Estimation of Evapotranspiration and Photosynthesis by Assimilation

Estimation of Evapotranspiration andPhotosynthesis by Assimilation of RemoteSensing Data into SVAT Models

Albert Olioso,* Habiba Chauki,* Dominique Courault,*and Jean-Pierre Wigneron*

Estimation of evapotranspiration and photosynthesis transfers, and particularly with evapotranspiration andCO2 assimilation by vegetation.from remote sensing data frequently use soil–vegetation–

atmosphere transfer models (SVAT models). These mod- Remote sensing data may be directly introduced insemiempirical models to compute energy and mass fluxesels compute energy and mass transfers using descriptions

of turbulent, radiative, and water exchanges, as well as at the time of measurements or at the daily scale usingan extrapolation procedure. Such approaches have beena description of stomatal control in relation with water

vapor transfers and photosynthesis. Remote sensing data used to estimate sensible and latent heat fluxes from sur-face temperature (e.g., Heilman and Kanemasu, 1976;may provide information that is useful for driving SVAT

models (e.g., surface temperature, surface soil moisture, Jackson et al., 1977; Seguin and Itier, 1983; Kustas, 1990;Inoue et al., 1990, Lagouarde, 1991, Lhomme et al.,canopy structure, solar radiation absorption, or albedo).

Forcing or recalibration methods may be employed to 1992; Moran et al., 1994; Norman et al., 1995; Zhan etal., 1996; Chehbouni et al., 1997; Troufleau et al., 1997).combine remote sensing data and SVAT models. In this

article a review of SVAT models and remote sensing esti- Some of these approaches are listed in Table 1, and thereader may find a detailed comparison of four recentlymation of energy and mass fluxes is presented. Examples

are given based on our work on two different SVAT mod- published models in Zhan et al. (1996). The simplifiedels. Eventually, some of the difficulties in the combined relationship, first derived at field scale by Jackson et al.use of multispectral remote sensing data and SVAT mod- (1977) and later analyzed by Seguin and Itier (1983), hasels are discussed. Elsevier Science Inc., 1999 been used for mapping daily evapotranspiration over

large areas (Lagouarde, 1991; Courault et al., 1994). Inanother wavelength domain, Chanzy et al. (1995) pro-

REMOTE SENSING OF posed a semiempirical model, which may be driven byEVAPOTRANSPIRATION AND surface soil moisture measurements in the microwavePHOTOSYNTHESIS domain, for estimating bare soil evaporation. These au-

thors proposed further to combine this model with theMonitoring energy and mass transfers of soils and vegetalsimplified relationship and thermal infrared data. Thus,canopies is a critical step for water and vegetal resourcesthey were able to specify the parameters of the soil evap-management. It is also useful for a better understandingoration model.of climate and for predicting its evolution. Remote sens-

Semiempirical approaches have also been applied foring is an attractive tool to achieve these goals since itestimating net primary production (which represents anprovides information which is related to energy and massintegrator of vegetation photosynthesis). They mostlyrely, in a first step, on the estimation of photosyntheti-

* INRA Bioclimatologie, Domaine Saint-Paul, Avignon, France cally active radiation (PAR) absorption by vegetation can-Address correspondence to Albert Olioso, INRA Bioclimatologie, opies from spectral reflectance measurements. A second

Domaine Saint-Paul, F-84914 Avignon Cedex 9, France. E-mail: olioso step is to convert this absorbed radiation into [email protected] 13 August 1997; revised 24 November 1998. through production models derived from Monteith (1972)

REMOTE SENS. ENVIRON. 68:341–356 (1999)Elsevier Science Inc., 1999 0034-4257/99/$–see front matter655 Avenue of the Americas, New York, NY 10010 PII S0034-4257(98)00121-7

342 Olioso et al.

Table 1. Some Semiempirical Models for Latent Heat Flux, Sensible Heat Flux, and Photosynthesis Estimationa

Simplified relationshipSeguin and Itier (1983) LEd5Rnd2A12B1(Ts,14h2Tax)

Methods based on an excess resistance

Kustas (1990) Hi5qcpTs2Ta

ra1rexLhomme et al. (1992)Moran et al. (1994) LEi5(12A2)Rni2Hi

Approaches based on a relationshipbetween

radiometric and a so-called “aerody-namic temperature”

Troufleau et al. (1997) Hi5qcpTaer2Ta

ra

Chehbouni et al. (1997) Taer2Ta5(12A3)(Ts2Ta)Two source approach

Norman et al. (1995) Hi5qcp1Tv2Ta

ra

1Tg2Ta

ra1rc2

Tg and Tv may be estimated from multiangular measurements ofTs or from one single measurement in combination with a Priestley-Taylor based energy balance calculation

Soil evaporation

Chanzy et al. (1995)Ed

Epd

5exp (A4 h[025]1B4)

C4 [11exp (A4 h[0–5])]1(12C4)

Biomass production

Kumar and Monteith (1981) DMS5eb1A51B5qnir

qr2RPAR

a Symbols: A1, A2, A3, A4, A5, B1, B4, B5, and C4: empirical coefficients; cp: specific heat of air; Ed: daily soil evaporation; Epd: daily soil potential evaporation;Hi: instantaneous sensible heat flux; LEi: instantaneous latent heat flux, LEd: daily latent heat flux; RPAR: cumulated incident photosynthetically activeradiation; ra: aerodynamic resistance (above the canopy); rc: aerodynamic resistance at the soil surface; rex: excess resistance; Rni: instantaneous netradiation; Rnd: daily net radiation; Ta: air temperature at some height above the canopy; Taer: “aerodynamic temperature” (here, it corresponds to themean air temperature at some height in the canopy); Tax: daily maximum air temperature; Tv: vegetation surface temperature; Tg: soil surface temperature;Ts: radiometric surface temperature; Ts,14h: radiometric surface temperature at 14 h local solar time; DMS: biomass increase; eb: conversion efficiencyof absorbed radiation into biomass; q: air density; qr and qnir: red and near-infrared relfectances; h[0–5]: 0–5 cm top soil moisture.

(cf. Table 1). This method was first proposed by Kumar which are acquired instantaneously. Conversely to sem-iempirical approaches, SVAT models give access to a de-and Monteith (1981), and used at field scale for example

by Steven et al. (1983), Asrar et al. (1985), Steinmetz et tailed description of soil and vegetation canopy pro-cesses, and not only to a limited number of finalal. (1989). It has also been applied at regional and global

scales from satellite data (e.g., Prince, 1991; Guerif et al., variables such as evapotranspiration or net primary pro-duction. They simulate intermediary variables linked to1993; Ruimy et al., 1994; Hanan et al., 1995). In some

studies, the effect of water stress on the conversion of hydrological or physiological processes. Thus, they areoften proposed to estimate soil moisture from remoteabsorbed radiation into biomass has been taken into ac-

count by adding thermal infrared data to the analysis sensing data and then used as an interface with othermodels, such as atmospheric or hydrological models.(Asrar et al., 1985; Steinmetz et al., 1989; Guerif et al.,

1993). Another way to compute photosynthesis from Their other advantage, compared to more empirical ap-proaches, consists of the fact that they may be operatedthermal infrared measurements has been proposed by

Smith et al. (1985) and Choudhury (1986; 1989) and is without a systematic use of remote sensing data; themodel intrinsically provides the mean for interpolatingbased on latent heat flux and vegetation surface conduc-

tance determinations. fluxes between remote sensing data acquisitions. More-over, it may be possible to implement procedures to as-More deterministic models may be used for estimat-

ing evapotranspiration and photosynthesis. In such mod- similate data acquired by a large range of remote sys-tems, differing in wavelength domains, acquisition timeels, most of the transfer mechanisms (radiative, turbu-

lent, and water transfers) and some physiological processes or geometry. For instance, it may be proposed to com-bine thermal infrared data from different sensors such(stomatal regulation, photosynthesis) are described inde-

pendently of remote sensing data. These models are of- as ATSR and AVHRR which provide midmorning andmidafternoon data, respectively. Data from Earth obser-ten called soil–vegetation–atmosphere transfer models

(SVAT models). Their time resolution is less than 1 h in vation systems (SPOT-HRV, ERS, Radarsat) might alsobe combined to thermal data provided by meteorologicalagreement with the dynamic of atmospheric and surface

processes. Their fine time resolution is also interesting sensor (e.g., AVHRR) despite their very different acqui-sition repetitivities.when they are combined with remote sensing data,

Estimation of Evapotranspiration and Photosynthesis 343

Other types of mechanistic models, such as crop • turbulent transfers, generally based on the re-production models or natural vegetation functioning sistive schemes by Deardorff (1978), Choudhurymodels, may be used to monitor net primary production (1989), or van de Griend and van Boxel (1989);or even economic crop yields (Delecolle et al., 1992; • heat and water transfers in the soil; in most mod-Running et al., 1989; Bouman, 1992; Moulin et al., els, a system with two soil reservoirs is used,1998). At present time, as in the approaches based on one representing the root zone and the otherMonteith’s production model, such models are mostly one the first centimeters of the soil which are di-combined with reflectance data, or in some occasions rectly concerned by soil evaporation (e.g., Deard-with active microwave data, via the estimation of leaf orff, 1978; Carlson et al., 1990);area index or radiation absorption. The use of thermal • water transfers from the soil to the atmosphere,infrared is more difficult because it gives access to pro- through the plants, based on an adequate de-cesses which have a time dynamic not compatible with scription of stomatal regulation and water extrac-model time steps (usually the day or longer). tion by the roots; the stomatal conductance may

We will focus our article on assimilation of remote be either, directly related to the soil water con-sensing data into SVAT models. In the first part of the tent (Taconet et al., 1986; Noilhan and Planton,article, we will give basic information on SVAT models, 1989), or indirectly using a description of waterand in the second part, illustrations of different types of transfers from the soil to the leaves along waterassimilation procedures found in the remote sensing lit- potential gradients (Lynn and Carlson, 1990;erature. In the third part, two SVAT models developed Braud et al., 1995; Olioso et al., 1996a);at INRA will be presented and applied to the estimation • in some cases, photosynthesis in relation with wa-of fluxes from thermal infrared data. In the fourth part, ter transfers, through stomatal regulation or wa-some of the difficulties encountered in the use of remote ter status of leaves (Zur and Jones, 1981; Oliososensing data and SVAT models will be discussed. et al., 1996a; Sellers et al., 1996a; Carlson and

Bunce, 1996).

SVAT MODELS SVAT models usually require information on vegeta-tion structure (LAI, height), optical properties of soil andMany SVAT models have been developed in the lastvegetation, physiological properties of vegetation (stoma-20 years:tal conductance description, water transfer from soil to

• to study evapotranspiration (Norman, 1979; Shut- plants), thermal and hydraulic properties of the soil, andtleworth and Wallace, 1985; Meyers and Paw U, atmospheric conditions (air temperature and humidity,1987; Choudhury, 1989; Braud et al., 1995), wind speed, incident radiations).sometimes in relation with photosynthesis (Zur The complexity of the soil–vegetation–atmosphereand Jones, 1981; Olioso et al., 1996a; Carlson system description varies a lot from simple one-layerand Bunce, 1996); models (Fig. 1), which only describe global exchange of

• to describe energy and water behavior of soils the soil–vegetation system with the atmosphere, such asand vegetation canopies in hydrological, climate, evapotranspiration (Soer, 1980; Courault et al., 1996), toor weather forecast models (Deardorff, 1978; very detailed models which describe microclimate pro-Dickinson et al., 1986; Noilhan and Planton, files inside the canopy and/or the soil (Norman, 1979;1989; Capehart and Carlson, 1994; Sellers et al., Meyers and Paw U, 1987; van de Griend and van Boxel,1996a); 1989; Bruckler and Witono, 1989; Braud et al., 1995).

• or to interpret remotely sensed data in relation Actually, intermediate two-layer models are more classi-with exchange processes at soil and canopy level

cally used (Fig. 2). They partition energy fluxes into(Soares et al., 1988; Bruckler and Witono, 1989;fluxes originating from one vegetation layer and from theSoer, 1980; Taconet et al., 1986; Hope et al.,soil surface (Taconet et al., 1986; Lynn and Carlson,1988; van de Griend and van Boxel, 1989; Lynn1990; Camillo, 1991; Olioso, 1992). They are less com-and Carlson, 1990; Camillo, 1991; Olioso, 1992;plex to handle than multilayer models, but they simulateCourault et al., 1996).separately vegetation transpiration and soil evaporation,

SVAT models simulate energy and mass transfers which behave in different ways depending on environ-between the soil, the vegetation, and the atmosphere. mental conditions. Because of a new interest in the studyThey are based on the resolution of the energy balance of CO2 fluxes in climatological and global change studies,and on the parametrization of different processes: some of the two-layer models have been extended to the

simulation of photosynthesis (Carlson and Bunce, 1996;• radiative transfers, often following a simpleSellers et al., 1996a). One-layer and two-layer modelsBeer–Lambert law or using radiative transferonly simulate global fluxes from the soil or the vegetationschemes as in Hope et al. (1988), Camillo

(1991), or in Olioso (1992); without a detailed description of the processes inside of

344 Olioso et al.

Figure 1. Schematic structure of single-layer and mul-tilayer SVAT models [adapted from Raupach andFinnigan (1988)]. Symbols: gvi: surface conductanceof vegetation layer i; H: sensible heat flux; Hs: sensi-ble heat flux from the soil surface; Hvi: sensible heatflux from vegetation layer i; hvi: turbulent exchangecoefficient for vegetation layer i; LE: latent heatflux; LEs: latent heat flux from the soil surface; LEvi:latent heat flux from vegetation layer i; qa: air hu-midity; qaci: air humidity inside vegetation layer i; r*:surface resistance; ra: aerodynamic resistance; Rg:global solar radiation; Ta: air temperature; Taci: airtemperature inside vegetation layer i.

the canopy (microclimate profiles). This raises difficulties are often in default for heterogeneous systems such asorchards, vineyards, developing crops or savannah.in the calculation of global turbulent exchange coefficients

and global surface conductance for the vegetation layer or SVAT models have usually been designed for homo-geneous surface. However, they have been employed atthe global soil–vegetation system (Raupach and Finnigan,

1988). Simple integration procedures from leaf to canopy very different spatial scales, from the plot level, to thescale of a grid cell in atmospheric general circulationhave been used to define these coefficients. They gener-

ally give good results for homogeneous canopies, but they models. It is easy to identify the type of canopy and the

Figure 2. Schematic structure of a two-layer SVATmodel. Symbols: gs: surface conductance of thesoil; gv: surface conductance of the vegetationlayer; H: sensible heat flux; Hs: sensible heat fluxfrom the soil surface; Hv: sensible heat flux fromthe vegetation layer; h: turbulent exchange coef-ficient between the canopy and the atmosphere;hs: turbulent exchange coefficient between thesoil surface and the air inside the canopy; hv: tur-bulent exchange coefficient between the vegeta-tion and the air inside the canopy; LE: latentheat flux; LEs: latent heat flux from the soil sur-face; LEv: latent heat flux from the vegetationlayer; qa: air humidity; qac: air humidity inside thecanopy; Rg: global solar radiation; Ta: air temper-ature; Tac: air temperature inside the canopy.


relevant input parameters at the plot level, but at a model, from a NOAA-AVHRR-based land cover map.For bare soils, surface soil moisture derived from micro-larger scale, heterogeneous landscapes imply to define

effective media. Procedures for averaging SVAT model wave measurements were “forced” as surface boundaryconditions of soil–atmosphere transfer models, for in-input parameters have been presented (e.g., Raupach,

1993; Blyth, 1995; Chehbouni et al., 1995; Noilhan and stance, by Bruckler and Witono (1989).Lacarrere, 1995). Remote sensing may also be a way todefine effective parameters (Kreis and Raffy, 1993). “Recalibration” Methods

“Recalibration” methods consist of adjusting some of themodel parameters in order to minimize the differenceREMOTE SENSING DATA ASSIMILATION INTObetween the information derived from remote sensingSVAT MODELSand the model simulations. For instance, it is then possi-

In this article, we will consider that “assimilation” may ble to reproduce the diurnal courses of canopy fluxesrepresent any kind of combination of models with re- from instantaneous measurements of surface tempera-motely sensed observations. Various remote sensing data ture (Soer, 1980; Camillo, 1991; Diak and Whipple,may be used to drive SVAT models. Spectral reflectances 1993; Kreis and Raffy, 1993; Ottle and Vidal-Madjar,in visible and near-infrared domains may give informa- 1994, Olioso et al., 1996b) (see Fig. 4). By recalibratingtion on the structure of vegetation canopies, as LAI (e.g., two SVAT models using one measurement of surfaceOttle and Vidal-Madjar, 1994; Sellers et al., 1996b; temperature each data at mid-day, Olioso et al. (1996b)Friedl, 1996; Gillies et al., 1997). The contribution of mi- monitoring evapotranspiration and stomatal conductancecrowave data has not been studied very often in the case throughout the cycle of a soybean crop. Differences wereof vegetation canopies, but they have been proposed for noticed between the two models in particular in the re-estimating vegetation structure and more often soil water trievals of stomatal conductance (Fig. 5). However, latentstatus (Ragab, 1995; Calvet et al., 1998). In the case of heat fluxes were retrieved with a good accuracy. Estima-bare soil, the use of microwave data was demonstrated tions of photosynthesis were also performed (Olioso etfor energy and water balance monitoring by Soares et al. al., 1996b).(1988), Bruckler and Witono (1989), and Entekhabi et In recalibration methods, sensitivity analyses ofal. (1994), for instance. For vegetation canopies, the SVAT models have shown that the best parameters to bemost natural approach is based on thermal infrared, retrieved are related to water transfers in the vegetationwhich is closely related to the energy balance via sur- layer (stomatal conductance or soil–plant hydraulic con-face temperature. ductance), or to soil surface water status (surface resis-

Depending on models and data, various assimilation tance, surface moisture). In some studies, relationshipsmethods have been developed. It is possible to directly between soil water status and somatal conductance werefeed models with quantities derived from remotely sensed introduced in order to assess soil moisture in the rootdata: “forcing” methods (following the definition given by zone. Some agricultural applications were presented, suchDelecolle et al., 1992). It is also possible to use remote as the estimation of holding water capacity of agriculturalsensing data to adjust some unknown model parameters fields (Vidal et al., 1987). More often, this aims to use(“recalibration” method following Delecolle et al., 1992). SVAT models as tools to estimate variables directly in re-Some approaches based on spatial variations of remote lation with hydrological or meteorological models, andsensing measurements have also been proposed (Carlson drive these models from remote sensing (Ottle and Vi-et al., 1990; 1995). dal-Madjar, 1994; Capehart and Carlson, 1994; Calvet et

al., 1998). In these works, the retrieved parameter was“Forcing” Methods actually the root zone soil moisture at a given date. Then,

a correction of possible temporal drift of the soil mois-“Forcing” methods consist of setting some of the inputquantities in the model at values estimated from remote ture, a variable which is dynamically predicted by SVAT

models, may be done. This method may be termed assensing measurements. Sellers et al. (1996b) exploit re-flectance measurements to infer LAI, albedo, maximum “reinitialization” instead of “recalibration” since an initial

value of a variable is adjusted instead of a model parame-canopy conductance, and maximum canopy photosynthe-sis (i.e., conductance and photosynthesis for an un- ter (Moulin et al., 1998). Calvet et al. (1998) employed a

simple variational assimilation techniques. These authorsstressed canopy) (see Fig. 3). This information may be“forced” in a SVAT model to compute surface energy retrieved the root zone soil moisture by reinitializing

their model using measurements of surface soil moisture,balance and photosynthesis (CO2 fluxes) inside of an at-mospheric general circulation model (Randall et al., or surface temperature, acquired during the following 5

or 15 days. This method was applied day after day in1996). Noilhan et al. (1991) prescribed the input param-eters of a SVAT model, which is used to define the sur- order to monitor the root zone soil moisture continu-

ously. Such a procedure was used with success by Mahf-face boundary conditions in a mesoscale atmospheric

346 Olioso et al.

Figure 3. Schematic procedures for generating globalfields of surface biophysical properties for the SVATmodel SiB2 which is used as surface description in theColorado State University Atmospheric General Circu-lation Model. NDVI data are used to estimate FPAR(the fraction of absorbed PAR by the vegetation) and,then, photosynthesis and surface conductance charac-teristics forced in the SVAT model (in combinationwith information on land cover). Other proceduresused to obtain albedo and turbulent transfer coeffi-cients are not shown on this Figure. Simplified fromSellers et al. (1996b).

ouf (1991) to retrieve soil moisture reservoir from assimi- transfer parametrizations in order to assimilate reflec-tance measurements. Camillo (1991) presented a prelim-lation of near surface air temperature and humidity

(variational techniques are commonly used in data assim- inary work using albedo measurements instead of spec-tral reflectance measurements. Olioso et al. (1994) alsoilation procedures for atmospheric modeling dedicated to

weather forecast). included microwave radiative transfer to simulate micro-wave emission and backscattering (see Fig. 7). However,A special recalibration method proposed by Carlson

et al. (1990) was originally based on the correlations be- as such microwave radiative transfer models are complex,their inversion in combination with SVAT modeling hastween surface temperature and vegetation indices which

were found when analyzing multispectral images. They not been undertaken yet.made use of these correlations in order to drive SVATmodels and then to estimate surface fluxes. This was ex- APPLICATIONS WITH THE ALiBi AND THEtended later to analyze the spatial variability of surface MAGRET SVAT MODELSsoil water availability and surface energy fluxes (Carlsonet al., 1995; Capehart, 1996; Gillies et al., 1997). Instead In this section, we provide detailed examples of recali-

bration procedures with two different SVAT models de-of working on surface temperature/NVDI correlations,these authors based their analysis on coupling SVAT veloped at INRA. We have chosen these two models be-

cause differences in their characteristics imply differentmodel stimulations to surface temperature/NDVI scatterplots (Fig. 6). The SVAT model was used for simulating recalibration strategies. The ALiBi model is a SVAT model

proposed by Olioso (1992) to relate evapotranspirationsurface temperature and fluxes for different surface soilwater availabilities and canopy fractional covers (canopy and photosynthesis to reflectance and surface tempera-

ture (more precisely, infrared brightness temperature)fractional covers were related to NDVI). Then, thesesimulations were matched to the scatter plots derived measurements. As simulations are typically done for a

1-day period, it is possible to neglect water transfer pro-from the images, making it possible to derive surface soilwater availability and surface fluxes for each pixels. cesses in the soil, which is interesting since soil hydraulic

parameters are difficult to assess. One infrared bright-Minimization procedures may be extended to reflec-tance and microwave measurements. Models implemented ness temperature measurement is used to recalibrate the

model every day. Conversely to ALiBi, the MAGRETby Camillo (1991) or Olioso (1992) included radiative


Figure 4. Schematic representation of the recalibra-tion of a SVAT model from surface temperature mea-surements.

model (Courault et al., 1996) includes a description of perature, and iii) PAR distribution inside of the canopy(which is used to compute stomatal conductances andthe water transfers in the soil. Then, this model may beleaf photosynthesis).used to monitor evapotranspiration over long periods.

The infrared brightness temperature Tb is obtained byOnly some temperature measurements at different timesduring a crop cycle must be used to recalibrate the fk(Tb)5ev,dkfk(Tv)1st,d,kes,d,lfl(Tg)1qt,d,kRgk↓,k , (3)model.

where Tv and Tg are the vegetation and the soil surfacetemperature [solutions of energy balance Eqs. (1) andThe ALiBi Model (Olioso, 1992)(2)], Rgk↓,k the incident radiation in the measurementThe ALiBi model is a two-layer model (Fig. 2): energywaveband, ev,d,k and es,d,k the vegetation and the soil emis-balances are computed at the soil surface (s indices) and sivity in the direction of observation (the subscript d re-

for the vegetation canopy (v indices), allowing separate fers to the directionality of the viewing), st,d,k the vegeta-simulations of soil evaporation and plant transpiration, tion transmission coefficient for thermal radiation, qt,d,kand a better determination of infrared brightness tem- the canopy reflection coefficient, fk(T) a function of theperatures: temperature to calculate the thermal emission in the

measurement waveband k (Olioso 1995a). Coefficients inRnv5LEv1Hv , (1)Eq. (3) are derived from the SAIL model. For instance,Rns5LEs1Hs1G . (2)the emissivity ev,d,k is obtained by considering the ab-

Rni, Hi, LEi, and G represent net radiations, sensible sorption of the direct radiation in the thermal domain byheat fluxes, latent heat fluxes, and ground heat flux. the vegetation layer. A similar calculation was done byThese fluxes, as well as photosynthesis, are calculated by Olioso (1995b) to determine the emissivity of the soil–solving Eqs. (1) and (2), and using descriptions of radia- vegetation system.tive transfers, turbulent transfers, water transfers, stoma- Turbulent fluxes are defined by using turbulent ex-tal conductance, and leaf photosynthesis. change coefficients between the atmosphere and leaf or

Radiative transfers are derived from the SAIL model soil surface (Fig. 2). Surface conductances are intro-which was developed by Verhoef (1984) to simulate can- duced for calculating latent heat fluxes. The soil surfaceopy spectral reflectances. Here, it is also used to com- conductance (gs) depends on the surface soil moisture inpute i) radiation absorption by the soil and the vegetation the first five centimeters (h[0–5]). The vegetation surfacelayer, in the solar domain as well as in the thermal do- conductance (gv) depends on stomatal conductances. For

example, the heat fluxes originating from the vegetationmain, ii) thermal emission and infrared brightness tem-

348 Olioso et al.

Figure 5. Comparison to measurements (cir-cles) of hourly evapotranspiration and sto-matal conductance retrieved by recalibratingtwo SVAT models, in different conditions ofwater availability: — Taconet et al. (1986),– • – and ALiBi (Olioso, 1992). From Oliosoet al. (1996b).

LEv and Hv are given by Eq. (4): midity, and the ac and v indices the air inside of thecanopy and at the surface of vegetation elements. Similar

LEv5qL1 1hv

11gv221

1q*v (Tv)2qac2 (4) equations hold for heat fluxes originating from the soiland between the canopy and the atmosphere above. Thevarious exchange coefficients involved in the calculationandof heat fluxes (Fig. 2) are related to the vegetation struc-

Hv5qcphv(Tv2Tac), ture and to the properties of the atmosphere (windspeed and stability). Calculations are based on Monin–where q is the air density, cp the air specific heat, L theObukhov theory above the canopy and the works byvaporization latent heat, and hv a turbulent exchange co-

efficient. Ti and qi represent the air temperature and hu- Cowan (1968) and van de Griend and van Boxel (1989)


the leaf surface. A detailed description of water transferand photosynthesis was given by Olioso et al. (1996a).

As a summary, the ALiBi SVAT model computes en-ergy balance fluxes for the soil and the vegetation, vege-tation photosynthesis, and infrared brightness tempera-ture. Main inputs are the meteorological forcing (airtemperature and humidity, wind speed, and incident ra-diations) and some parameters describing the vegetationstructure, the surface soil water status (represented byh[0–5]), the optical properties of the canopy elements, thecapacity of roots to extract water (represented by Gp),and the response of stomatal conductance and leaf pho-tosynthesis to driving variables (radiation, water poten-tial, and saturation deficit). As water transfers in the soilare not described, the model may only be used for 1-dayperiods with its initial set of parameters. Longer periodsmay be simulated, but input parameters have to be pre-scribed each day.

Evapotranspiration andPhotosynthesis EstimationEvapotranspiration and photosynthesis for a soybeancrop were estimated by recalibrating the ALiBi modelevery day. The modeled infrared brightness temperatureat midday was fitted to its measurement by adjusting thehydraulic conductance parameter Gp. Only one measure-ment of thermal brightness temperature was used eachday to derive one single value of Gp. However, the

Figure 6. Daily average of sensible (H) and latent (LE) heat hourly evolution of the fluxes was derived since Gp wasflux simulations for different fractional vegetation covers and assumed constant throughout the day. The other re-different surface soil moisture availabilities (M0), for the Ma-

quired parameters were directly measured in the fieldhantango area (Pennsylvania, USA), for 12 August 1990, for a(LAI, canopy height, surface soil moisture, root zone wa-generic agricultural crop. Fluxes are presented in a fractional

vegetation3normalized temperature space, together with M0 ter potential), as well as forcing meteorological variablesisolines (s50.0, e50.2, h50.4, n50.6, ,50.8, 351.0). (air temperature, air humidity, and wind speed at a ref-From Capehart (1996).

erence level above the canopy and solar radiation at eachtime step). A description of the experiment may befound in Olioso et al. (1996a,b).

inside of the canopy. We assume that the vegetation con- Evolutions of daily mean evapotranspiration andductance gv is given by the sum of the leaf stomatal con- photosynthesis estimated by recalibration are presentedductances over the whole canopy and that the stomatal on Figure 8. There was a good agreement with the mea-conductance depends on incident PAR at leaf surface, surements (root mean square errors (RMSE) of 18.1 Wleaf water potential, and air saturation deficit at the leaf m22 over the whole experiment for evapotranspiration,surface [as in Winkel and Rambal (1990)]. and of 1.50 lmol m22 s21 for photosynthesis between day

Water transfers are solved by assuming that the tran- 240 and 255). Two significant peaks occurred immedi-spiration is equal to the water extraction by the roots, ately after rains on days 226 and 242. These two rainywhich may be described by van den Honert’s equation events were followed by two drying periods which wereas a function of water potential difference between the well reproduced by the model. Evapotranspiration esti-soil and the leaves (wg2wv) and of an hydraulic conduc- mations were almost unbiased. However, systematic un-tance (Gp): derestimations occurred before day 225, that is, when

LAI was lower than 3. Crop biomass production was alsoLEv5Gp (wg2wv) (5)monitored with a good agreement (Fig. 9) by assuming

Canopy photosynthesis is computed independently that the assimilation of 1 mol of CO2 resulted in the pro-of water transfers and stomatal regulation from a leaf duction of 30 g of dry matter. In order to compute thesub-model which account for incident PAR at the leaf above-ground biomass, we considered that the fraction

of root biomass inside of the total biomass varied linearlysurface, leaf water potential, and air saturation deficit at

350 Olioso et al.

Figure 7. Schematic diagram of the couplingbetween a SVAT model and a microwave ra-diative transfer model. Redrawn from Oliosoet al. (1994).

between 0.5 and 0.2 between day 199 and 226, and that The MAGRET Model (Lagouarde, 1991;Courault et al., 1996)it kept a constant value (0.2) later (from Wilkerson et al.,

1985). Hourly evolutions of estimated latent heat flux, MAGRET considers only one energy balance to repre-canopy photosynthesis, and stomatal conductance were sent the whole soil–vegetation system (Fig. 1):presented by Olioso et al. (1996b) (cf. Fig. 5). Midday

Rn5LE1H1G . (6)depressions which appeared in water stress conditions(Olioso et al., 1996a) were reproduced. The albedo is computed by using a simple radiative

transfer model developed by Baret et al. (1988). A radia-tion attenuation factor is introduced to compute theALiBi Recalibration Sensitivityground heat flux below the canopy. Turbulent transfersModel recalibrations were performed assuming that allwere based on Monin–Obukhov theory taking into ac-model parameters, except Gp, were known. LAI, vegeta-count the difference between thermal and momentumtion height (zh), surface soil moisture (h[0–5]), root zoneroughness length. A global surface resistance r* is intro-water potential (wg), for the most important parameters,duced in Eq. (7) to compute the latent heat flux:were prescribed from their measured values. We investi-

gated the effect of the uncertainty of these parametersLE5qL

q*s (Ts)2qa

ra1r*(7)on the recalibration results (Table 2). The vegetation

height had a large influence on the estimation of photo-with Ts the surface temperature [solution of Eq. (6)] andsynthesis and evapotranspiration. This was in relationra the aerodynamic resistance between the surface andwith the high sensitivity of surface temperature andthe atmosphere. The surface resistance r* depends onfluxes to turbulent exchange coefficients. The surface soilthe vegetation structure, the soil surface conductance,moisture h[0–5] had a marked influence on photosynthesisand the vegetation surface conductance. This last con-and transpiration values since it was a preponderant fac-ductance is calculated from the stomatal conductancetor in the partition of latent heat flux between the soiland depends on incident radiation, saturation deficit,surface and the vegetation layer. Its effect was smallertemperature, and water status of the soil, following aon evapotranspiration because a compensation occurredsimilar description as used in ALiBi (however, the waterbetween soil surface and vegetation conductances, whichstatus is accounted for through the soil water reserve andkept LEv1LEs almost constant. LAI and root zone waternot through the leaf water potential). Water transfers inpotential had very small effects. In the case of wg, a di-the soil are simulated using a two-reservoir system whichrect compensation occurred between the potential andare discharged by plant uptake and are filled by rains (inthe hydraulic conductance Gp for a constant transpirationthe limit of the maximum reserve).[Eq. (5)]. In the case of LAI, variations in radiative effect

on vegetation conductance and photosynthesis (due toMAGRET Applicationschanges in LAI) were compensated for by variations inWe used the MAGRET model to estimate the evapo-water effect (introduced by obtaining a different adjustedtranspiration of the soybean crop over its whole cycle.value of Gp at each LAI). A slight decrease of mean dailyThe model was recalibrated using about 15 surface tem-photosynthesis was due to the increase in respirationperatures measured at 14 local solar time throughout thewhen the LAI rose. Diurnal photosynthesis was onlycycle (every 3–5 days). Two cases were investigated. Inslightly affected. A detailed analysis of the sensitivity ofthe first case, we assumed that all the water suppliestwo SVAT model recalibrations, including ALiBi, was

presented by Chauki et al. (1997). (rains1irrigations) were known, and we adjust a parame-


Figure 9. Evolution of canopy biomass estimated by recali-bration of ALiBi: —5above-ground biomass; – –5totalbiomass; d5above-ground biomass measurements.

was bare or when the vegetation was not developed (tillaround day 220), Figure 10 shows that the model hadvery different behaviors depending on water supply re-gimes. The daily supplies used in the second casesmoothed the evolution of evapotranspiration and did notallow rapid drying of the soil surface as in the first case.

DISCUSSION ON SOMEOCCURRING PROBLEMS

When using SVAT models to monitor energy and massFigure 8. Evolution of daily evapotranspiration and photo- fluxes, or soil and plant water status, from remote sens-synthesis of the soybean canopy: —5recalibrated model ing measurements, one may face some difficult points.(ALiBi); d5measurements; error bars correspond to the Some of the problems we encountered in the bibliogra-range of photosynthesis measurements with two crop

phy or our own work are discussed in this section.chambers (see Olioso et al., 1996a).

About the Soil Moisture vs. Surface ResistanceTo be able to retrieve soil moisture from thermal infra-ter representing the maximum water holding in the soil.

In a second case, only the rains were known, and we ad- red measurements, it is required that surface tempera-ture is sensitive to changes in soil moisture. This may notjusted the amount of irrigation supply (assuming that a

constant amount was supplied each day). Simulations be the case for the soil moisture in the root zone in win-ter conditions, for a low vegetal cover or in absence ofwere initialized at the beginning of May, when heavy

rains had saturated the soil. A bare soil was considered water stress (see, e.g., Taconet and Vidal-Madjar, 1988;Calvet et al., 1998). Conversely, surface soil moisture hasuntil emergence, which occurred at the beginning of

July. The crop development (LAI and vegetation height) generally a large influence on surface temperature (BenMehrez et al., 1992; Carlson et al., 1995; Capehart,was introduced from field measurements after emer-

gence. Meteorological forcing was obtained from the net- 1996). However, Carlson et al. (1995) showed that SVATmodel sensitivity to surface soil moisture actually de-work meteorological station in Montfavet, some hundred

meters away from the soybean field. Both recalibration pends on the sensitivity to water availability at the soilsurface. The relation between this availability, which maycases had a similar behavior when the crop was present

(Fig. 10), with a 1.5 mm day21 RMSE. Underestimations be represented by the soil surface conductance, and thesurface soil moisture depends on soil type and climaticof evapotranspiration were noticed: 0.7 mm day21 in the

first case and 0.4 mm day21 in the second. As for ALiBi conditions (Daamen and Simmonds, 1996). A similarproblem occurs with the root zone water status, whichresults, the largest underestimation occurred when the

canopy was still in its development phase. When the soil affects surface temperature via stomatal conductance and

352 Olioso et al.

Table 2. Effect of LAI, Vegetation Height (zh), Surface SoilMoisture (h[0–5]), and Root Zone Water Potential (wg) on DailyMean Evapotranspiration (LE), Transpiration (LEv), andPhotosynthesis (Ac) Retrieved by Recalibrating ALiBi*

h[0–5] (day 236) LE LEv Ac

(m3 m23) (W m22) (W m22) (lmol m22 s21)

0.05 118.1 111.9 8.870.09* 115.8 106.4 8.490.10 113.3 102.9 8.160.15 106.2 86.7 6.690.20 97.5 56.6 3.32

LAI (day 236) LE LEv Ac

(m2 m22) (W m22) (W m22) (lmol m22 s21)

2.0 117.2 106.9 9.563.0 118.3 108.8 9.453.7* 115.8 106.4 8.494.0 116.4 107.3 8.205.0 117.9 109.8 7.48 Figure 10. Evolution of daily evapotranspiration: —56.0 122.7 115.9 7.13 MAGRET recalibration by adjusting the maximal reserve;

– –5MAGRET recalibration by adjusting the water sup-wg (day 236) LE LEv Ac plies; d5measurements.(MPa) (W m22) (W m22) (lmol m22 s21)

0 116.3 107.1 8.6920.53* 115.8 106.4 8.49

About Turbulent Exchange Coefficients21.0 112.4 102.7 7.86The sensitivity analysis of the ALiBi model recalibrationzh (day 249) LE LEv Acshowed that an accurate knowledge of vegetation height(m) (W m22) (W m22) (lmol m22 s21)was required to obtain a good estimation of fluxes. This

0.1 140.0 132.2 11.2result illustrates the significant role played by turbulent0.3 138.6 129.5 9.6exchange coefficients when employing thermal data. An-0.5 131.3 120.7 7.6

0.65* 116.1 103.9 5.1 other vision of this problem may be found in the large0.8 82.8 68.9 3.0 amount of works which assesses the effect of the thermal

* Measured value of the parameter. roughness parameter on sensible heat flux estimationfrom surface temperature (e.g., Heilman and Kanemasu,1976; Kustas, 1990; Moran et al., 1994; Zhan et al., 1996;Chebouni et al., 1997; Troufleau et al., 1997). The im-water transfers through the plants. Studies by Tardieu etportance of exchange coefficients also appeared in modelal. (1992) showed that the hydraulic conductance Gp sig-results at low LAI, when large underestimations ofnificantly changed depending on soil type and root spa-evapotranspiration were noticed. Many works havetial distributions. This explains why it is often requiredshown that these coefficients were difficult to parame-to tune the relation between the stomatal conductancetrize in partial canopy conditions (e.g., Kustas, 1990; Benand the soil moisture in SVAT models. Moreover, theMehrez et al., 1993; van den Hurk et al., 1995).soil depth explored by the roots may vary greatly during

a crop cycle. On the contrary, when a SVAT model isAbout the Possible Estimation of Surfaceinverted on parameters related to stomatal conductanceQuantities and Their Assimilation in Modelsor to the hydraulic conductance, as with the ALiBi

model, it is possible to retrieve fluxes, without using a The sensitivity analysis of ALiBi recalibration also showedrelationship between the stomatal conductance and the some model parameters, such as LAI or soil water poten-moisture in the root zone (Olioso et al., 1996b). A de- tial (wg) can tolerate some uncertainty when thermal datatailed analysis of these approaches has been presented were assimilated. This may be surprising if one considersby Chauki et al. (1997). On another hand, it is possible that these quantities should have a significant effect onthat the assimilation of surface soil moisture evolution flux simulations when using a SVAT model in a directfrom microwave data, as proposed by Calvet et al. (1998) mode (i.e., without using remote sensing data for its re-provides a way to estimate the soil moisture in the root calibration). Actually, compensation occurred betweenzone. Ben Mehrez et al. (1992) proposed elsewhere to these quantities and the parameters concerned by the re-combine microwave data and thermal data to infer the calibration (Gp in the ALiBi model). In the case of sur-relationship between surface soil moisture and soil hy- face soil moisture, an accurate knowledge was requireddraulic properties which are the determinant of soil sur- to assess fluxes originating from the vegetation layer

(transpiration), but not for the fluxes originating from theface conductance (see also Soares et al., 1988).


whole soil–vegetation system (evapotranspiration). This moisture may be possible, at least in some conditions.We also wanted to emphasize that a lot of work has stillillustrates that it is important to consider carefully theto be done on the estimation of turbulent transfer coeffi-sensitivity analysis of the recalibration procedure, insteadcients and surface conductances. The problem of esti-of the direct model, when the accuracy requirements ofmating canopy height or aerodynamic parameters (vege-the remote sensing data are determined.tation roughness) is determinant for using thermalIn the case of the ALiBi model, it will be possibleinfrared. When implementing assimilation procedures, itto estimate accurate enough values of LAI with remotewill also be important: to analyze carefully the compensa-sensing techniques to realize correct recalibrations. Thistions occurring between the different parameters or vari-will not always be possible for surface soil moisture. Mi-ables in the model, as well for choosing the quantitiescrowave sensors are a priori the more practical instru-which will be recalibrated, as to analyze type of data andments to estimate surface soil moisture (Engman andaccuracy requirements; to analyze errors due to uncer-Chauhan, 1995). However, many factors, such as vegeta-tainties in input remotely sensed variables and meteoro-tion amount or soil surface roughness, have a strong in-logical forcing variables; to account for sensor character-fluence on the measurements and significantly limit theistics, in particular to determine the possible repetitivityaccuracy of the retrievals. The assessment of vegetationof the measurements and the possible combination ofheight and aerodynamic parameters, which are very im-data from different sensors; and also to analyze scale as-portant parameters when using thermal infrared as statedpects, since high repetitivity sensors usually provide databefore, has no satisfactory solution at the present time.at a scale which is not compatible with the hypothesis ofIt may be proposed to use relationships with LAI orhomogeneous media which was used for developinga priori prescriptions depending on canopy type and veg-SVAT models. Work on effective media and model lin-etation stage. The most promising way may be to use la-earity is required to analyze this last point (Kreis andser altimeters, which provide information on the distri-Raffy, 1993; Friedl, 1996; Bouguerzaz et al., 1998). HA-bution of vegetation height with a fine resolutionPEX and FIFE types of experiment may provide frame-(Menenti and Ritchie, 1994).work to analyze some of these aspects. More recently,the Alpilloes-ReSeDA program (Prevot et al., 1998; Oli-About Meteorological Forcing and the Effect ofoso et al., 1998) has been initiated to develop and testSurface Heterogeneitymethods to assimilate multispectral/multiangular/multi-

SVAT models, as here ALiBi, often use a local meteoro- emporal remote sensing data into soil and vegetationlogical forcing, which is relevant of the studied surface functioning models (including SVAT models).conditions. To be independent of local forcing, someSVAT models, as MAGRET, have been developed in a

Many thanks are due to J. Saumade for some of the drawingsway to use data from meteorological networks. This pro- and to W. J. Capehart for providing a figure from his Ph.D.cedure is questionable since surface conditions have a thesis.strong effect on the lower atmosphere characteristics,and meteorological data from a network station may be

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Estimation of Evapotranspiration and Photosynthesis by Assimilation