Incorporating remote sensing data in physically based distributed

agro-hydrological modelling

E. Boegha,*, M. Thorsenb, M.B. Buttsb, S. Hansena, J.S. Christiansenb,P. Abrahamsena, C.B. Hasagere, N.O. Jensene, P. van der Keura,1,

J.C. Refsgaardb,1, K. Schelded, H. Soegaardc, A. Thomsend

aLaboratory for Agrohydrology and Bioclimatology, The Royal Veterinary and Agricultural University, Copenhagen, DenmarkbDHI Water and Environment, Hørsholm, Denmark

cInstitute of Geography, University of Copenhagen, Copenhagen, DenmarkdDepartment of Crop Physiology and Soil Science, Danish Institute of Agricultural Sciences, Tjele, Denmark

eRisø National Laboratory, Roskilde, Denmark

Received 23 August 2002; revised 30 September 2003; accepted 29 October 2003

Abstract

Distributed information on land use and vegetation parameters is important for the correct predictions of evapotranspiration

rate and soil water balance while, in turn, the growth and function of vegetation are also highly dependent on the soil water

availability. In this study, the relationship between the soil water balance and the vegetation growth is represented by coupling a

hydrological model (MIKE SHE) and a vegetation-SVAT model (Daisy) which simulates the interactions between soil,

vegetation and atmosphere including the seasonal variation in plant structure and function. Because the coupling of process

models is accompanied by increasing difficulties in obtaining values for the numerous parameters required, the utility of

satellite data to set up, verify and update such a model system is the focus of the present paper.

To achieve spatially distributed information on surface conditions, field data of leaf area index (L) and eddy covariance fluxes

were collected, and high-resolution remote sensing (RS) data were acquired to produce maps of land cover, leaf area index and

evapotranspiration rates (E). The land cover map is used to set up the model which is run throughout 1998 for a Danish

agricultural area with a time step of 1 h. In May, the spatial heterogeneity of the leaf area index is at its largest, and the model

performance is evaluated in time and space using the field measurements and the RS-based maps of L and E: Finally, the effect

of adjusting the simulated L to match the RS-based L is investigated. The adjustment strategy includes synchronization of all

vegetation parameters to maintain congruity of the model canopy representation. While the predicted crop yields were

improved, a large micro-scale spatial heterogeneity in L within the operational modelling units restricted improvements in the

simulated E: The delineation of modelling units that are homogeneous with respect to the assimilated variable, L; requires

separation of land use classes with respect to the temporal development in vegetation cover.

q 2004 Elsevier B.V. All rights reserved.

Keywords: Distributed model; Remote sensing; Evapotranspiration; Validation; Data assimilation

0022-1694/$ - see front matter q 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.jhydrol.2003.10.018

Journal of Hydrology 287 (2004) 279–299

www.elsevier.com/locate/jhydrol

1 Present address: Geological Survey of Denmark and Greenland, Copenhagen K, Denmark.

* Corresponding author. Present address: Institute of Geography, University of Copenhagen, Oester Voldgade 10, 1350 Copenhagen,

Denmark. Tel.: þ45-3532-2584; fax: þ45-3532-2501.

E-mail address: evb@geogr.ku.dk (E. Boegh).

1. Introduction

Because of the difficulties in obtaining values for

the parameters required in spatially distributed

modelling, the integration of remote sensing (RS)

data and hydrological, soil–vegetation–atmosphere

transfer (SVAT) or crop production modelling has

been a topic subjected to large expectations during the

last decades. Agriculture already has a long successful

experience record with the use of RS-data for

production modelling (see review in Moulin et al.,

1998), and in recent years much focus has been on the

assimilation of RS-data in SVAT models (Sellers

et al., 1996; Gillies et al., 1997; Olioso et al., 1999;

Cayrol et al., 2000), ecosystem models (White et al.,

1998; Nouvellon et al., 2001) and hydrological

models (Ottle and Vidal-Madjar, 1994; Kite and

Pietroniro, 1996; Houser et al., 1998; Su, 2000; Biftu

and Gan, 2001; Loumagne et al., 2001; Pauwels et al.,

2001). While the first applications of satellite data in

spatially distributed modelling were restricted to a

descriptive analysis of land use to advance the

assignment of model parameters, more complex

methodologies are now available which facilitate a

quantitative analysis of the satellite signal. Spectral

reflectances provide the basis for estimating global

albedoes, and vegetation indices can be calculated to

assess bio-physical parameters such as the leaf area

index and the minimum canopy resistance to evapor-

ation (Sellers et al., 1992). The surface temperature

can be achieved from various satellite sensors to

improve the simulation of energy balance components

(Ottle and Vidal-Madjar, 1994; Gillies et al., 1997),

and the surface soil moisture content can be derived

from microwave data (Schmugge, 1998) to improve

the process modelling of bare soil (Bruckler and

Witono, 1989), sparsely vegetated surfaces (Houser

et al., 1998) and selected land cover types (Loumagne

et al., 2001) when assimilated in hydrological models.

RS of soil moisture is, however, still not operational

because no consistent methods are yet available to

separate the signal contributions from soil and

vegetation.

Different strategies have been suggested to benefit

from Earth observations in distributed modelling. RS-

data can be used to assess the appropriate spatial scale

of distributed models (Wood, 1995; Artan et al.,

2000); they can be used to compare and evaluate

model performance (Artan et al., 2000; Kite and

Droogers, 2000; Biftu and Gan, 2001; Sandholt et al.,

2002), or they can be assimilated directly with the

purpose of initializing, driving, updating or re-

calibrating models. If models and data were perfect,

RS-based initialization of models would be sufficient

to provide spatially distributed information on surface

characteristics. More realistically, the models may

also be driven by sequential assimilation of RS-based

surface variables, or the RS-data can be used to adjust

model variables to keep the model on track. Often a

high frequency of cloud cover reduces the availability

of RS-data in which case the adjustment strategy is

preferable. While the adjustment strategy assumes

that the model structure is correct, the application of

RS-data for re-calibrating models modifies the

governing equations by tuning some of the (constant)

model parameters or by adding corrective terms. The

combined use of adjustment and re-calibration

strategies has also been suggested (Shuttleworth,

1998). For a review and discussion on different

assimilation strategies, see Fischer et al. (1997),

Moulin et al. (1998) and Shuttleworth (1998).

Even though the feasibility of using RS-data in

agricultural and hydrological modelling has been

demonstrated in several studies, such results were

often obtained using field measurements or satellite

acquisitions representing a single land surface type

such as bare soil (Bruckler and Witono, 1989),

grassland (Cayrol et al., 2000; Nouvellon et al.,

2001), forest (White et al., 1998; Ranson et al., 2001),

maize (Maas, 1988), sugar beet (Bouman, 1992;

Clevers and van Leeuwen, 1996; Guerif and Duke,

2000), wheat (Weiss et al., 2001), or the spatial model

outputs were evaluated at catchment scale by

comparison with averaged RS-based soil moisture

(Biftu and Gan, 2001) or measurements of streamflow

at the basin outlet (Ottle and Vidal-Madjar, 1994;

Loumagne et al., 2001; Pauwels et al., 2001). The

spatial variation of distributed model outputs is rarely

validated. Furthermore, in most satellite based

studies, low resolution (1 km2) RS-data were applied

because of their high temporal availability (daily). For

the application of such data in heterogeneous terrain,

the scaling characteristics of the RS-data (Moran et al.,

1997) and the effect of sub-grid variations on the

modelling of land surface processes (Sellers et al.,

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299280

1997; Hasager and Jensen, 1999) needs to be

considered. The uncertainty associated with sub-

scale processes is also well known in hydrology

(Refsgaard, 1996) where the modelling is usually

carried out at larger grid scales using fitted model

parameters. In contrast, crop modelling is usually

applied at scales allowing the monitoring of individ-

ual (homogeneous) fields.

In the present study, an agricultural Vegetation-

SVAT model (Daisy) and a physically based dis-

tributed hydrological model (MIKE SHE) is coupled

and run at field scale resolution using physical

parameters. The coupling of the models at this scale

features the identification of homogeneous modelling

units which allows objective selections of parameter

values. Such a model setup is of particular interest for

studies of the linkage between agricultural manage-

ment and water resources, i.e. the effect of fertilizer

application on ground water contamination could be

explored, provided a proper validation of the dis-

tributed outputs can be obtained. In this study, the

feasibility of high-resolution RS-data to set up,

validate and update an agro-hydrological model is

investigated. The model is set up using a RS-based

land cover map of an agricultural landscape in

Denmark which is composited by a mosaic pattern

of small mixed fields. When the spatial heterogeneity

of the leaf area index (L) is at its largest, the model

performance is evaluated using field measurements

and RS-based maps of L and evapotranspiration rates.

Finally, the effect of adjusting the simulated L to

match the RS-based L is investigated with respect to

the impact on the predicted crop yields and evapo-

transpiration rates. Whereas the present paper focuses

on the evaluation of spatially distributed simulations

and assimilation effects, the temporal model perform-

ance is evaluated by Butts et al. (2003) using soil

moisture measurements and field and landscape scale

eddy covariance fluxes of H2O and CO2.

2. Data description and processing

2.1. Field data

Standard meteorological data used for driving

MIKE SHE/Daisy were recorded continuously at the

climate station of Foulum Research Centre which is

located in an agricultural region of Denmark

(9.4238E, 56.4868N) The measurements were

recorded in 1998 and comprise precipitation, global

radiation (CM-7, Kipp & Zonen, NL), air tempera-

ture, vapour pressure and wind speed. The year 1998

was characterized by excessive rain with a total

precipitation of 860 mm corresponding to 24% above

the mean annual precipitation (1961–1990).

Measurements of atmospheric fluxes of water

vapour above the canopy were measured using the

eddy covariance technique at four fields comprising

winter wheat, grasses, maize and spring barley. Flux

measurements were initiated in the beginning/middle

of April and lasted until mid/end of August 1998

corresponding to the duration of the growing season.

Except for the setup at the spring barley field, all

masts were equipped with a three-dimensional sonic

anemometer (Gill Solent, UK), and an infrared gas

analyzer was used for measuring water vapour

concentrations (LI-COR 6262, LI-COR Inc., NE,

USA). At the spring barley field, a one-dimensional

anemometer (METER USA-1) measured wind speed

fluctuations and water vapour concentrations were

recorded using an optical hygrometer (OPHIR IR-

2000).

Information on management practice and weekly

measurements of the green leaf area indices (L) were

collected at eight fields throughout the experiment.

The management information includes times for

sowing and harvest, fertilization rates and soil

management for winter barley, winter wheat, spring

barley, spring barley/grass, grass for cutting, peas,

beets and maize. For the assessment of the L of these

fields, both the LICOR LAI-2000 instrument and

destructive measurements were used. The LAI-2000

instrument uses multiple-view measurements of

transmitted diffuse light in the canopy to compute

the L: With respect to the destructive measurements of

L and biomass, all plant material from two areas of

2500 cm2 (15,000 cm2 for maize field) were cut

within each field. The total samples were sorted and

weighted in laboratory, and sub-samples were used as

a basis for measuring the green leaf area (LICOR

3100 Planometer). The derived relationship between

sub-sample fresh weight and green leaf area were then

applied to the total samples to estimate L: The

assessment of the dry matter content was based on

the drying of sub-samples at 80 8C during 16 h.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 281

Maps of topography (1:25,000) and soil types

(1:50,000) were used as a basis for collecting

representative soil samples to determine hydraulic

parameters. The topography ranges from zero to more

than 60 m above sea level, and there are five major

soil types in the model region (Fig. 1). Soil texture

data were obtained for the major soil types, and the

Cosby pedo-transfer function (Cosby et al., 1984) was

used to derive the saturated hydraulic conductivity

and parameters to construct soil water retention

curves (Campbell, 1974) for all horizons (Table 1).

The use of pedo-transfer functions was found superior

to the application of measured retention data (Butts

et al., 2003) which are susceptible to micro-scale

variations of soil hydraulic properties. Hydraulic

conductivity curves were produced using the Camp-

bell/Burdine function (Burdine, 1952; Campell,

1974).

2.2. Satellite data availability and pre-processing

High-resolution satellite data were acquired to

facilitate the integration of RS-data and distributed

modelling. For this purpose Landsat TM scenes were

preferred because data are provided both in the

visible, near-infrared and thermal infrared spectra at

a spatial resolution of 30 m (120 m for the thermal

infrared channel). Due to unfavourable cloudiness

during the Danish summer of 1998, only one Landsat

TM scene was available that year (18-May). Two

additional SPOT scenes were then acquired to provide

temporal information on the vegetation cover (21-Jun,

11-Aug). The SPOT satellite provides visible and

near-infrared reflectance data at a resolution of 20 m.

All satellite scenes were co-registered and calibrated,

and atmospheric correction was performed using

radiosonde data in the radiative transfer model

MODTRAN-3, as detailed in Boegh et al. (2002).

The surface temperature was calculated using the

method described by Goetz et al. (1995). Surface

reflectances provide the basis for calculating the

Normalized Difference Vegetation Index, NDVI ¼

ðrNIR 2 rREDÞ=ðrNIR þ rREDÞ; where rNIR is the near-

infrared reflectance and rRED is the red reflectance.

2.3. Land cover map and vegetation parameters

Within the study, farmers were interviewed to

provide information on the type of crops cultivated at

107 fields This information was stored in a Geo-

graphical Information System (GIS). In order to

extend the GIS map of land use to represent the

larger model area, ‘training’ (reference) areas repre-

senting 19 different surface types were selected for

use as inputs to a RS-based supervised land use

classification. Because the model setup area extends

down through a river valley, an orthophoto was used

to locate training regions representing natural areas,

such as meadow (grazing area) and moor. The

numerical separability between training areas was

Fig. 1. Topography (meters above sea level), soil types and land use types in the model area. Descriptions of the five soil types are given in Table

1. The land use map is composited of a GIS based map (19% of model area) and a remote sensing (RS) based land surface classification (81% of

model area). An orthophoto provides the background for the RS/GIS land use map.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299282

examined by calculating the Jeffries–Matusita (JM)

distance. The JM distance can obtain values between

0 and 1.414. When the JM distance is zero, there is a

complete overlap between training areas (similar

surface types) while a JM distance of 1.414 symbo-

lizes perfect separability (no overlap between

classes). Combining the spatio-temporal information

on spectral reflectance and NDVI (representing

vegetation development) computed from the three

available satellite scenes in 1998, near-perfect

spectral separability was obtained in the present case

between training regions (mean JM

distance ¼ 1.408). The surface classification was

performed using the minimum-distance algorithm.

In order to suppress the effect of mixed pixels in the

classified map, the results were subsequently filtered

using a median filter in a 3 £ 3 running window.

For the assessment of the accuracy of the classified

map, the confusion matrix (Aronoff, 1982) was

computed. Excluding the 19 training regions, the

remaining ground-thruth information was used to

represent validation areas in which case an overall

classification accuracy of 65% was found. The

confusion of grazing areas and fields where grasses

are grown for cutting is partly responsible for the low

classification accuracy, and the discrimination

between the different types of spring barley fields is

also troublesome. Differences in farmers decision on

the time of sowing (and cutting) of crops is an

important factor that introduces heterogeneity

between fields which is not represented by the training

areas. Generally, winter-sown crops, spring-sown

crops and later sown crops (beets and maize, which

are sown in May) make up well-separated groups. In

order to reduce the effect of classification errors in

MIKE SHE/Daisy results, the GIS map was super-

imposed upon the RS-based land use map (Fig. 1).

The GIS land use map covers 19% of the model setup

area. The remaining part of the model setup area is

represented by the RS-based land use classification.

2.4. Remote sensing based leaf area index

RS-based vegetation indices, such as the NDVI,

are linearly related to the fractional vegetation cover

and exponentially related to the green leaf area index

(Sellers, 1989). In this study, the coefficients of an

exponential function relating field data of L (destruc-

tive measurements) and satellite observations of

NDVI were established on the basis of Landsat TM

and SPOT data. Because of the different sensor

characteristics of these two satellite systems, slightly

different relationships are expected. Leaf area index

maps were produced using the derived relationships

for Landsat TM (Fig. 2a)

L ¼ 0:0051 e7:947 NDVI ðr2 ¼ 0:99Þ ð1Þ

For SPOT, the following relationship was used

(Fig. 2b)

L ¼ 0:0122 e7:675 NDVI ðr2 ¼ 0:83Þ ð2Þ

2.5. Remote sensing based evapotranspiration rate

The evapotranspiration rate (E) is calculated on the

basis of RS-based estimates of surface temperature,

Table 1

Saturated hydraulic conductivity (Ksat; in units of cm h21) and saturated water content (us; in units of m3 m23) for all horizons of five different

soil types used in the model setup

Coarse sand above

moraine (I)

Moraine sand (II) Peat above sand and clay

(III)

Moderate clay moraine

(IV)

Fine sand above

moderate clay moraine

(V)

Z Ksat us Z Ksat us Z Ksat us Z Ksat us Z Ksat us

0.3 5.98 0.39 0.3 4.37 0.41 0.4 0.33 0.77 0.3 4.73 0.41 0.3 4.37 0.41

0.7 7.90 0.38 0.6 6.2 0.39 x 10.2 0.37 1.0 2.77 0.43 1.0 2.77 0.43

x 5.76 0.39 x 5.08 0.40 2.0 8.72 0.38 2.0 8.72 0.38

x 2.87 0.43 x 2.87 0.43

Z denotes the lower boundary of horizons (m). The deepest horizon ends at the depth of the ground water table (x).

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 283

net radiation and soil heat flux using a method

whereby three equations are used to solve for three

variables (Boegh et al., 2002); the atmospheric

resistance between the surface and the air ðraeÞ; the

surface resistance ðrsÞ and the vapour pressure at the

surface ðesÞ

rae ¼ rcp

ðTs 2 TaÞ þ ðes 2 eaÞ=g

Rn 2 G½s m21� ð3Þ

rs ¼ raeðeps 2 esÞ=ðes 2 eaÞ ½s m21� ð4Þ

es ¼ 0:9Veps þ ð1 2VÞea ½Pa� ð5Þ

where rcp is the heat capacity (J m23 K21), g is the

psychrometer constant (Pa K21), Ts is surface tem-

perature, Ta is air temperature, eps is the saturated

vapour pressure at the surface (Pa) which is calculated

from the surface temperature, ea is the vapour pressure

in the canopy (Pa), and V is the surface–atmosphere

decoupling coefficient, V ¼ ðD=gþ 1Þ=ðD=gþ 1 þ

rs=raeÞ (Jarvis and McNaughton, 1986), which is

used as a weighting factor to place es between its

two limit values, eps and ea:

The derived estimates of rae and rs are then used to

calculate the evapotranspiration rate

lE ¼ ðrcp=gÞeps 2 ea

rs þ rae

½W m22� ð6Þ

where l is the latent heat of vaporization (J kg21).

The method was found useful for simulating evapo-

transpiration rates in Denmark using half-hourly

inputs of surface temperature, net radiation, air

temperature and air humidity recorded at the exper-

imental wheat field in a 100 day period during which L

ranged between 0 and 4, and it was also successfully

applied to satellite data (Boegh et al., 2002). One

significant drawback of the method is that when the

temperature of the ‘evaporating front’ is not rep-

resented by the measured surface temperature (e.g. a

dry canopy or a dry soil), it is necessary to adjust a

parameter to estimate the relative humidity at the

surface. The method is detailed in Boegh et al. (2002).

It was applied to the Landsat TM scene covering the

model area on 18-May 1998 at 11 local hour. At this

time, the RS-based estimates of E range from 0.09 to

0.33 mm h21, and they were found to be linearly

related to field measurements of evapotranspiration

fluxes (r2 ¼ 0:83; slope ¼ 1.07; intercept ¼ 0)

(Boegh et al., 2002).

3. Methodology

3.1. Model overview

While MIKE SHE is a deterministic, distributed

and physically based modelling system applicable for

the simulation of water, solutes and sediments in the

entire land phase of the hydrological cycle (Refsgaard

and Storm, 1995), Daisy is an advanced modelling

system for representing SVAT interactions at the land

surface (Hansen et al., 1991; Abrahamsen and

Hansen, 2001; van der Keur et al., 2001) which also

supports the linkage with other models systems, such

as MIKE SHE. Within RS-Model, MIKE SHE and

Daisy are coupled for the simulation of crop

Fig. 2. Relationships derived between field data of leaf area index

(L) and the Normalized Difference Vegetation Index (NDVI)

computed from Landsat TM (a) and SPOT (b).

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299284

production and water balance. The agricultural

practices, plant growth, evapotranspiration processes,

nitrogen transformations and organic matter turnover

are quantified in Daisy while the soil water content

and water movement including flow in the unsaturated

zone, groundwater, stream, and at the ground surface,

along with nutrient transport are quantified in MIKE

SHE. Both Daisy and MIKE SHE will reversibly react

upon changes caused in the other model, e.g. Daisy

crops will not grow in areas where MIKE SHE

simulates flooding.

Daisy uses a one-dimensional detailed description

of atmosphere, soil and vegetation canopy processes.

With respect to the modelling of evapotranspiration

rates, energy is partitioned between soil and veg-

etation in combination with a two-source atmospheric

resistance network (van der Keur et al., 2001). The

soil evaporation rate ðEsÞ is computed as exfiltration

(Richards equation) towards the soil surface driven by

the potential soil evaporation and restricted by the

hydraulic properties of the upper soil layer. It is

assumed that water vapour released at the soil surface

is transported without lateral loss to the mean canopy

air stream, whereas the calculation of the sensible heat

flux between the soil surface and the mean canopy air

stream is based on atmospheric resistance formulation

(Choudhury and Monteith, 1988) and soil energy

balance fulfilment. Transpiration rates ðEvÞ are

calculated by

lEv ¼ ðrcp=gÞepl 2 e0

rac þ rst

½W m22� ð7Þ

where epl is the saturated vapour pressure at leaf

temperature (Pa), e0 is the vapour pressure in the

canopy (Pa), rst is the bulk stomatal resistance (s m21)

and rac is the aerodynamic resistance between the

leaves and the air in the canopy (s m21). For the

calculation of epl ; leaf temperature is estimated by

solving the leaf energy balance. The rst in Eq. (7) is

modelled using a constraint function (F) to modify the

minimum stomatal resistance ðrminst Þ

rst ¼rmin

st

LF21 ½s m21� ð8Þ

where rminst ¼ 30 s m21: The leaf area index (L) is used

to upscale the leaf stomatal model to canopy level rst:

F is a soil water stress constraint function given by

FðuÞ ¼

Ei þðZr

0SðzÞdz

Ei;p þðZr

0SpðzÞdz

ð9Þ

where S is the water uptake by root (m3 m23 s21), Sp is

the water demand of the root corresponding to

potential transpiration (m3 m23 s21), z is the depth of

soil (m), Ei is the evaporation of intercepted water

(m s21), and Ei;p is the potential evaporation of

intercepted water. The soil water uptake by roots is

calculated using the single root concept in each of the

numeric layers providing solutions for Richard’s

equation. The single root concept assumes water to

move radially towards the root surface (Darcy

equation) where it is taken up at a rate determined by

the conditions in the soil and the conditions at the root

surface, i.e. the water uptake by plant roots is

calculated by

S ¼ luðhrÞ

us

Mh 2 Mhr

2 0:5 lnðr2r plÞ

½m3 m23 s21� ð10Þ

where l is root density (m m23), u is volumetric soil

water content (m3 m23), us is volumetric soil water

content at saturation (m3 m23), M is matrix flux

potential (m2 s21) which is a function of the pressure

potential, h is soil water pressure potential of the bulk

soil (m), hr is the soil water pressure at the root surface

(m), and rr is root radius (m). The calculations are

detailed in Hansen et al. (1991).

Considering vegetation growth and the prediction

of L; the Daisy code comprises a number of different

growth models. The one invoked in this study is a

further development of a generic model proposed by

Penning de Vries et al. (1989). The model simulates

vegetation growth and L development by incorporat-

ing the processes of photosynthesis and respiration,

and the produced assimilates are partitioned between

the different plant compartments. The leaf area index

is calculated by

L ¼ SlaWla ð11Þ

where the specific leaf area index (Sla; in units of

m2 kg21) is a function of the development stage (DS),

and the leaf weight (Wla; in units of kg m s22) is

determined by the net production rate. Schematically,

the net production rate is partitioned to the different

plant compartments (leaves, stems, etc.) as a function

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 285

of DS which is controlled by temperature and day

length (Penning de Vries et al., 1989). The net pro-

duction rate is computed using a multi-layer photo-

synthesis model depending on light absorption and

crop specific initial light use efficiencies and maximum

photosynthetic rates. The photosynthetic rate is

restricted by multiplication by a water stress factor

which equals the ratio of actual and potential evapo-

transpiration rate from the leaves (Eq. (9); Hansen

et al., 1991). Nitrogen uptake is simulated, and nitrogen

stress is accounted for using a nitrogen stress index

related to the difference between actual plant nitrogen

content and a crop specific critical value which is also

dependent on DS. When a mixture of crops is present

at the field, Daisy considers leaf areas of the individual

crops for the computation of leaf area index.

3.2. Model setup

The extension of the MIKE SHE/Daisy model

region was chosen to include the most typical footprint

areas of an integrating tower flux mast which is

located in the North-Eastern part of the model area

(Fig. 1). The application of data from the tall mast for

evaluating model performance is described by Butts

et al. (2003). The lower boundary of the unsaturated

zone is represented by a groundwater model with

drainage and continuously calculated temporal press-

ure level. The horizontal borders of the model area are

mostly hydrologically closed. It includes a stream at

the lowest level (Southern border) whereas at the

higher level (Northern border), the position of the

groundwater was extracted from simulations using a

full catchment MIKE SHE setup which was calibrated

on the basis of observed ground water levels from 20

bore holes (Joergensen, 1997). The upper boundary

conditions of the unsaturated zone allows for surface

run-off. Land surface heterogeneity is described in

terms of topography, soil type and vegetation type.

Information on the management practices for the eight

experimental fields is assumed to represent the

cultivation strategies in the area. For the remaining

crop types, default management practices in Daisy are

used. Because Daisy contains no standard parameter-

izations for meadows/grazing areas, forest/trees and

areas set aside, these surface types were simulated like

grasses subjected to different management techniques.

Buildings were simulated like bare soil.

The model is run with a grid size of 40 m from 31st

August 1997 to 31st December 1998 with a time

resolution of 1 h. In order to keep computational

speed at a reasonable level, MIKE SHE was enabled

to identify and classify grids having similar properties

in terms of depth to the ground water, soil type and

vegetation type. For the present study, this implies

that simulations are done deterministically in 220

vertical columns (rather than 6350 grids), each

representing a homogeneous agro-hydrological

group response unit (GRU), and then transferred to

profiles having the same characteristics.

3.3. Assimilation strategy

The assimilation of Earth Observations in MIKE

SHE/Daisy is performed using an ‘adjustment strategy’

whereby the modelled L is adjusted to match the RS-

based L: In order to associate the RS-based L and the

modelled L; the RS-based estimates of L are averaged

within each of the 220 agro-hydrological GRU’s.

The adjustment of the modelled L is accomplished

by producing and updating ‘checkpoints’ created by

MIKE SHE/Daisy at the day and time for satellite

imaging. The checkpoint (CP) is a simple text-file

constituting information on the status of soil and

vegetation at a given time for all 220 agro-

hydrological units. The CP file is used to hotstart the

model after its proper adjustment.

In order to ensure that the adjustment of L in MIKE

SHE/Daisy balances other simulated vegetation

parameters such as the root development, plant height,

DS, etc. the Daisy model was used to simulate the

attributes of a given canopy characterized by the RS-

based L: In summary, the following steps are

conducted in order to construct the hotstart files

containing RS-based estimates of L:

1. MIKE SHE/Daisy is executed and a checkpoint file

(CPw) is produced on the day and time for satellite

imaging.

2. The average RS-based L is determined for all 220

agro-hydrological GRU’s.

3. Daisy is executed and another checkpoint file

(CPv) is produced when the modelled L matches

the RS-based L:

4. The two sets of CP files are combined to produce a

hotstart file. The water/temperature status is taken

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299286

from CPw and the vegetation status is taken from

CPv.

5. MIKE SHE/Daisy is hotstarted.

By performing these steps, the entire vegetation

canopy is updated. Illogical combinations of L and

other vegetation parameters are avoided, and the

updating of the DS (Section 3.2) also indirectly

corrects for erroneous setup of sowing time which is

very important for the proper modelling of L: When

MIKE SHE/Daisy is updated at the subsequent

satellite passages, only the L is adjusted.

4. Results

4.1. Time series: comparison based on field data

Time series of L and E were extracted for the four

flux-recording sites which are located in the model

area. With respect to the modelled leaf area indices,

the results in the initial and vegetative growth phases

are generally in good agreement with the destructive

measurements of L (Fig. 3). Even though the influence

of the wintering conditions is very difficult to predict,

the rising limb of the L curve for the winter-sown

wheat field is well predicted whereas for grass, the

simulated L starts increasing a bit too early in spring.

The peak L is also well predicted for wheat, but it

remains too low for grass and maize. For the mixed

spring barley/grass field, the predicted peak L appears

too early. In the senescent phase, the modelled L

values exceed all destructive L measurements, but in

this period, the presence of wilted and damaged leaves

makes the correct measurement of green L very

difficult. In fact, measurements of CO2 fluxes

(Soegaard et al., 2003) certify that the vegetation

remains photosynthetically active in periods when the

destructive L measurements of senescent vegetation

are zero. This implies that the green L determined by

destructive measurements are not appropriate estima-

tors for calculating vegetation fluxes in this period. In

Fig. 3. The modelled leaf area index (L) (full line) is compared with field data of L; determined using both destructive sampling (filled circle) and

the LAI-2000 instrument (thick line). The RS-based leaf area indices are represented by filled squares (Landsat TM) and triangles (SPOT HRV),

and the dotted lines illustrate the L simulations after remote sensing based adjustment of the simulated L:

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 287

Fig. 3 is also shown the L measured by the LAI-2000

instrument. In the vegetative phase, the two different

methodologies for measuring the L agree, but after the

peak L is reached, the LAI-2000 overestimates L

because the sensor recordings are influenced by the

presence of stems and ears in the canopy. The real L is

expected to be somewhere between the destructive

measurements and the LAI-2000 estimates. The effect

of updating the modelled L based on RS-data is

discussed in Section 4.3.

With respect to the modelling of evapotranspira-

tion rates, the simulations are compared with eddy

covariance measurements during a drying sequence

in the period 11–21 May (Fig. 4). At 11-May, 8 mm

of rain caused the soils to reach a water content near

field capacity (pF < 2) and, apart from a short spell

on 12 May (0.3 mm), there were no rainfall

occurrences until the 21-May. In this period, the

modelled L was adequately predicted for all

experimental sites (Fig. 3). The simulated E

approaches the field measurements of both dense

vegetation (Fig. 4a and b) and bare soil (Fig. 4d) but

for sparse vegetation (Fig. 4c), the E is overestimated

in the driest period (14–20 May). Water stress is

simulated for the spring barley/grass field at 18-May

due to its shallow root depth at this time. Following

one week of soil drying, the modelled E also

overestimates the E recorded for the densely

vegetated fields. The lower E measurements insin-

uate the presence of soil water stress which is not

caught by the model. However, field measurements

of soil water contents in the root zone of the fully

developed canopies indicate plentiful soil water

availability, e.g. for the 18-May, the soil water

content at the wheat field is 24% (Boegh et al.,

2002).

4.2. Spatially distributed results: comparison

with RS-based maps

Generally, the maps derived from the RS-data are

more heterogeneous than the results modelled by

MIKE SHE/Daisy (Fig. 5). The within-field hetero-

geneity in the RS-derived L is caused by (sub-grid)

buildings, trees, windbreaks, biotypes, non-uniform

fertilization rates and variations in soil textural and

hydraulic parameters. With respect to the RS-based E;

the different spatial resolutions of the thermal infrared

channel (120 m) and the shortwave channels (30 m)

were also found to introduce scatter in the results

(Boegh et al., 2002). In this case, the surface temp-

erature may represent a mixture of bare soil grids and

vegetated grids while the shortwave channels (used to

assess soil heat flux and global albedo; Boegh et al.,

2002) represent one or the other of these situations.

This situation is typical along field edges and

locations with large micro-scale (,120 m) contrasts

in L:

Fig. 6a facilitates a more detailed comparison

between the modelled and remotely sensed estimates

of L along four horizontal transects, and Fig. 6b

compares the corresponding evapotranspiration rates

(see location of transects in Fig. 5). While the profiles

1 and 2 are located within the GIS-mapped land use

area which is assumed to be very accurate in terms of

its land use characterization, the profiles 3 and 4 are

located within the remotely sensed land use mapped

area which is less accurately described with respect to

land use.

Profile 1 is located across three experimental fields

for which the L was already found to be adequately

predicted in May (Fig. 3). Because differences in L of

dense canopies (e.g. when L . 3) do not influence

evapotranspiration rates much, the agreement

between the modelled and RS-based E along Profile

1 (Fig. 6b) is better than for the leaf area index

estimates (Fig. 6a).

Profile 2 reveals adequate results of L within the

GIS-based land use mapped area except for

the beginning of the profile (from the left) where the

predicted L remains lower than the RS-based L: This

could be a result of variations in sowing dates due to

soil trafficability and farmers decision which may

cause large differences in L during canopy establish-

ment (Guerif and Duke, 2000). Profile 3 extends from

the agricultural area and down to the river valley.

Along this transect, the depth to the water table

reduces strongly. The L is adequately prescribed for

the upper (Northern) part of the transect whereas, in

the river valley, errors in the classified land use map

and anomalous sowing times may cause the two

different estimates of L to diverge. Another reason is

the low depth to the ground water table which

occasionally prevents the establishment of vegetation

in the model. Indeed, 1998 was characterized by

excessive rain, and some fields were left uncultivated

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299288

due to water-logging in the river valley. The modelled

extension of areas without vegetation is only slightly

larger than that observed from the satellite (Fig. 5). In

the river valley (Profile 4), the simulated low depth to

the ground water table causes the modelled E to

remain high irrespective of the amount of vegetation

present. This contrasts the RS-based E which is low

when L is low and surface temperature is high.

Fig. 4. Time series of evapotranspiration rates (E) modelled by MIKE SHE/Daisy (full line) are compared to eddy covariance evapotranspiration

fluxes (filled circle) recorded at the four experimental fields.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 289

Fig. 5. Maps of leaf area index (L) and evapotranspiration rates (E) derived from Landsat TM and MIKE SHE/Daisy modelling, respectively. (a)

MIKE SHE/Daisy leaf area index, (b) Landsat TM leaf area index, (c) MIKE SHE/Daisy evapotranspiration rate and (d) Landsat TM

evapotranspiration rate. The situation represent the time of satellite passage; 18-May at 11 local hour. The black box outlines the model area.

The four lines imposed upon the maps illustrate the locations of four transects where results were extracted for comparison (Fig. 6); from the top,

the four profiles are referred to as Profile 1, Profile 2, Profile 3 and Profile 4.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299290

Because of the large differences in the spatial

heterogeneity (and statistical variance) of modelled

and RS-based results, a direct statistical comparison

between these two spatial sets of information is

illegitimate on a per-pixel basis. The same rule

(F-test) rejects the statistical comparison based on

the average L of the GRU’s. Indeed, many similar

vegetation types (having exactly the same L) are

present within the 220 GRU’s, providing a discrete

structure of modelled L values. In contrast, the RS-

observations have a normalized distribution of L:

When the agro-hydrological units are further grouped

Fig. 6. (a) Comparison of RS-based and simulated leaf area index (L) extracted along horizontal transects in the model area (Fig. 5). (b)

Comparison of RS-based and simulated evapotranspiration rates (E) extracted along horizontal transects in the model area (Fig. 5). RS-based

results are given by thick lines, and the MIKE SHE/Daisy results are seen as thin lines.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 291

into classes of different surface types, the MIKE

SHE/Daisy simulations and the RS-based results are

linearly correlated when only agricultural fields are

considered (Fig. 7). The average simulated and

RS-based estimates of L for the agricultural classes

(Table 2) are statistical similar with respect to their

variance (at 1% significance level) and their mean

values (at 5% significance level). The simulated

average E of the same classes are, however,

significantly higher than those which are calculated

directly from the RS-data (Table 2). Only for one land

use type (spring barley) is the modelled average E

lower than the RS-based E (Fig. 7). The low simulated

E of this crop is caused by a very low predicted L (0.1)

whereas the RS-based average L (1.6) of spring barley

is higher.

The apparent consistency of the relationship

between L and E; predicted by MIKE SHE/Daisy and

from Landsat TM data, is further explored in Fig. 8

where all grid-point representations of these variables

have been plotted against each other. The L–E

relationships based on the RS-observations (Fig. 8a)

and the MIKE SHE/Daisy simulations (Fig. 8b) reveal

similarity by predicting high E of dense vegetation,

and, for bare soils, E typically ranges between 0 and

0.4 mm h21. The large variation in the simulated E of

bare soils signifies the importance of distributed soil

types and the variable depth to the ground water table.

For surfaces characterized by intermediate values of L;

there is a large variation in the RS-based estimates of E

whereas the response of the simulated E to a rise in L

from 0 to 1–2 is more regular. Overall, both the RS

estimates (Fig. 8a) and the Daisy/MIKE SHE simu-

lations (Fig. 8b) expose a large sensitivity of E to

variations in L:

4.3. Assimilating the RS-based L in MIKE SHE/Daisy

The effect of assimilating the RS-based L estimated

from the Landsat TM scene (18-May) and two SPOT

scenes (21-June and 11-August) is shown in Fig. 3 for

the experimental sites. For wheat, there is virtually no

effect of updating the predicted L because it is at all

times in excellent agreement with the RS-based L: For

grass, the effect is also very small during the first two

satellite passages, but the predicted L is increased

threefold during the last satellite passage which

causes an improvement of the simulated L: For the

mixed spring barley/grass field, the L is down-

adjusted already during the second satellite passage

and again during the third satellite passage. For maize,

the L is slightly down-adjusted during the second

satellite passage, but the effect is quite strong because

Fig. 7. Comparison between simulated and RS-based leaf area

indices (a) and evapotranspiration rates (b) of different agricultural

land use classes. The symbols represent averaged values, and the bi-

directional bars show the standard deviation of simulated and RS-

based results. The regression line (solid) and the 1:1 line (dotted) are

also shown.

Table 2

The average and standard deviation (SD) of simulated and RS-based

leaf area index (L) and evapotranspiration rates (E; mm h21) for the

total model setup area

Simulated L RS-based L Simulated E RS-based E

Average 2.48 2.28 0.29 0.21

SD 2.37 1.29 0.09 0.07

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299292

an increasing proportion of the total assimilate is

being used for stem elongation around this time. This

results in a lower peak value of L which, however, is

adjusted by the final assimilation.

Generally, the substitution of the simulated L by the

RS-observations renders a rather illogical course of

the vegetation development. In order to smooth the L

curves, more frequent acquisitions of satellite data

around the time of maximum L and during senescence

will be advantageous, or nudging techniques could be

applied to push the simulations more gently towards

the RS-observations (Houser et al., 1998; Pauwels

et al., 2001). Alternatively, optimization procedures

could be applied to re-parameterize the crop model

based on the RS-observations (Clevers and van

Leeuwen, 1996; Cayrol et al., 2000; Nouvellon et al.,

2001) but this approach requires much more computer

time. Overall, the effect of assimilating the RS-based L

using the applied technique is a slight improvement in

the simulated yields of the experimental sites (Fig. 9).

Fig. 8. (a) Grid-based comparison of the relationship between leaf area index (L) and evapotranspiration rates (E), based on Landsat TM

calculations. (b) Grid-based comparison of the relationship between L and E; based on MIKE SHE/Daisy simulations. The size of the symbols

illustrate the number of occurrences at a given set of (L;E) values.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 293

With respect to the effect of the RS-based L-

assimilation on the evapotranspiration calculation, the

simulated E is very sensitive to L during dry periods

when soil evaporation is low and transpiration is high

due to the extraction of water from deeper soil layers

by the roots. This situation is represented during the

two periods with available satellite data in May and

June (18-May and 21-June), but the third satellite

image (11-Aug) was recorded in a period of frequent

rainfall and, thus, low sensitivity of E to L (Fig. 10).

Because the modelled leaf area indices were already

well predicted for the experimental sites at 18-May

and 21-June (Fig. 3), or the difference between the

simulated and RS-based L was not important for the

calculation of E; it is not possible to use the E

measurements to test effects of the L-assimilation

(Fig. 10). For instance, the evapotranspiration is not

sensitive to variations in L of dense vegetation (e.g.

when L . 3), and the down-adjustment of L from 5.8

to 3.9 for spring barley/grass on 21-June does

therefore not influence the E calculation (Fig. 10).

In order to assimilate the RS-based L in the river

valley, it was found necessary to lower the ground

water level of water-logged areas to allow the

establishment of vegetation in the model. Even though

this reduced the prediction of E in the valley bottom

(thus approaching the RS-based E), the assimilation of

the RS-based L generally increased both the L

(Fig. 11a) and the E (Fig. 11b). Because of the spatial

heterogeneity of the RS-based L within the GRU’s, its

averaging caused the range and the standard deviation

of the assimilated L to become less than those of the

modelled L (Fig. 11c). As may also be inferred from

Fig. 8, many grids having no or low vegetation cover

obtain larger L values after assimilation while the L of

many dense canopies reduce slightly after the

RS-assimilation. Because the modelled E of sparsely

vegetated surface is extremely sensitive to small rises

in L (Fig. 8b), the E of such surfaces increase strongly

as a result of the RS-assimilation while the E of dense

canopies respond less dramatically to moderate

reductions in L: Accordingly, the overall result of

the RS-assimilation is an increase in E (Fig. 11b) and

a reduction in its standard deviation (Fig. 11d). During

overcast/rainy weather conditions and when soil

moisture availability is plentiful, the two estimates

of E converge (Fig. 11d) because E is less sensitive to

L during such situations. As the modelled L keeps

increasing to represent dense vegetation (e.g. L . 3),

the modelled E also becomes insensitive to the

RS-based adjustment. During June, virtually all fields

are fully vegetated in Denmark (Fig. 3).

The averaging of the RS-based L within the agro-

hydrological GRU’s obviously remove important

spatial heterogeneity which, because of the strong

non-linear relationship between L and E (Fig. 8b),

causes the E to be overestimated. The basic problem is

that the GRU’s are not homogeneous with respect to

the assimilated (averaged) RS-based variable, L: In

May, the heterogeneity of L is at its largest in

Denmark since bare fields are mixed with fields

holding winter-sown and spring-sown crops at various

development stages. Furthermore, the rapid L devel-

opment which takes place in May makes the correct

prediction of L very sensitive to the time of sowing in

spring which is decided individually by farmers in the

area. In order to ensure the delineation of grids which

are really homogeneous with respect to the L; more

emphasis should therefore be put on the temporal

development of NDVI in the RS-based surface

classification procedure. Alternatively, more appro-

priate methods to upscale the high-resolution satellite

data must be used. One such method could be to use a

stochastic approach allowing both the average and the

standard deviation of the RS-based L0s of the GRU’s

to be assimilated.

Fig. 9. Comparison of MIKE SHE/Daisy simulated crop production

(empty symbols), adjusted MIKE SHE/Daisy simulated crop

production (filled symbols) and field data of harvested dry matter

content for grass (square), winter wheat (circle), spring barley/grass

(triangle) and maize (diamond).

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299294

5. Conclusion

The distributed hydrological model, MIKE SHE,

and the advanced Vegetation-SVAT model, Daisy,

were coupled and set up using distributed information

on topography, soil types and land use where the latter

information was derived by combining field surveying

and Earth observations. There are five soil types and 19

Fig. 10. Comparison of evapotranspiration (E) estimates given by MIKE SHE/Daisy simulations (thin line), MIKE SHE/Daisy simulations

following data assimilation (thick line) and field measurements (dots). The arrows at the bottom of the figure illustrate the time of satellite

passage. Because the leaf area indices were already well predicted, or the evapotranspiration rates were not sensitive to its adjustment (see text),

only a very little effect of the leaf area index assimilation is seen at the field sites.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 295

surface types within the study area of which 13 surface

types are made up by different crop types/composi-

tions. Pedo-transfer functions were used to derive soil

parameters, and default vegetation parameters in Daisy

were used to characterize the vegetation functioning.

Management information obtained from eight different

fields (including the four flux-sites) was used as

representative for the area. For the remaining crop

types, default settings were used.

Field data and high-resolution RS-based maps of L

and E were used to evaluate the model results. The

maps were useful to identify modelling units where

model predictions and RS-based results disagreed.

The model system predicted convincingly the L of the

experimental fields in the initial and vegetative phase,

and the modelled spatial distribution of L was also

found to be statistical similar to the RS-based L in

May after grouping of results into larger agricultural

classes. Spatial averaging of results was necessary to

reduce the difference in the spatial heterogeneity (and

statistical variance) of model results and the RS-based

products. The relative good agreement between

simulated and RS-based L confirms the suitability of

the RS-based land cover map to set up the model, and

it also signifies the practical utility of the RS-

observations to evaluate model performance. The L

predictions were more uncertain in later development

stages which points to the important potential of RS-

observations to adjust the simulations around this

time.

With respect to the modelling of evapotranspira-

tion rates, the simulations were comparable to eddy

covariance fluxes recorded from both bare and dense

fields even though the modelled E tended to over-

estimate during the drier observation period. Com-

parison of the spatial distributions of modelled E and

RS-based E was carried out for the 18-May which is in

the drier part of the observation period. On this day,

the L is a very important variable for quantifying

evapotranspiration rates. A large range of soil

evaporation rates was apparent both in the modelled

and RS-based maps, and the results also agreed by

predicting high transpiration rates of dense veg-

etation. The simulated and RS-based estimates of E

for different agricultural classes were linearly corre-

lated ðr2 ¼ 0:63Þ; but the modelled E were significant

Fig. 11. The figure shows effects of data assimilation at 18-May for the total model setup region. Daily MIKE SHE/Daisy simulations (filled

circles) and adjusted MIKE SHE/Daisy simulations (empty square) are compared in terms of their average and standard deviation of simulated

leaf area index (L) and evapotranspiration (E) in the model setup region. (a) Mean L for the total model area; (b) precipitation and mean E for the

total model area; (c) standard deviation of L within the model area; (d) standard deviation of E within the model area.

E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299296

higher than the RS-based E: The intercomparison

suggests that the simulated E is overestimated for

vegetation in this dry period, and that E is also

overestimated in the river valley, where the low depth

to the groundwater also occasionally prevented the

establishment of vegetation in the model.

Because the simulated leaf area index of the

experimental sites already agreed well with the

RS-based L; or variations in L were not significant

for the calculation of E (e.g. dense vegetation cover,

humid conditions), the assimilation of the RS-based L

did not have a large impact on the simulations at the

experimental sites. However, these results were

obtained using exact model input information on

management practice (time of sowing, etc.) and,

furthermore, during conditions when climate and soil

fertility provided optimal conditions for crop growth.

Since the development in L reflects deficiencies in

water and nutrients, RS-based assimilation remains

important to discover such situations.

In order to assimilate the RS-based L in MIKE

SHE/Daisy, the RS-data were accommodated to the

semi-lumped nature of the model by averaging the

RS-based L within the 220 agro-hydrological GRU’s

which provide the basis for the simulations. The

assimilation strategy included synchronization of all

vegetation parameters to preserve congruity of the

canopy representation. By this approach, an indirect

updating of the (unknown) sowing time was also

achieved. For allowance of vegetation in the valley

bottom, it was necessary to reduce the simulated

ground water table of some of the GRU’s. This caused

the simulated E to decrease in the river valley, thus

approaching the RS-based E: Unexpectedly, the

averaging of L within the GRU’s removed important

spatial heterogeneity for the calculation of E: Because

of the strong non-linearity between L and E; this

method for assimilating RS-data was therefore not

successful in improving the simulated E: With respect

to the predicted yields, the simulations were improved

slightly following the assimilation of the RS-based L:

In order to catch the RS-based variability in the L

in the study area, it would need to be represented

stochastically in the model, or it must be ensured that

the delineated GRU’s are really homogeneous with

respect to the assimilated variable (in this case L).

The achievement of GRU’s that are homogeneous

with respect to L requires more emphasis on

the temporal development of L (or NDVI) in the

RS-based surface classification procedure; i.e. it

would be necessary to have separate classes for

crops that are sown early and those that are sown later

in spring. Since an increased accuracy of the RS-

based surface classification is also warranted, a fewer

number of different surface types needs to be

considered. This suggests that the model has to be

accommodated to the capability of the RS-data to

provide land use information. In the future, sensitivity

studies will be conducted in order to group the

numerous field types defined in the model into fewer

‘functional’ classes.

Acknowledgements

The study took place within the framework of the

research project RS-Model which was financed by the

Danish Earth Observation Programme. The Earth

Observation Programme was granted by the Danish

Research Councils.

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