions between canopy mean height and reference height are here evaluated by the Richardson number Ri (Thom, 1975), unstable when R i < 0 and stable when R i > 0. It is commonly assumed (similarity hypothesis), that under fully forced convection conditions a single canopy aerodynamic resistance term, raa , rather than separate aerodynamic resistances for vapor and heat flow, rav and rah , can be used to calculate both latent and sensible heat fluxes, i.e. r aa = r av = r ah (Thom, 1975). Nichols (1992) derived rav and rah separately from measured latent and sensible heat fluxes, respectively, by the Bowen ratio method above 0.75 m high sparsely vegetated shrubs in west central Nevada, USA. It was found that rav generally was one order of a magnitude higher than rah . Under the conditions at Foulum for 1997 it is assumed that the similarity hypothesis is valid until derived values from eddy covariance measurements become available for verification.

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

2.4. Network resistances

ras =

where Si is measured global radiation and Sex is the extraterrestrial radiation, a function of latitude and time of year, and ap and bp are site specific coefficients. For the Danish Foulum location at 56◦ 300 N an average of the (daily) values found at DeBilt (52◦ N, Kohsiek, 1971, unpublished) and MatanusckaAnchorage, Alaska (61◦ N, Baker and Haines, 1969) were used (references from Brutsaert (1982), p. 135), i.e. a p = 0.21 and bp = 0.50. The phase difference in calculated extraterrestrial radiation and measured global radiation at solar and local time, respectively, is accounted for. G is calculated from Ts and a DAISY calculated upper soil temperature, T0 G = kh

In unstable (temperature lapse) conditions, vertical motions are enhanced by buoyancy, effectively reducing the aerodynamic resistance raa (Thom, 1975). This

h e αk s (e−αk z0 / h − e−αk (d+z0 )/ h ) αk K(h)

(18)

and the eddy diffusion coefficient K(h) K(h) =

κ 2 u(h − d) ln((zref − d)/z0 )

(19)

220

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

where α k is attenuation coefficient of eddy diffusivity through sparse canopy set to 2.0, z0s (=10−2 m) is roughness length for the soil surface, κ is the von Kármáns constant (=0.41), u is wind speed at reference height and the rest previously defined. In common with Choudhury and Monteith (1988) and Shuttleworth and Gurney (1990) d and z0 are calculated as functions of leaf area index derived from second-order closure theory (Shaw and Pereira, 1982), yielding Eqs. (20) and (21) in which cd is the mean drag coefficient for a leaf, set to 0.05 (Shuttleworth and Gurney, 1990) d = 1.1 h ln(1 + (cd L)0.25 )

(20)

The response of stomata to changing ambient humidity has been the subject of some controversy (Monteith, 1995a; Lhomme et al., 1998) and it has been proposed that stomata respond to the rate of transpiration rather than air humidity per se (Mott and Parkhurst, 1991). However, as there are uncertainties as how to upscale alternative constraint formulations as proposed by Monteith (1995b) from leaf to canopy, the Lohammar et al. (1980) environmental function is applied based on the findings that in many species, the stomatal resistance increases as the relative humidity decreases, i.e. as the leaf-to-air water vapor concentration difference increases (Turner, 1991), thus

and

F2 = (1 + ζ (ea∗ − ea ))−1

The aerodynamic resistance between leaves and mean source height is defined by Jones (1983) and Choudhury and Monteith (1988)

αu 1 w 0.5 (22) rac = 2aL u(h) 1 − e−0.5αu

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

Although many parameterizations of stomatal resistance neglect the influence of ambient air temperature (see Lhomme et al. (1998) for a review) it has earlier been stated, e.g. Dickinson (1984), that stomatal resistance usually shows a decrease with increasing air temperature to a maximum value and then an increase at still higher ambient temperatures. This temperature optimum varies with species and can be increased by growth at high temperatures and vice versa. F3 related to air temperature (Dickinson, 1984)

  z0s + 0.3h(cd L)0.25 for 0 ≤ cd L ≤ 0.2 1−d (21) z0 =  0.3h for 0.2 < cd L ≤ 1.5 h

where u(h) is wind speed at h

ln((h − d)/z0 ) u(h) = u ln((zref − d)/z0 )

(23)

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

Finally, canopy resistance rsc is parameterized following Dickinson (1984), Jarvis (1976) and Noilhan and Planton (1989) by using four constraint functions F1 to F4 and taking into account the physiology of the vegetation as applied by, e.g. Bougeault (1991) and Tourula and Heikinheimo (1998) rsc =

rsmin (F1 F2 F3 F4 )−1 L

(24)

where F1 is a function related to solar radiation and here parameterized following Dolman et al. (1991) F1 =

)−1

Si (T + Si 1000(100 + T )−1

(25)

where T is taken to be 250 W m−2 as optimized for oats in Dolman (1993).

F3 = 1 − ξ(Tref − Ta )2

(26)

(27)

where ξ = 0.0002 K−2 (Jarvis, 1976). Tref is a ‘reference temperature’ (Noilhan et al., 1991) or optimum temperature as explained above (Turner, 1991) set to 298 K by Dickinson (1984).

Stomatal resistance increases as the soil dries, where soil water status influences stomatal conductance either through its influence on leaf water potential or by changes in the level of phytohormones produced by roots in response to soil dehydration. These processes are represented by the F4 function taking account of water stress (Bougeault, 1991) F4 =

θ − θwilt θc − θwilt

(28)

where θ c and θ wilt are soil water content at ‘field capacity’ (at 2.0 pF) and ‘wilting point’ (at 4.2 pF), respectively, and estimated from soil hydraulic properties as described later in Section 4. The root zone

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

soil moisture content θ is calculated as an average of DAISY simulated water content at nodes within 0–100 cm.

The minimum resistance rcmin , i.e. rsmin /L in Eq. (24) is the parameter of interest for linking energy balance modeling, e.g. latent heat flux (evapotranspiration), to remote sensing data. However, as such data is not yet available for this study, minimum canopy resistance is estimated from rc in Eq. (29) (Allen et al., 1989; FAO, 1990) rc =

rday 200 = 0.5L L

(29)

where rday is the average daily (24 h) stomatal resistance of a single leaf. Sellers et al. (1992a,b) estimated rcmin to be between 40 and 120 s m−1 for crops, corresponding 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 was collected at selected sites with agricultural crops at the Research Centre Foulum (RCF, 56◦ 300 N, 9◦ 360 E, altitude 45 m above sea level) in Jutland, Denmark.

For the plot-scale study here focus was on a winter wheat crop. Height of the crop throughout the growing season varied from 0.31 m (16 May), 0.41 m (26 May), 0.70 m (9 June), 0.80 m (24 June), 0.87 m (30 June) and 0.85 m until 8 August. Schjønning (1992) investigated the soils surrounding RCF and found for most of the profiles a rather homogeneous distribution of texture with depth. Soil content of clay generally increases from 7% in the topsoil to about 10–15% in the deepest part of the profile. The fine sand fraction (20–200 ␮m) was found to make up about half the particles in all depths. Meteorological data including radiation, precipitation, air humidity, air temperature and wind profiles, as well as water and carbon dioxide fluxes by means of eddy covariance equipment have been monitored at a winter wheat and spring barley site. Canopy related measurements such as spectral reflectance, leaf angle distribution, cover fraction, leaf area index, biomass and water content were specifically designed for accommodating the study of combining crop modeling and remote sensing. In addition soil moisture at various levels was measured

221

by means of time domain reflectometry (TDR) using horizontal and vertical probes. Data on soil temperature and ground heat fluxes were also collected.

3.1. Meteorological data During the 1997 experiment all relevant meteorological data was measured locally at the winter wheat site, see Fig. 2, and was used as forcing data for the DAISY model. However, during the ‘spin-up’ period prior to the 1997 campaign meteorological data from a nearby climate station (distance approximately 1 km) at the Foulum Research Centre is applied. Since the crop in question is a winter wheat, the ‘spin-up’ period was from June 1996 to approximately the beginning of April 1997 when some of the field campaign measurements were initiated. Missing local values from the climate mast at the winter wheat site were replaced by data from the nearby Foulum climate station. On-site precipitation data, available during the period of interest, was used for simulation purposes. Applied time step was 1 h.

3.2. Soil moisture measurements Continuous soil profile measurements of water content by TDR using single horizontal probes at 5, 10, 15, 20, 30, 40 and 50 cm on half hourly basis were compiled for the period 14 May to 8 August and averaged to hourly values. Soil water content by vertically inserted TDR probes for depths 0–20, 0–50 and 0–100 cm, were also available for the same period. The applied TDR system includes a 1502B/C Tektronix cable tester (Tektronix Inc., Beaverton, OR) operated by a portable PC, a TSS 45 Tektronix multiplexer, interface electronics (Thomsen and Thomsen, 1994), and two wire TDR probes. Measured dielectric constants by the Tektronix cable tester were converted to volumetric water content by the Topp et al. (1980) equation. The software used for the analysis of TDR traces is discussed by Thomsen (1994).

3.3. Energy flux measurements Eddy covariance measurements at the winter wheat site were made using an open path system comprising an Ophir IR-2000 Optical Hygrometer and a METEK USA-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 of 2.5 m.

The eddy covariance and meteorological station was placed close (50 m) to the northern edge of the long and flat field of winter wheat. Fetch conditions to the N and NE of the station were considered inadequate due to the presence of a farm surrounded by tall trees (minimum distance 135 m) and a forest edge (minimum distance 290 m). Therefore, a bad fetch sector was defined comprising 129◦ to the N and NE of the station. The positive fetch sector, comprising 231◦ , was to the W, S and SE of the station. Borders of the field were windbreaks (3–5 m high) situated at minimum and maximum distances of 200 and 750 m from the station, respectively. Fetch conditions were adequate throughout the day on 12 June, 19–21 June, 25–29 June, 9–23 July, and 5–7 August 1997. The closure of the energy balance (R n − B − G − H − λE ∼ 0) was generally not good. A regression of heat fluxes (H + λE) versus available energy (R n − G), using all available 30 min concurrent measurements, yielded (H +λE) = 0.71(R n −G)+5 W m−2 (R 2 = 0.87). A

similar regression that included only data on days with a good fetch as defined above, produced (H + λE) = 0.70 (R n − G) + 5 W m−2 (R 2 = 0.90), indicating that the closure problems were not related to fetch requirements. This leaves some 30% of the available energy to be accounted for by photosynthesis, canopy energy storage and measurement errors. The components whose order of magnitude could be evaluated were analyzed as described below.

The net radiometer (REBS Q∗ 7.1, Seattle, WA) was new at the start of the measurement period and the factory calibration was applied. Net radiation measured at the winter wheat field was in the same order of magnitude as net radiation measured above short green grass at the nearby (3 km) Foulum meteorological station. We found a similar agreement between soil heat flux observed at the winter wheat and at the grass cover. Even if the energy balances at the two different surface covers were not likely to be identical, we have confidence that wheat net radiation and soil heat flux measurements were sound.

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

223

The measurements of latent heat flux were evaluated by comparing to changes in water content measured using the automated TDR station. Over periods with no rainfall, the difference from start to end in water content in the top 50 or 100 cm soil (eight replicates of each probe length) equals the amount of water lost as soil evaporation and crop transpiration to the atmosphere. We assume, in agreement to model predictions, that there is no significant drainage from the soil profile during such dry intervals. During the period 10–13 June 1997, the accumulated amount of water lost to the atmosphere according to the eddy correlation measurements was 11 mm, while the water content decline recorded by the 50 and 100 cm TDR probes corresponded to 11 and 13 mm of water, respectively. During the equally short dry period 18–20 June 1997, latent heat loss was equivalent to 9 mm of water, while the 50 and 100 cm TDR probes recorded water deficits of 9 and 12 mm, respectively. 9–23 July 1997 was a long dry spell. Observed water depletion during this period in the 0–50 and 0–100 cm soil depth was 49 and 69 mm, respectively. Accumulated latent heat loss during the dry spell was equivalent to 45 mm of water. Since the root zone of the wheat crop probably exceeded 50 cm in July, and since we assume the TDR technique to be accurate in estimating relative changes in soil water content, the results indicate that eddy covariance estimates of latent heat flux could be underestimated in July.

for laboratory analyses, so hydraulic properties were estimated from previously performed profile analyses from adjacent locations. Brooks and Corey parameters were determined from an average of three soil profiles at four depth intervals: 0–32.5, 32.5–52.5, 52.5–100, and 100–230 cm. In the absence of locally measured hydraulic data and aware of the fact that large spatial variations in soil physical characteristics probably occur, Brooks & Corey parameters were adjusted to obtain good agreement between simulated and TDR measured soil moisture content for 0–0.2, 0–0.5 and 0–1.0 m. The saturated hydraulic conductivity is calculated as a logarithmic average of the three profiles for each horizon. Surface albedo for calculation of net radiation in Eq. (13) is estimated from measured incoming and reflected radiation in 640–660 nm (red) using Skye SKR 1800 equipment during June, July and most of August. The mean value is 0.2 with a slight decreasing tendency from June to August. The winter wheat crop has been sampled several times during the growing season for crop development measurements like leaf area index, dry matter content and canopy height. LAI measurements for green, semigreen and yellow leaves by means of scanning in the laboratory have been supplemented by LAI2000 (Li-Cor) data for total LAI. Winter wheat was sown in September 1996 and harvested in August 1997. Application of both inorganic and organic (pig slurry) fertilizer are in accordance with recommended amounts (Plantedirektoratet, 1997/1998).

4. DAISY model simulations

4.1. Simulation results

The model was set up to simulate on hourly basis from 1 June 1996 to 31 December 1997. Meteorological forcings (global radiation, air temperature, air humidity, precipitation and windspeed) were retrieved from the RCF climate station using standard equipment. During the campaign period local forcing data were used whenever available as explained previously.

The soil profile was partitioned in 20 compartments with discretization size varying from 2.5, 5 and 10 cm in the upper 75 cm to 10, 15, 20, 30 cm in the lower 75–200 cm. The hydraulic properties were parameterized following the Brooks and Corey (1964) model for soil water characteristics and the Mualem (1976) model for unsaturated conductivity. No soil profile measurements were conducted at the winter wheat site

4.1.1. Net radiation Simulated net radiation using the Brunt equation (14) and calculated relative sunshine duration using Eq. (15) for the period 13 June to 7 August yielded reasonably good agreement during day-time, whereas simulated night-time values generally were too low compared to the local net radiometer data (REBS Q∗ 7, REBS, Seattle, WA). Schelde et al. (1998) observed a similar bias for a bare soil in Denmark.

Recently, significant discrepancies in estimates of Rn between different (12) REBS net radiometers under different conditions were observed and analyzed by, e.g. Kustas et al. (1998), who found significant differences. Halldin and Lindroth (1992) compared six net 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. measured net radiation.

ranging from 6 to 20%. In this study the normalized root mean square error (NRMSE) defined as p ((Rnsim − Rnobs )2 /n), i.e. RMSE, divided by the mean of Rnobs is 0.36 for day- and night-time values and 0.25 for day values only, defined by Si > 10 W m−2 , for the period 13 June to 7 August on hourly basis. Sensitivity analyses suggest that the best agreement as compared to the Q∗ 7 measurements (NRSME = 0.31, Fig. 3) is obtained when the parameter nsun in Eq. (14) is equal to zero, corresponding to full cloudiness, during night-time and estimated from Eq. (15) for day-time periods. The latter approach was therefore adopted for this study.

4.1.2. Crop development simulation DAISY simulated crop development was evaluated against measured leaf area index and dry matter content. Modeled LAI showed a too fast development in May compared to measured green LAI (GLAI) as determined in the laboratory (Fig. 4a, below). During June and July modeled LAI was slightly higher than measured GLAI. Simulated total dry matter content compared very well to measured total dry matter, i.e. green and dead material (Fig. 4a, top). Sub samples of dry matter content fractioned after stem, leaf and ears (Fig. 4b) were also in good agreement with modeled data. Simulated nitrogen content (not shown here) appeared to be close to values sampled from a field nearby with the same crop and fertilizer treatment.

Fig. 4. (a) Simulated (—) and measured (䊉) total winter wheat dry matter content and green LAI. (b) Simulated (—) and measured (䊉) winter wheat fractions.

4.1.3. Soil water modeling For the purpose of this study, which is to enable the DAISY model to be linked to remotely sensed data by adding a resistance network approach as previously described, modeled soil moisture content for the 0–20, 0–50 and 0–100 cm levels must satisfactorily match TDR measured soil water content with vertically inserted probes (Fig. 5). However, since the objective of

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

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

this study is not modeling of soil water dynamics, it has not been attempted to optimize agreement between simulated and TDR measured soil moisture content for each horizontal level, nor to test model performance for other periods than the calibration period.

4.1.4. Energy balance modeling Simulation of energy fluxes was performed for the period of 13 June to 7 August, when eddy covariance data was available. Bare soil evaporation contribution to latent heat flux is assumed to be negligible under full canopy conditions, but constitutes an increasing part with decreasing LAI. The relative importance of the rcmin parameter, amenable to RS data, is closer examined by substitution of rcmin − 50%, rcmin and rcmin + 50% in Eq. (24) for computation of canopy resistance rsc for subsequent use in Eq. (5) for simulation of latent heat flux from leaf surface to mean source height. This is demonstrated in Fig. 6 (lower graph), where rsc , moderated by the environmental constraint functions in Eqs. (24)–(28), is modeled for 18 and 19 June. From Fig. 6 (upper graph) it is clear that given the same environmental constraints, the value of rcmin , as potentially sensed by RS data, is important for a correct modeling of latent heat flux through rsc in Eq. (24). However, it must be borne in mind that a correct specification of stress functions, which may be site-specific to a high degree, are at least equally

225

Fig. 6. Simulated (—) and measured (䊉) latent heat fluxes (λE) for different canopy resistance (rsc ) values. Lower graph: rsc (—) as calculated from minimum canopy resistance, rcmin in Eq. (24), rsc from rcmin + 50% (䉱) and rsc from rcmin − 50% (䉲). Upper graph: largest simulated λE for smallest rsc value and vice versa.

important in this respect. Fig. 7a and b show modeled latent heat flux and sensible heat flux against measured eddy covariance data as well as rcmin and canopy resistance rsc . At night stomata close and the canopy resistance may be very high, therefore night-time values are truncated in Fig. 7a and b (top). The periods 17–22 June and 17–22 July were chosen to illustrate differences in simulated energy fluxes when no water stress occurred and under stress, respectively, and both periods with sufficient fetch (see Section 3.3). During the first period in June, it is clear that during day-time hours rsc is very close to rcmin , indicating effectively no environmental stress, i.e. Fi close to unity. After a prolonged drought in July (Fig. 2), rsc becomes much higher than rcmin during day-time hours, mainly as a result of an increasing regulation by the constraint functions, particularly the influence of F4 in Eq. (28).

Fig. 8 supports the notion that a larger discrepancy between measured and modeled λE occurs when soil water content in the root zone decreases. Observed latent heat flux as measured by the eddy covariance equipment is generally in good agreement with the modeled data in the first period with no water stress.

In the second period from 17 to 22 July, energy fluxes are simulated less well, particularly when considering that 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. 8. Simulated (—) and measured (䊉) latent heat fluxes and simulated 0–50 cm soil moisture content, SMC (- - -). The periods 17–22 June (no water stress) and 17–22 July (water stress) are represented by top and bottom graph, respectively.

4.2. Potential link to remote sensing data

Fig. 7. (a) Simulated (—) and measured (䊉) energy fluxes for 17–22 June. Upper graph: stressed (䉲) and unstressed (- - -) canopy resistance, rsc and rcmin , respectively. Note that the very high stressed canopy resistance values during night-time are truncated. (b) Simulated (—) and measured (䊉) energy fluxes for 17–22 July.

Upper graph: stressed (䉲) and unstressed (- - -) canopy resistance, rsc and rcmin , respectively. Note that the very high stressed canopy resistance values during night-time are truncated.

derestimated. The low simulated fluxes are due to an increased control by the environmental functions, as mentioned before. Although the hydraulic parameters in the F4 function were derived from actual field data and therefore physically based, there is little doubt that this function overestimated the water stress effect and needs improving or an alternative parameterization.

Correct estimation of minimum canopy conductance is especially important when the DAISY model is incorporated into a distributed hydrological model for simulation of hydro-ecological processes at the landscape level. Remote sensing data is crucial in accomplishing this. The canopy resistance rsc is linearly proportional to the upscaled unstressed canopy resistance rcmin and an accurate estimation of the development of rcmin is thus needed as well as a correct parameterization of the environmental constraint functions, for modeling energy fluxes. It has been mentioned earlier that the rcmin parameter can be estimated by means of vegetation indices and that could then lead to improved transpiration modeling provided that a more robust and physically based parameterization for constraint functions is obtained (e.g. Jacobs, 1994; Monteith, 1995a,b; Baldocchi and Meyers, 1998). From the data presented in this study it is clear that transpiration modeling is sensitive to a correct rcmin level for an agricultural crop during the growing season. Equally clear is the need for development of appropriate stress functions. However, within the interest sphere of distributed hydrologic modeling at the landscape level or larger, there is an obvious

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

need to sense plant physiological development at this scale and use this information in simulation of surface energy fluxes.

5. Concluding remarks The soil plant system simulation model DAISY extended by a two source resistance network for modeling energy balance processes for agricultural crops was presented. A two-source approach explicitly accounts for bare soil evaporation and is therefore more generally applicable under different vegetation stages. The resistance approach for modeling surface energy fluxes provides a link to remotely sensed data through the unstressed canopy resistance rcmin upscaled by means of LAI. A simplified representation of rcmin was applied as no remotely sensed data is yet available. A Jarvis–Stewart formulation for coupling the (actual) canopy resistance to rcmin by means of environmental constraint functions was implemented. Crop dry matter content and leaf area index are modeled adequately. Modeled soil water content, based on a Brooks & Corey parameterization, from 0 to 20, 0 to 50 and 0 to 100 cm were calibrated satisfactorily against measured TDR values.

Simulated and observed energy fluxes were generally in good agreement when water supply in the root zone was not limiting. With decreasing soil moisture content during a longer drought period, modeled latent heat flux was lower than observed, calling for both improved parameterization of environmental controls and for an improved estimation of the rcmin parameter. The latter can potentially be estimated at landscape scales by means of remote sensing data, which should lead to improved modeling of surface energy fluxes especially in areas where no ground based information on vegetation development is available.

List of symbols Al As b1 , b2 , b3 , b4

available energy at leaf surface (W m−2 ) available energy at soil surface (W m−2 ) coefficients in Brunt equation

cd cp

227

mean drag coefficient for a leaf specific heat of air at constant pressure (J kg−1 K−1 ) d displacement height of vegetation (m) (measured) water vapor content at ea reference height (2 m) (kg m−3 ) ∗ saturated water vapor content at reference ea height (2 m) (kg m−3 ) water vapor content at mean source ec height (kg m−3 ) ∗ saturated water vapor content at leaf el surface (kg m−3 ) Fi , i = 1, environmental constraint functions of . . . , 4 Jarvis–Stewart type equation for rsc G ground heat flux (W m−2 ) H sensible heat flux (W m−2 ) sensible heat flux from mean source to Ha reference height (W m−2 ) sensible heat flux from leaf to mean Hl source height (W m−2 ) h vegetation height (m) k extinction coefficient in Beer’s law type of equation thermal conductivity of soil kh (W m−1 K−1 ) L leaf area index (m2 m−2 ) LAI leaf area index (m2 m−2 ) downwards longwave radiation (W m−2 ) Ld Lo Obukhov stability length (m) upwards longwave radiation (W m−2 ) Lu n eddy decay coefficient relative duration of sunshine nsun (in Brunt equation) Richardson number Ri net radiation (W m−2 ) Rn aerodynamic resistance between canopy raa and reference height (s m−1 ) boundary layer resistance of vegetative rac elements in canopy (s m−1 ) aerodynamic resistance for sensible heat rah flow (s m−1 ) aerodynamic resistance between soil ras surface and mean source height (s m−1 ) rav aerodynamic resistance for vapor flow (latent heat) (s m−1 ) canopy resistance as defined in rc Allen et al. (1989) (s m−1 )

228

rcmin rday rsc rsmin Si T Ta Tc Tl Tref Ts Tsurf T0 u u(z) w x Y z0 z0s zref

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

minimum canopy resistance (s m−1 ) average daily (24 h) stomatal resistance of single leaf (s m−1 ) bulk stomatal resistance of the canopy (s m−1 ) minimum stomatal resistance (s m−1 ) global radiation (W m−2 ) coefficient in F1 (W m−2 ) air temperature at reference height (measured) (K) canopy temperature at mean source height (K) leaf temperature (K) reference/optimum temperature in F3 (K) soil temperature (‘skin’ temperature) (K) ‘bulk’ surface temperature (K) soil temperature at depth 1z simulated

by DAISY (K) windspeed at reference height (measured) (m s−1 ) windspeed at height z (m s−1 ) average leaf width in equation for rac (m) parameter in Businger–Dyer equations argument psychrometric ‘constant’ (h Pa K−1 ) canopy roughness length (m) roughness length for the soil surface (m) reference height (2 m) (m)

λEl λEs ψh ψh∗ ψm ∗ ψm ρ σ θ

θc θ wilt ξ

latent heat flux from leaf to mean source height (W m−2 ) latent heat flux from soil to mean source height (W m−2 ) stability correction function for sensible heat derived ψ h stability correction function for momentum derived ψ m density of air (kg m−3 ) Stefan Boltzmann’s constant (W m−2 K−1 ) soil water content simulated by DAISY (cm3 cm−3 ) soil water content at ‘field capacity’ (cm3 cm−3 ) soil water content at ‘wilting point’ (cm3 cm−3 ) coefficient in F3 (K−2 )

Acknowledgements The RS-Model research program within the framework of the Earth Observation Program is funded by the Danish Space Board Committee, the Danish Agricultural and Veterinary Research Council and the Danish Technical Research Council. Per Abrahamsen (Danish Informatics Network in the Agricultural Sciences) is greatly acknowledged for his help on DAISY programming and technical advice.

Greek letters α αk αu ∆ 1z ζ κ λ λE λEa

albedo attenuation coefficient of eddy diffusivity through sparse canopy attenuation coefficient of wind speed rate of change of absolute humidity with temperature (kg m−3 K−1 ) soil depth (m) coefficient in F2 (kg−1 m3 ) von K´arm´ans constant latent heat of vaporization of water (J kg−1 ) latent heat flux (W m−2 ) latent heat flux from mean source to reference height (W m−2 )

References Allen, R.G., Jensen, M.E., Wright, J.L., Burman, R.D., 1989. Operational estimates of evapotranspiration. Agron. J. 81, 650–662.

Asrar, G., Fuchs, M., Kanemasu, E.T., Hatfield, J.L., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76, 300.

Baker, D.G., Haines, D.A., 1969. Solar radiation and sunshine duration relationships in the North-Central region and Alaska.

Tech. Bull. No. 262, University of Minnesota, Agric.

Exp. Station, 372 pp.

Baldocchi, D., Meyers, T., 1998. On using eco-physiological micrometeorological and biochemical theory to evaluate carbon dioxide water vapor and trace gas fluxes over vegetation: a perspective. Agric. For. Meteorol. 90, 1–25.

Bastiaanssen, W.G.M., 1996. Regionalization of surface flux densities and moisture indicators in composite terrain. A remote

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 sensing approach under clear skies in Mediterranean climates.

Thesis Landbouwuniversiteit Wageningen.

Bougeault, P., 1991. Parameterization schemes of land-surface processes for mesoscale atmospheric models. In: Schmugge, T.J., André, J.C. (Eds.), Land Surface Evaporation: Measurement and Parameterization. Springer, Berlin.

Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology Paper no. 3, Colorado University, Fort Collins, CO, 27 pp.

Brunt, D., 1932. Notes on radiation in the atmosphere. Q.J.R. Meteorol. Soc. 58, 389–420.

Brutsaert, W., 1982. Evaporation into the atmosphere. Theory, History, and Applications. Reidel Publishing Company, Dordrecht/Boston/London, 1982.

Businger, J.A., 1988. A note on the Businger–Dyer profiles.

Boundary-Layer Meteorol. 42., 145–151.

Chauhan, N.S., 1997. Soil moisture estimation under a vegetation cover. Combined active passive microwave remote sensing approach. Int. J. Remote Sens. 18 (5), 1079–1097.

Choudhury, B.J., Monteith, J.L., 1988. A four-layer model for the heat budget of homogeneous land surfaces. Q.J.R. Metorol.

Soc. 114, 373–398.

Dickinson, R.E., 1984. Modeling evapotranspiration for three-dimensional climate models. Climate Processes and Climate sensitivity. Geophys. Monogr. No. 29, Am. Geophys.

Union, pp. 58–72.

Diekkrüger, B., Söndgerath, D., Kersebaum, K.C., McVoy, C.W., 1995. Validity of agroecosystem models. A comparison of results of different models applied to the same data set. Ecol.

Model. 81, 3–29.

Dolman, A.J., 1993. A multiple-source land surface energy balance model for use in general circulation models. Agric. Meteorol.

65, 21–45.

Dolman, A.J., Gash, J.H.C., Roberts, J.M., Shuttleworth, W.J., 1991. Stomatal and surface conductance of tropical rainforests.

Agric. For. Meteor. 54, 303–318.

Dyer, A.J., 1974. A review of flux-profile relationships.

Boundary-Layer Meteorol. 7, 363–372.

Dyer, A.J., Hicks, B.B., 1970. Flux-gradient relationships in the constant flux layer. Q.J.R. Meteorol. Soc. 96, 715–721.

FAO, 1990. Expert consultation on revision of FAO methodologies for crop water requirements. Annex V. FAO Penman-Monteith Formula.

Gamon, J.A., Penuelas, J., Field, C.B., 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41, 35–44.

Gillies, R.R., Carlson, T.N., Cui, J., Kustas, W.P., Humes, K.S., 1994. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (NDVI) and surface radiant temperature. Int. J. Remote Sens.

18 (15), 3145–3166.

Halldin, S., Lindroth, A., 1992. Errors in net radiometry.

Comparison and evaluation of six radiometer designs. J. Atmos.

Oceanic Technol. 9, 762–783.

Hansen, S., Jensen, H.E., Nielsen, N.E., Svendsen, H., 1991.

Simulation of nitrogen dynamics and biomass production in

229

winter wheat using the Danish simulation model DAISY. Fertilizer Res. 27, 245–259.

Hatfield, J.L., 1983. Evapotranspiration obtained from remote sensing methods. In: Hilled, D.E. (Ed.), Advances in Irrigation.

Academic Press, New York, pp. 395–416.

Jacobs, C.M.J., 1994. Direct impact of atmospheric CO2 enrichment on regional transpiration. PhD thesis, Agricultural University, Wageningen.

Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Phil. Trans. R. Soc. London Ser. B 273, 563–610.

Jensen, C., Stougaard, B., Olsen, P., 1994. Simulation of nitrogen dynamics at three Danish locations by use of the DAISY model.

Acta Agriculturae Scandinavica, Section B. Soil Plant Sci. 44, 75–83.

Jones, H.G., 1983. Plants and Microclimate. Cambridge University Press, New York.

Kustas, W.P., 1990. Estimates of evapotranspiration with a oneand two-layer model of heat transfer over partial canopy cover.

J. Appl. Meteorol. 29., 704–715.

Kustas, W.P., Prueger, J.H., Hipps, L.E., Hatfield, J.L., Meek, D., 1998. Inconsistencies in net radiation estimates from use of several models of instruments in a desert environment. Agric.

For. Meteor. 90, 257–263.

Lhomme, J.-P., Elguero, E., Chehbouni, A., Boulet, G., 1998.

Stomatal control of transpiration. Examination of Monteith’s formulation of canopy resistance. Water Resour. Res. 34 (9), 2301–2308.

Lohammar, T., Larsson, S., Lindner, S., Falk, S., 1980. FAST simulation models of gaseous exchange in Scots pine. Structure and function of northern coniferous forests — an ecosystem study. Ecol. Bull. 32, 505–523.

Makkink, G.F., 1957. Ekzameno de la formula de Penman. The Netherland J. Agric. Sci. 5, 290–305.

Monteith, J.L., 1965. Evaporation and the environment. Symp. Soc. Expl. Biol. 19, 205–234.

Monteith, J.L., 1977. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. London Ser. B 281, 277–294.

Monteith, J.L., 1995a. A reinterpretation of stomatal responses to humidity. Plant Cell Environ. 18, 357–364.

Monteith, J.L., 1995b. Accommodation between transpiring vegetation and the convective boundary layer. J. Hydrol. 166, 251–263.

Moran, M.S., Jackson, R.D., Raymond, L.H., Gay, L.W., Slater, P.N., 1989. Mapping surface energy balance components by combining Landsat thematic mapper and ground-based meteorological data. Remote Sens. Environ. 30, 77–87.

Mott, K.A., Parkhurst, D.F., 1991. Stomatal responses to humidity in air and helox. Plant Cell Environ. 14, 509–515.

Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12, 513–522.

Nichols, W.D., 1992. Energy budgets and resistances to energy transport in sparsely vegetated rangeland. Agric. For. Meteor.

60, 221–247.

Njoku, E.G., Patel, I.R., 1986. Observations of seasonal variability of soil moisture and vegetation cover over Africa using

230

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

satellite microwave radiometry, ISLSCP. In: Proceedings of an International Conference, Rome, Italy. ESA, SP-248, Paris, pp. 349–356.

Noilhan, J., Planton, S., 1989. A simple parameterization of land-surface processes for meteorological models. Mon. Wea. Rev. 117, 536–549.

Noilhan, J., et al., 1991. Some aspects of the HAPEX-MOBILHY programme. In: Wood, E.F. (Ed.), Land Surface/Atmosphere Interactions for Climate Modeling, Kluwer Academic Publishers, Dordrecht, pp. 31–62.

Ottlé, C., Vidal-Madjar, D., Cognard, A.L., Loumagne, C., Normand, M., 1996. Radar and optical remote sensing to infer evapotranspiration and soil moisture. In: Steward, et al.

(Eds.), Scaling up in Hydrology Using Remote Sensing. Wiley, Chicester.

Paulson, C.A., 1970. The mathematical representation of windspeed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteorol. 9, 857–861.

Petersen, C.T., Jørgensen, U., Svendsen, H., Hansen, S., Jensen, H.E., Nielsen, N.E., 1995. Parameter assessment for simulation of biomass production and nitrogen uptake in winter rape. Eur. J. Agron. 4, 77–89.

Plantedirektoratet, 1997/1998. Vejledning og skemaer. Ministeriet for Fødevarer, Landbrug og Fiskeri (in Danish).

Prescott, J.A., 1940. Evaporation from a water surface in relation to solar radiation. Trans. Roy. Soc. Australia 64, 114–125.

Rosenberg, N.J., Blad, B.L., Verma, S.B., 1983. Microclimate: The Biological Environment, 2nd Edition, Wiley, New York, 1983, 495 pp.

Schelde, K., Thomsen, A., Heidmann, T., Schjønning, P., Jansson, P.B.E., 1998. Diurnal fluctuations of water and heat flows in a bare soil. Water Resour. Res. 34 (11), 2919–2929.

Schjønning, P., 1992. Characterization of the area surrounding Research Centre Foulum (summary in English). Beretning nr.

S 2229, 1992, landbrugsministeriet, Statens Planteavlsforsøg.

Sellers, P.J., 1985. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 6., 1335–1372.

Sellers, P.J., 1987. Canopy reflectance, photosynthesis, and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sens. Environ. 21, 143–183.

Sellers, P.J., 1991. Modeling and observing land–surface– atmosphere interactions on large scales. In: Wood, E.F. (Ed.), Land–Surface–Atmosphere Interactions for Climate Modeling: Observations, Models and Analysis. Kluwer Academic Publishers, Dordrecht, 1991.

Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B., Hall, F.G., 1992a. Canopy reflectance, photosynthesis, and transpiration.

III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens. Environ. 42, 187–216.

Sellers, P.J., Heiser, M.D., Hall, F.G., 1992b. Relations between surface conductance and spectral vegetation indices at intermediate (15–100 km2 ) length scales. J. Geophys. Res. 97 (D17), 19033–19057.

Shaw, R.H., Pereira, A.R., 1982. Aerodynamic roughness over a plant canopy: a numerical experiment. Agric. Meteorol. 26, 51–65.

Shuttleworth, W.J., Gurney, R.J., 1990. The theoretical relationship between foliage temperature and canopy resistance in a sparse crop. Q.J.R. Meteorol. Soc. 116, 497–519.

Shuttleworth, W.J., Wallace, J.S., 1985. Evaporation from sparse crops — an energy combination theory. Q.J.R. Meteorol. Soc. 111, 839–855.

Smith, P., Smith, J.U., Powlson, D.S., Arah, J.R.M., et al., 1997. A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments.

Geoderma 81 (1/2) 153–222.

Svendsen, H., Hansen, S., Jensen, H.E., 1995. Simulation of crop production, water and nitrogen balances in two german agro-ecosystems using the DAISY model. Ecol. Model. 81, 197–212.

Tenhunen, J.D., Geyer, R., Valentini, R., Mauser, W., Cernusca, A., 1999. Ecosystem studies, land-use, and resource management. In: Tenhunen, J.D., Kabat, P. (Eds.), Integrating Hydrology, Ecosystem Dynamics, and Biochemistry in Complex Landscapes. Wiley, Chicester.

Thom, A.S., 1975. Momentum, mass and heat exchange of plant communities. In: Monteith, J.L. (Ed.), Vegetation and the Atmosphere: Principles, Vol. I. Academic Press, London, pp. 57–109.

Thomsen, A., 1994. Program AUTOTDR for making automated TDR measurements of soil water content. SP Rep. 38, 17 pp. Danish Institute of Plant and Soil Science, Tjele, 1994 (available at anton.thomsen@agrsci.dk).

Thomsen, A., Thomsen, H., 1994. Automated TDR measurements.

Control box for Tektronix TSS 45 relay scanners. SP rep. 10, 29, 29 pp. Danish Institute of Plant and Soil Science, Tjele, 1994 (available at anton.thomsen@agrsci.dk).

Topp, G.C., Davis, J.L., Annan, A.P., 1980. Electromagnetic determination of soil water content. Measurements in coaxial transmission lines. Water Resour. Res. 3, 574–582.

Tourula, T., Heikinheimo, M., 1998. Modelling evapotranspiration from a barley field over the growing season. Agric. For. Meteor.

91, 237–250.

Tucker, C.J., Holben, B.N., Elgin, J.H., Mcmurtrey, E., 1981.

Remote sensing of total dry matter accumulation in winter wheat. Remote Sens. Environ. 11, 171–190.

Turner, N.C., 1991. Measurements and influence of environmental and plant factors on stomatal conductance in the field. Agric.

For. Meteorol. 54, 137–154.

Verma, S.B., Sellers, P.J., Walthall, C.L., Hall, F.G., Kim, J., Goetz, S.J., 1993. Photosynthesis and stomatal conductance related to reflectance on the canopy scale. Remote Sens. Environ. 44, 103–116.

Wang, J.R., Shiue, J.C., Schmugge, T.J., Engman, E.T., 1989.

L-band radiometric measurements of the FIFE test site in 1987/88. Presented at the AMS symposium on the First ISLSCP Field Experiment (FIFE), 7–9 February, Anaheim, CA, pp. 85–87.

Waring, R.H., Running, S.W., 1999. Remote sensing requirements to drive ecosystem models at the landscape and regional scale. In: Tenhunen, J.D., Kabat, P. (Eds.), Integrating Hydrology, Ecosystem Dynamics, and Biochemistry in Complex Landscapes. Wiley, Chicester.

P. van der Keur et al. / Agricultural and Forest Meteorology 106 (2001) 215–231 Webb, E.K., 1970. Profile relationships: the log-linear range, and extension to strong stability, and extension to strong stability.

Q.J.R. Meteorol. Soc. 117, 67–90.

Wessman, C.A., Cramer, W., Gurney, R.J., Martin, P.H., Mauser, W., Nemani, R., Paruelo, J.M., Penuelas, J., Prince, S.D., Running, S.W., Waring, R.H., 1999. Group report: remote sensing perspectives and insights for study of complex

231

landscapes. In: Tenhunen, J.D., Kabat, P., Wessman, C.A. (Eds.), Integrating Hydrology, Ecosystem Dynamics, and Biochemistry in Complex Landscapes. Wiley, Chicester. de Willigen, P., 1991. Nitrogen turnover in the soil-crop system comparison of 14 simulation models. Fertilizer Res. 27, 141– 149.