Journal of Hydrology 287 (2004) 279–299 www.elsevier.com/locate/jhydrol
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 a
Laboratory for Agrohydrology and Bioclimatology, The Royal Veterinary and Agricultural University, Copenhagen, Denmark b DHI Water and Environment, Hørsholm, Denmark c Institute of Geography, University of Copenhagen, Copenhagen, Denmark d Department of Crop Physiology and Soil Science, Danish Institute of Agricultural Sciences, Tjele, Denmark e Risø 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 * Corresponding author. Present address: Institute of Geography, University of Copenhagen, Oester Voldgade 10, 1350 Copenhagen, Denmark. Tel.: þ 45-3532-2584; fax: þ45-3532-2501. E-mail address: evb@geogr.ku.dk (E. Boegh). 1 Present address: Geological Survey of Denmark and Greenland, Copenhagen K, Denmark. 0022-1694/$ - see front matter q 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2003.10.018
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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 evaporation (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. RSdata 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 recalibrating 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.,
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1997; Hasager and Jensen, 1999) needs to be considered. The uncertainty associated with subscale 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 individual (homogeneous) fields. In the present study, an agricultural VegetationSVAT model (Daisy) and a physically based distributed 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 management and water resources, i.e. the effect of fertilizer application on ground water contamination could be explored, provided a proper validation of the distributed 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 evapotranspiration rates. Whereas the present paper focuses on the evaluation of spatially distributed simulations and assimilation effects, the temporal model performance 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
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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 temperature, 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 IR2000). 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.
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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 Campbell/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 rRED Þ=ðrNIR þ rRED Þ; where rNIR is the nearinfrared 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 Geographical Information System (GIS). In order to extend the GIS map of land use to represent the larger model area, ‘training’ (reference) areas representing 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.
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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 0.7 x
5.98 7.90 5.76
0.39 0.38 0.39
0.3 0.6 x
4.37 6.2 5.08
0.41 0.39 0.40
0.4 x
0.33 10.2
0.77 0.37
0.3 1.0 2.0 x
4.73 2.77 8.72 2.87
0.41 0.43 0.38 0.43
0.3 1.0 2.0 x
4.37 2.77 8.72 2.87
0.41 0.43 0.38 0.43
Z denotes the lower boundary of horizons (m). The deepest horizon ends at the depth of the ground water table (x).
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 symbolizes 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 superimposed 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 (destructive 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
ðr 2 ¼ 0:99Þ
ð1Þ
For SPOT, the following relationship was used (Fig. 2b) L ¼ 0:0122 e7:675 NDVI
ðr 2 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,
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decoupling coefficient, V ¼ ðD=g þ 1Þ=ðD=g þ 1 þ rs =rae Þ (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 =gÞ
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).
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 ðrae Þ; the surface resistance ðrs Þ and the vapour pressure at the surface ðes Þ ðT 2 Ta Þ þ ðes 2 ea Þ=g rae ¼ rcp s Rn 2 G rs ¼ rae ðeps 2 es Þ=ðes 2 ea Þ es ¼
0:9Veps
þ ð1 2 VÞea
½s m21
ð3Þ
½s m21
ð4Þ
½Pa
ð5Þ
where rcp is the heat capacity (J m23 K21), g is the psychrometer constant (Pa K21), Ts is surface temperature, 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
eps 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 evapotranspiration rates in Denmark using half-hourly inputs of surface temperature, net radiation, air temperature and air humidity recorded at the experimental 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 represented 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 (r 2 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
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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 vegetation in combination with a two-source atmospheric resistance network (van der Keur et al., 2001). The soil evaporation rate ðEs Þ 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 ðEv Þ are calculated by
lEv ¼ ðrcp =gÞ
epl 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 ðrstmin Þ r min rst ¼ st F 21 L
½s m21
ð8Þ
where rstmin ¼ 30 s m21 : The leaf area index (L) is used to upscale the leaf stomatal model to canopy level rst :
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F is a soil water stress constraint function given by ð Zr Ei þ SðzÞdz 0 ð9Þ FðuÞ ¼ ð Zr Ei;p þ Sp ðzÞdz 0
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 uðhr Þ Mh 2 Mhr S¼l ½m3 m23 s21 ð10Þ us 2 0:5 lnðrr2 plÞ 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 incorporating 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 Sla Wla 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
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of DS which is controlled by temperature and day length (Penning de Vries et al., 1989). The net production rate is computed using a multi-layer photosynthesis 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 evapotranspiration 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 pressure 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 parameterizations 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 RSbased 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 agrohydrological 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 RSbased 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
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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,
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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 estimators 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:
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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 evapotranspiration rates, the simulations are compared with eddy covariance measurements during a drying sequence in the period 11 â&#x20AC;&#x201C;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 â&#x20AC;&#x201C;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 insinuate 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 heterogeneity 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 temperature 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 establishment (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
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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.
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.
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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.
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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.
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 GRUâ&#x20AC;&#x2122;s. Indeed, many similar vegetation types (having exactly the same L) are present within the 220 GRUâ&#x20AC;&#x2122;s, providing a discrete structure of modelled L values. In contrast, the RSobservations have a normalized distribution of L: When the agro-hydrological units are further grouped
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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)
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
Average SD
Simulated L
RS-based L
Simulated E
RS-based E
2.48 2.37
2.28 1.29
0.29 0.09
0.21 0.07
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 â&#x20AC;&#x201C; 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 â&#x20AC;&#x201C;2 is more regular. Overall, both the RS estimates (Fig. 8a) and the Daisy/MIKE SHE simulations (Fig. 8b) expose a large sensitivity of E to variations in L: 4.3. Assimilating the RS-based L in MIKE SHE/Daisy
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 bidirectional bars show the standard deviation of simulated and RSbased results. The regression line (solid) and the 1:1 line (dotted) are also shown.
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 downadjusted 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
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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.
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).
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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).
With respect to the effect of the RS-based Lassimilation 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 agrohydrological 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 development 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 appropriate 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 L0 s of the GRU’s to be assimilated.
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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.
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
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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.
surface types within the study area of which 13 surface types are made up by different crop types/compositions. 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 RSobservations to evaluate model performance. The L predictions were more uncertain in later development stages which points to the important potential of RSobservations to adjust the simulations around this time. With respect to the modelling of evapotranspiration rates, the simulations were comparable to eddy covariance fluxes recorded from both bare and dense fields even though the modelled E tended to overestimate during the drier observation period. Comparison 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 vegetation. The simulated and RS-based estimates of E for different agricultural classes were linearly correlated ðr 2 ¼ 0:63Þ; but the modelled E were significant
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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
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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 RSbased 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.
References Abrahamsen, P., Hansen, S., 2001. Daisy: an open soil–crop– atmosphere system model. Environmental Modelling and Software 15, 313–330. Aronoff, S., 1982. Classification accuracy: a user approach. Photogrammetric Engineering and Remote Sensing 48 (8), 1299–1307. Artan, G.A., Neale, C.M.U., Tarboton, D.G., 2000. Characteristic length scale of input data in distributed models: implications for modeling grid size. Journal of Hydrology 227, 128 –139. Biftu, G.F., Gan, T.Y., 2001. Semi-distributed, physically based, hydrologic modeling of the Paddle River Basin, Alberta, using remotely sensed data. Journal of Hydrology 244, 137 –156. Boegh, E., Soegaard, H., Thomsen, A., 2002. Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance. Remote Sensing of Environment 79, 329–343. Bouman, B.A.M., 1992. Linking physical remote sensing models with crop growth simulation models, applied for sugar beet. International Journal of Remote Sensing 13, 2565–2581. Bruckler, L., Witono, H., 1989. Use of remotely sensed soil moisture content as boundary conditions in soil–atmosphere water transport modelling. 2. Estimating soil water balance. Water Resources Research 25, 2437–2447. Burdine, N.T., 1952. Relative permeability calculations from pore-size distribution data. Transactions of the AIME 198, 35 –42.
298
E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299
Butts, M.B., Thorsen, M., Boegh, E., Soegaard, H., Hansen, S., Christiansen, J.S., van der Keur, P., Abrahamsen, P., Hasager, C.B., Jensen, N.O., Thomsen, A., Schelde, K., Refsgaard, J.C., 2003. Modelling evapotranspiration rates and carbon dioxide fluxes at plot and landscape scale. In preparation. Campbell, G.S., 1974. A simple method for determining unsaturated conductivity from moisture retention data. Soil Science 117, 311 –314. Cayrol, P., Kergoat, L., Moulin, S., Dedieu, G., Chehbouni, A., 2000. Calibrating a coupled SVAT-Vegetation growth model with remotely sensed reflectance and surface temperature—a case study for the HAPEX-Sahel grassland sites. Journal of Applied Meteorology 39, 2452– 2473. Choudhury, B.J., Monteith, J.L., 1988. A four-layer model for the heat budget of homogeneous land surfaces. Quarterly Journal of the Royal Meteorological Society 114, 373 –398. Clevers, J.G.P.W., van Leeuwen, H.J.C., 1996. Combined use of optical and microwave remote sensing data for crop growth monitoring. Remote Sensing of Environment 56, 42 –51. Cosby, B.J., Hornberger, G.M., Clapp, R.B., Ginn, T.R., 1984. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research 20 (6), 682–690. Fischer, A., Kergoat, L., Dedieu, G., 1997. Coupling satellite data with vegetation functional models: review of different approaches and perspectives suggested by the assimilation strategy. Remote Sensing Reviews 15, 283–303. Gillies, R.R., Carlson, T.N., Cui, J., Kustas, W.P., Humes, K.S., 1997. 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. International Journal of Remote Sensing 18 (15), 3145–3166. Goetz, S.J., Halthore, R.N., Hall, F.G., Markham, B.L., 1995. Surface temperature retrieval in a temperate grassland with multiresolution sensors. Journal of Geophysical Research 100 (D12), 25397–25410. Guerif, M., Duke, C.L., 2000. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agriculture, Ecosystems and Environment 81, 57–69. Hansen, S., Jensen, H.E., Nielsen, N.E., Svendsen, H., 1991. Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY. Fertilizer Research 27, 245–259. Hasager, C.B., Jensen, N.O., 1999. Surface-flux aggregation in heterogenous terrain. Quarterly Journal of the Royal Meteorological Society 125, 1–28. Houser, P.R., Shuttleworth, W.J., Famiglietti, J.S., Gupta, H.V., Syed, K.H., Goodrich, D.C., 1998. Integration of soil moisture remote sensing and hydrological modeling using data assimilation. Water Resources Research 34 (12), 3405–3420. Jarvis, P., McNaughton, K., 1986. Stomatal control of transpiration. Scaling up from leaf to region. Advances in Ecological Research 15, 1–49. Joergensen, G., 1997. Remote sensing based crop simulation and soil–vegetation–atmosphere transport modelling. MIKE-SHE
modelling. Data collection and preliminary setup. Danish Hydraulic Institute, internal report, 33 pp. Kite, G.W., Droogers, P., 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field data. Journal of Hydrology 229, 3–18. Kite, G.W., Pietroniro, A., 1996. Remote sensing applications in hydrology. Hydrological Sciences 41 (4), 563–592. Loumagne, C., Normand, M., Riffard, M., Weisse, A., Quesney, A., Le He´garat-Mascle, S., Alem, F., 2001. Integration of remote sensing into hydrological models for reservoir management. Hydrological Sciences 46 (1), 89–103. Maas, S.J., 1988. Use of remotely-sensed information in agricultural crop growth models. Ecological Modelling 41, 247– 268. Moran, M.S., Humes, K.S., Pinter, P.J. Jr., 1997. The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes. Journal of Hydrology 190, 337 –362. Moulin, S., Bondeau, A., Dele´colle, R., 1998. Combining agricultural crop models and satellite observations: from field to regional scales. International Journal of Remote Sensing 19 (6), 1021–1036. Nouvellon, Y., Moran, M.S., Seen, D.L., Bryant, R., Rambal, S., Ni, W., Be´gue´, A., Chehbouni, A., Emmerich, W.E., Heilman, P., Qi, J., 2001. Coupling a grassland ecosystem model with Landsat imgaery for a 10-year simulation of carbon and water budgets. Remote Sensing of Environment 78, 131–149. Olioso, A., Chauki, H., Courault, D., Wigneron, J.P., 1999. Estimation of evapotranspiration and photosynthesis by assimilation of remote sensing data into SVAT models. Remote Sensing of Environment 68 (3), 341 –356. Ottle´, C., Vidal-Madjar, D., 1994. Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX_MOBILHY region. Journal of Hydrology 158, 241 –264. Pauwels, V.R.N., Hoeben, R., Verhoest, E.C., De Troch, F.P., 2001. The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for smallscale basins through data assimilation. Journal of Hydrology 251, 88–102. Penning de Vries, F.T.W., Jansen, D.M., Berge, H.F.M., Bakema, A., 1989. Simulation of ecophysiological processes of growth in several annual crops, Simulation Monographs, PUDOC, Wageningen, 271 pp. Ranson, K.J., Sun, G., Knox, R.G., Levine, E.R., Weishampel, J.F., Fifer, S.T., 2001. Northern forest ecosystem dynamics using coupled models and remote sensing. Remote Sensing of Environment 75, 291 –302. Refsgaard, J.C., 1996. Parameterisation, calibration and validation of distributed hydrological models. Journal of Hydrology 198, 69 –97. Refsgaard, J.C., Storm, B., 1995. MIKE SHE. In: Abbott, M.B., Refsgaard, J.C. (Eds.), Distributed Hydrological Modelling, Kluwer Academic Publishers, Dordrecht, pp. 17–39. Sandholt, I., Rasmussen, K., Andersen, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79 (2/3), 213–225.
E. Boegh et al. / Journal of Hydrology 287 (2004) 279–299 Schmugge, T., 1998. Applications of passive microwave observations of surface soil moisture. Journal of Hydrology 212/213, 188–198. Schuttleworth, J., 1998. Combining remotely sensed data using aggregation algorithms. Hydrology and Earth System Sciences 2 (2/3), 149 –158. Sellers, P.J., 1989. Vegetation-canopy spectral reflectance and bio physical processes. In: Asrar, G., (Ed.), Theory and Applications of Optical Remote Sensing, Wiley, New York, pp. 297 –336. Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B., Hall, F.G., 1992. Canopy reflectance, photosynthesis and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment 42, 187–216. Sellers, P.J., Randall, D.A., Collatz, G.J., Berry, J.A., Field, C.B., Dazlich, D.A., Zhang, C., Collelo, G.D., Bounoua, L., 1996. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I. Model formulation. Journal of Climate 9, 676–706. Sellers, P.J., Heiser, M.D., Hall, R.G., Verma, S.B., Desjardins, R.L., Schuepp, P.M., MacPherson, J.I., 1997. The impact of using area-averaged land surface properties—topography, vegetation condition, soil wetness—in calculations of intermediate scale (aaproximately 10 km2) surface–atmosphere heat and moisture fluxes. Journal of Hydrology 190, 269 –301.
299
Soegaard, H., Jensen, N.O., Boegh, E., Hasager, C.B., Schelde, K., Thomsen, A., 2003. Carbon dioxide exchange over agricultural landscape using eddy correlation and footprint modeling. Agricultural and Forest Meteorology 114, 153 –173. Su, Z., 2000. Remote sensing of land use and vegetation for mesoscale hydrological studies. International Journal of Remote Sensing 21 (2), 213–233. van der Keur, P., Hansen, S., Schelde, K., Thomsen, A., 2001. Modification of DAISY SVAT model for use of remotely sensed data. Agricultural and Forest Meteorology 106 (3), 215 –231. Weiss, M., Troufleau, D., Baret, F., Chauki, H., Pre´vot, L., Olioso, A., Bruguier, N., Brisson, N., 2001. Coupling canopy functioning and radiative transfer models for remote sensing data assimilation. Agricultural and Forest Meteorology 108, 113 –128. White, J.D., Running, S.W., Thornton, P.E., Keane, R.E., Ryan, K.C., Fagre, D.B., Key, C.H., 1998. Assessing simulated ecosystem processes for climate variability research at Glacier National Park, USA. Ecological Applications 8 (3), 805 –823. Wood, E.F., 1995. Scaling behaviour of hydrological fluxes and variables: empirical studies using a hydrological model and remote sensing data. Hydrological Processes 9, 331–346.