Pergamon Phys. Chem .Eurth (B), Vol. 25, No. 2, pp. 159-165,2bOO

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Remote Sensing of Parameters that Regulate Energy and Water Transfer at the Land- Atmosphere Interface

Koen De Ridder

Flemish institute for Technological Research (Vito), Remote Sensing and Atmospheric Processes, Mol, Belguim.

Received 24 April 1999; revised 18 August 1999; accepted 14 September 1999

Abstract. The exchange of energy and water between the land sur-

face and the atmosphere is dominated by two parameters: the fractional green vegetation cover, and the wetness of the bare soil patches. An account is given of the way satellite remote sensing can be employed to infer these parameters, and of their integration into a land surface model to simulate the surface energy and water balance. Fractional green vege- tation cover is obtained by means of the AVHRR Normal- ized Difference Vegetation Index, adopting a simple linear relation between this vegetation index and fractional green vegetation cover. Soil moisture is retrieved using the SSM/I Polarization Difference Temperature, employing the depen- dence of the dielectric constant of the soil on water content. A demonstration is given of the implementation of these two parameters in a land surface model, running the latter for the temperate grassland area of the FIFE field experiment for a two-week period in summer. A comparison with observa- tions shows that the error on the simulated energetic fluxes is of the same order as the estimated experimental error of ground-based flux measurements. 0 2000 Elsevier Science Ltd. All rights reserved

1 Introduction

Numerous model studies have demonstrated the impact of the land surface energy balance on the atmosphere. Bel- jaars et al. (1996) found a pronounced feedback between land surface hydrology and summer precipitation in the central United States. Eltahir and Pal (1996) demonstrated the exis- tence of a relationship between surface conditions and subse- quent rainfall in convective storms in West-Africa. Recently, SchSir et al. (1999) showed that summertime European pre- cipitation depends heavily upon soil moisture content. In or- der to better understand the role of the land surface on precip- itation recycling, De Ridder (1997) performed an analytical

Correspondence to: Koen De Ridder, Vito-TAP, Boeretang 200, B-2400 Mol, Belgium, email: dridderk@vito.be

study using a slab model of the convective boundary layer. It was shown that the potential for convective precipitation over land, as represented by the equivalent potential temper- ature (e.g. Salby, 1996) increases with the surface evapora- tive fraction, which is the ratio of the latent heat flux to the sum of sensible and latent heat fluxes.

In order to represent land surface characteristics, most at- mospheric models include schemes to compute the energy balance of the Earth’s surface, which contain detailed pa- rameterizations of soil hydraulics, plant transpiration, atmo- spheric turbulence, and radiation exchange. However, de- spite the sophistication of these land surface schemes, the specification of the required input parameters that character- ize the land surface is often a neglected aspect.

Evidence from field experiments and model sensitivity studies indicates that vegetation cover fraction and soil mois- ture dominate the surface energy balance (Hall et al., 1995; Carlson et al., 1996). The only way to obtain information about these parameters with sufficient temporal and spatial sampling for large enough regions resides in satellite mea- surements. Furthermore, satellite instruments measure areal averages, which is compatible with surface grid cells of at- mospheric models. Note also that several authors have ar- gued that land surface modelling should be more closely linked to satellite remote sensing (Harding et al., 1996; Bougeault, 1997).

Many efforts have been directed towards the use of re- motely sensed radiometric surface temperature to obtain soil moisture (e.g., Carlson et al., 1995; Van den Hurk et al., 1997). In these methods, vegetation fraction cover and soil moisture are adjusted in a numerical land surface scheme un- til agreement between simulated and measured surface tem- perature is obtained. Although land surface temperature is a key variable in the surface energy balance, thermal methods suffer from the relatively poor accuracy of satellite-measured surface temperature due to uncertainty regarding surface emissivity and atmospheric transmissivity (Hall et al., 1992; Cooper et al., 1995). Another severe limitation of surface temperature-based methods is that they can only be applied

159

160 K. De Ridder: Remote Sensing of Parameters that Regulate Energy and Water Transfer

in the absence of clouds, which seriously reduces the tem- poral sampling. In recent work, Diak et al. (1995) and Teng et al. (1995) pointed out that land surface fluxes can be char- acterized by a combination of remotely sensed soil moisture and fractional vegetation cover, which is the approach fol- lowed here.

In this paper an account is given of a method to infer frac- tional green vegetation cover and soil moisture content of bare soil patches from satellite remote sensing. Furthermore, a description is given of the implementation of the retrieved parameters in a surface energy balance modelling exercise applied to the grassland area of the First International Satel- lite Land Surface Climatology Project (ISLSCP) Field Ex- periment (FIFE). The FIFE campaign took place on a 15 x 15 km2 grassland area in Kansas, USA (Figure 1). during 1987 and 1989. This experimental campaign was designed, among other, to bridge the gap between ground measurements and satellite remote sensing, and has been extensively described in Sellers et al. (1992) and Hall and Sellers (1995); the col- lected data are available on a series of CD-ROMS (Strebel et al., 1994). During FIFE, several two-week Intensive Field Campaigns (IFC) with increased instrument coverage were carried out, the ground observation stations being distributed among the different land surface types to provide an area- weighted sampling scheme. In this study use is made of data collected during IFC3, that took place from 6 to 21 August 1987 (Julian day 218 to 233). The reason for taking this pe- riod is that a major precipitation event took place on a soil that was rather dry in the upper layers, which brought about a transition from dry to wet conditions in a very short times- Pan.

The remainder of this paper is organized as follows. Sec- tion 2 discusses a methodology to infer green vegetation cover and moisture of bare soil patches from satellite remote sensing. Section 3 describes the implementation of these re- motely sensed parameters in a land surface scheme, which is then used to simulate the surface energy balance of the FIFE prairie.

2 Remote sensing of land surface characteristics

In this section green vegetation cover is derived from AVHRR imagery, and skin soil moisture from SW/I im- agery. Pre-processed AVHRR and SW/I data are available through the Internet:

- l-km AVHRR Global Land Data Set at http://edcwww. cr.usgs.gov/landdaac/lKM/lkmhomepage.html

- NOAA/NASA Pathfinder Equal Area SW/I Earth Grid Brightness Temperatures at http://www-nsidc.colorado. edu/NSIDC/CATALOG/ENTRIES/nsi-0032.html

These satellite data, required for the methods described be- low, cover long periods while providing daily global cover- age.

100 -99 -98 -97 -96 -95 - ‘<..._.. t ..,... ;q...‘....... *.:l . . . . . ..*...~__l__.~_._( . . . .._ I . . . . . . (...,_.. ~..r ..‘

. 1 * * if 1 * . :. * * .; * I * , j :..*i..*

:

-99 -98 -97 -96 -95

Fig. 1. Eastern part of Kansas, USA. The FIFE area is indicated by the grey square. The thick solid reiztsngle delimits the AVHRR scene used in this study (Figure 3). me symbols (+) represent the locations of the SSMlI grid. Degrees latitude and longitude sre indicated at the left and lower sides of the figure, respectively.

2.1 Green vegetation cover from NOAA-AVHRR

The Advanced Very High Resolution Radiometer (AVHRR) instrument has been aboard the polar orbiting satellite plat- forms of the National Atmospheric and Oceanic Adminis- tration (NOAA) for many years, providing long-term mea- surements in the optical and thermal parts of the electromag- netic spectrum. The AVHRR instrument is a five-channel radiometer measuring reflectances at 0.63 and 0.91 pm, and brightness temperatures at 3.7, 10.8, and 12.0 pm. The spa- tial resolution at nadir is 1.1 km. Although initially designed for meteorological purposes, the AVHRR has been used ex- tensively for vegetation studies. A detailed description of the AVHRR and its applications is given in Cracknell (1997). The data used in this study are level lb AVHRR images from the NOAA-9 platform, available on the FIFE CD-ROM series (Strebel et al., 1994). However, as indicated above, pre-processed AVHRR imagery is also available in the l-km AVHRR Global Land Data Set (Eidenshink and Faundeen, 1994), and in most applications this data set will be the most convenient to use.

The Normalized Difference Vegetation Index (NDVI) is defined by

with RNI*R and RRED the reflectance in the NIR (0.91 pm) and RED (0.63 pm) wavebands, respectively. The NDVI ex- ploits the fact that green vegetation exhibits a large spectral contrast between RED and NIR reflectances. The position of the AVHRR RED and NIR wavebands are shown in Figure 2, together with typical spectral curves of green vegetation and bare soil.

K. De Ridder: Remote Sensing of Parameters that Regulate Energy and Water Transfer 161

0 004 0 50 0.60 0.70 0.80 0.90 1 .OO 1 10 1 .20

h (i.Lm)

Fig. 2. Spectral reflectance of green vegetation (solid line) and bare soil (dashed line), from data presented by Price (1995). The RED and NIR wave- bands of fhe AVHRR are indicated in the upper part of the graph.

Following Wittich and Hansing (1995), the fractional veg- etation cover is related to the NDVI by

f= NDVI - NDVImi,

NDVI,,, - NDVI,i, ’ (2)

where NDVI,;, and NDVI,,, represent values of bare soil and green vegetation, respectively.

Figure 3 shows the AVHRR NDVI of a region centered on the FIFE area for Julian day 227 in 1987 (i.e., during IFC3) at 20:45 GMT, which approximately corresponds to 14:20 local time. The average value for the FIFE! area is 0.452. Rather than taking fixed values for NDVI,,,i, and NDVL,, these coefficients are extracted from the image itself. The benefit of doing so is that effects related to satellite viewing/illumination geometry and atmospheric transmissiv- ity are reduced to a minimum.

Whereas obtaining NDVI,,,,, from an NDVI image is rather straightforward, this is not the case for NDVI,,+,. The reason is that the presence of clouds or water in satel- lite imagery strongly depresses the NDVI, meaning that the lowest values in a satellite image do not necessarily corre- spond to bare soil. The approach taken here is to associate NDVI,;, with the pixel in the image with the highest sur- face temperature, the latter being contained in the AVHRR imagery as well. The minimum and maximum values thus obtained from the image shown in Figure 3 are 0.175 and 0.667, respectively. Inserting these values in (2) yields a fractional green vegetation cover of 56.3 %. Note that in the procedure outlined here the assumption has been made that NDVL,z corresponds to full vegetation cover, limiting the current method to areas where such an assumption holds.

Table 1 lists average (for the FIFE area), minimum, and maximum NDVI values obtained from AVHRR imagery cor-

Fig. 3. The AVHRR NDVI of the region centered on the PIPE area on Julian day 227 of 1987 at 20~45 GMT. Dark and light tones correspond to low and high NDVI values, respectively. The FIFE area itself is enclosed by the solid rectangle.

responding to some clear-sky days in early and mid summer 1987. Furthermore, this table contains fractional vegetation cover of the FIFE area calculated with (2). The first two lines correspond to two subsequent days (Julian days 156 and 157), and $e calculated fractional vegetation cover turns out to be very stable, demonstrating the robustness of the method. The third line corresponds to a clear-sky day at the time of maximum greening, yielding a fractional vegetation cover of 7 1.6 %. The fourth line corresponds to the day that was dealt with in previous paragraph and shown in Figure 3, and the fifth line corresponds to an image taken six days later, near the end of IFC3, with a calculated fractional vege- tation cover of 54.7 %. The approach taken here to calculate fractional vegetation cover is based on the assumptions that

1.

2.

the green fractional vegetation cover of IFC3 is the av- erage value of values calculated for days 227 and 233 (both within IFC3), that is, 55.5 %;

the decrease of green vegetation cover from 7 1.6 % (day 178, near maximum greening) to 55.5 % (IFC3 aver- age) is caused by wilting of the vegetation, implying that the difference between these figures, i.e., 16.1 %, corresponds to dead vegetation.

As a result, during IFC3,55.5 % is green vegetation, 16.1 % is dead vegetation, and the remaining fraction; constituting 28.4 %, is bare soil.

2.2 Soil moisture from DMSP-SSM/I

Microwave observations of soil moisture rely on the fact that the dielectric constant of soils changes with water con- tent, which influences the soil’s microwave polarization and

162 K. De Ridder: Remote Sensing of Parameters that Regulate Energy and Water Transfer

Table 1. Average NDVI of the FIFE area (subscript F), together with the minimum and maximum values occuring in a region centered on the FIFF site (Figure 3). The two leftmost columns give the Julian day and the time (GMT) of the satellite pass, respectively. The right column contains the fractional vegetation cover f calculated by (2).

day hour NDVIp NDVIm,i, NDVAna, f 156 21X 0.460 0.187 0.580 0.694

157 21:36 0.483 0.168 0.622 0.695

178 21:lO 0.485 0.136 0.623 0.716

227 20~45 0.452 0.175 0.667 0.563

233 21:20 0.428 0.139 0.667 0.547

emissivity characteristics (Figure 4). The Special Sensor Microwave/Imager (SSM/I), which has been aboard the po- lar platforms of the Defense Meteorological Satellite Pro- gram (DMSP) since mid-1987, is currently the only space- borne microwave imager with any potential in soil mois- hire applications (Jackson et al., 1996). This instrument is a seven-channel, four-frequency, linearly polarized, passive microwave radiometric system that measures brightness tem- peratures at 19.35,22.235,37.0, and 85.5 GHz.

The data employed in this study, NOAA/NASA Pathfinder SSM/I Level 3 EASE-Grid Brightness Temperatures from the DMSP F-8 platform, were obtained from the National Snow and Ice Data Center (NSIDC, 1998). Use is made of the vertically and horizontally polarized brightness tempera- tures at 19.35 GHz, which are the best suited for soil mois- ture retrieval (DMSP SSM/I Cal/Val Team, 1991). This fre- quency is also relatively unaffected by atmospheric moisture and clouds (Diak et al., 1995). Although the data are stored on a 25 km-grid, the measurement footprint size is around 56 km for the 19.35 GHz channels. With respect to soil moisture monitoring such a spatial resolution may seem large. How- ever, it has been shown that the horizontal scale of soil wet- ness - small-scale topography-related variability aside - is essentially similar to the scale of major rain-bearing systems that extend from tens to hundreds of kilometers (Vinnikov et al., 1996). Note also that the sensing depth of the 19.35 GHz waveband is of the order of a few millimeters only (En- gman and Chauhan, 1995). However, in the context of spec- ifying soil moisture for atmospheric models this is rather an advantage, since bare soil evaporation takes places in the up- per parts of the soil.

Recently, De Ridder (1999) developed a physically-based moisture retrieval scheme based on time series of the SSM/I Polarization Difference Temperature (PDT) at 19.35 GHz, which is the difference between the vertically and horizon- tally polarized brightness temperatures. Combining existing theories and parameterizations, the PDT was expressed as

TV - TH = (1 - f)ta(T, -T&+(1 - 24?)(I’~ -r,).(3)

In this expression, the left-hand side contains the surface mi- crowave brightness temperatures at vertical (subscript V) and horizontal (subscript H) polarization. At the right-hand side, 1 - f accounts for the portion of the SSM/I pixel that consists of bare soil. t,, T,, and Td represent, respectively, the mi-

0.35

i I

L’

0 25

Fig. 4. TbeoreticaIly computed values of FH - FV at 19.35 GHz as a function of volumetric mopisture content for a silty clay loam soil, the pre- dominant type at the FIFE area. The curve was obtained using Fresnel’s relations for the specular reflection at the air-soil boundary together with the Wang-Schmugge (1980) model for the moismre-dependent soil dielectric constant.

crowave atmospheric transmissivity, the thermodynamic sur- face temperature, and the microwave downward atmospheric temperature close to the surface. The parameters h and & describe the roughness and the depolarisation characteristics of the surface, respectively. The specular reflection coeffi- cients at the air-soil boundary, I’H and FH, are computed us- ing Fresnel’s relations together with the Wang and Schmugge (1980) model for the moisture-dependent soil dielectric con- stant. As such, (3) essentially expresses the PDT, which is measured by the SSMLI, as a function of skin soil moisture content, and the latter is obtained by inversion. This method was applied to the FIFE area for a 2-week period in August 1987. Although a straightforward validation was not pos- sible due to the different sensing depths (a few mm for the SSM/I and 5 cm for the ground-observations), it waq con- cluded that, qualitatively, remotely sensed soil moisture com- pared favourably to FIFE ground-measurements (Figure 5).

Fig. 5. Volumetric soil moisture content obtained from the SSMfI-based method (solid line) and ground-observations (dots) for FIFE-IFC3.

K. De Ridder: Remote Sensing of Parameters that Regulate Energy and Water Transfer 163

Table 2. Vegetation and soil parameter values used in the land surface model. The subscripts vis and nir refer to the visible and near-infrared por- tions of the solar spectrum respectively. The green leaf fraction is either 0 (for the dead vegetation component) or 1 (for the green vegetation compo nent). The soil parameters are those listed by Clapp and Homberger (1978) for silty clay loam, following the classification scheme of the United States Department of Agriculture (USDA).

vegetation parameters parameter description grass

zom (m) roughness length momentum 0.03

d (m) displacement height 0.20

L (-) leaf area index (LAD 4

Pvis (%) leaf reflectivity 11

Pnir (%) leaf reflectivity 58

rlJvis (o/o) leaf transmissivity 7

rnir (%) leaf transmissivity 25

9v (-) green leaf fraction Wl ro (s m-l) minimum stomatal resistance 50

‘PO.lm (-) root fraction upper O.lm soil 0.7

rp (s) internal plant resistance 5 x 10s

soil parameters parameter description silty clay loam

r)sat C-1 saturated water content 0.477

1Cl.d (4 saturated water potential -0.356 K Sat Cm s -1 ) saturated hydraulic conductivity 1.7 x 10-e

b (0 water retention curve exponent 1.75

Asd (-) dry soil albedo (broadband) 0.2

3 Simulating the surface energy balance of FIFE-lFC3

The model used for simulating the surface energy balance of FIFE-IFC3 was developed at the Institut d’Astronomie et de Geophysique Georges Lemaitre (IAGL), Universite Catholique de Louvain, Louvain-la-Neuve, Belgium. It con- tains one vegetation layer, a soil skin layer, and four subsur- face soil layers. Shortwave and longwave radiation transfer in the vegetation are calculated with the two-stream theory. Turbulent transfer computations are based on surface layer similarity theory and consider canopy-air and ground-air ex- changes separately. Plant water flow is governed by differ- ences in water potential between the soil and the leaves. The stomatal resistance formulation uses the effective leaf area index and the leaf water potential as key variables, and the re- sulting transpiration scheme implicitly accounts for the influ- ence of visible radiation, soil moisture, atmospheric satura- tion deficit, and leaf temperature. The required meteorolog- ical input consists of wind speed, temperature, and humidity at a level in the surface layer, together with the downward shortwave and longwave radiation fluxes, and the precipita- tion intensity. The output consists of the turbulent fluxes of sensible and latent heat, the soil heat flux, and the radiation balance. Full details about this model, including a descrip- tion of an extensive validation study, are given by De Ridder and Schayes (1997).

The model was run in mosaic-mode (Klink, 1995), mean- ing that energetic fluxes were calculated separately for each of the surface types (55.5 % green transpiring vegetation, 16.1 % dead non-transpiring vegetation, and 28.4 % bare soil, see Section 2.1), and averaged with weighting coeffi- cients proportional to the fraction of each of them. Instead

of letting the soil model calculate its own water balance, the following assumptions were made:

for the bare soil fraction, the volumetric moisture con- tent of the upper model soil layer (2 cm thick) was forced to take the values obtained from the method de- scribed in Section 2.2 as shown in Figure 5;

for the vegetated fraction, the volumetric moisture con- tent of the root zone was set to field capacity (X 0.322 m3 mm3), which ensured full transpiration capacity for the green vegetation.

The meteorological input consisted of site-average mea- surements (Betts and Ball, 1998). Since the FIFE area is cov- ered mainly by prairie, the parameters used to characterize the vegetation in the land surface model are those of grass. The soil type present is silty clay loam, and parameters were chosen accordingly. All vegetation and soil parameters used in the simulation are listed in Table 2.

Figure 6 shows the components of the surface energy bal- ance calculated with the IAGL land surface model using re- motely sensed vegetation fraction and skin soil moisture, to- gether with observed surface fluxes of the FIFE area during IFC3. The overall agreement is relatively satisfactory, espe- cially when considering that neither the land surface scheme nor the satellite-based surface parameter retrieval methods underwent calibration of any kind. The root mean square dif- ferences between modelled and observed fluxes are 21, 15, 22, and 31 W rnm2, for the net radiation, ground heat flux, sensible heat flux, and latent heat flux, respectively. These figures are of the same order as the experimental errors on the ground measurements themselves (Kim and Vet-ma, 1990). However, Figure 6 also reveals that the simulated fluxes ex- hibit some biases, which is particularly true for the latent heat flux during the second half of the simulated period.

4 Conclusious

A description was given of methodologies to infer fractional green vegetation cover and skin soil moisture from satellite imagery. Fractional green vegetation cover was obtained by means of a linear scaling of observed values of the AVHRR Normalized Difference Vegetation Index between minimum and maximum values that were obtained from the image it- self. Skin moisture of bare soil was retrieved using the SSMA 19.35 GHz Polarization Difference Temperature. Note that both methodologies are complementary. Indeed, in cases of low vegetation cover the AVHRR-NDVI might not give a very precise estimate of percentage vegetation cover, but in such a situation bare soil evaporation will affect the surface energy balance most, and this is precisely the circumstance under which the SSM/I yields best accuracy for soil moisture. Conversely, in case of dense vegetation cover the moisture retrieval method is less accurate, but then the surface energy balance will be dominated by vegetation transpiration, hence minimising the effect of inaccurate moisture retrieval.

K. De Ridder: Remote Sensing of Parameters that Regulate Energy and Water Transfer

FIFE IFC3 (6-21 AUGUST 1987)

218 219 220 221 222 223 224 225 226 227 226 229 230 231 232 233 234 JULIAN DAY 1987

Fig. 6. Simulated (solid line) and ground-measured (dots) surface energy fluxes for the FIFE grassland area for 6-21 August 1987. Shown are, from top to bottom, sensible heat flux, latent heat flux, net radiation, and ground heat flux.

Satellite-derived green vegetation cover and bare soil skin moisture were used in a simulation study of the surface energy balance of the FIFE grassland area during a two- week period in the summer of 1987. The overall agree- ment between modelled and ground-observed surface ener- getic fluxes was found satisfactory, although a bias towards high latent heat fluxes was observed during the second half of the simulated period.

ologies for indirect measurement of soil water content. A&c. Fo,: Mete- oral., 77,191-205, 1995.

Future work will extend the above methods to larger areas and longer time periods. Exploitation of the methods de- scribed in this paper may fully benefit from the advent of a new generation of optical (VEGETmON, MERIS, MODIS) and microwave (MIMR) satellite instruments, that generally offer higher spatial resolution, more spectral channels, and better geometric accuracy.

Carbon, T.N., W.J. Capeheart, D.A.J. Ripley, and R.R. Gillies, A partial re- evaluation of soil water parameterization in land surface models. Preprint volume, Second International Science Conference on the Global Energy and Water Cycle, 17-21 June 1996, Washington DC, 1996.

Clapp, R.B., and G.M. Homberger, Empirical relations for some soil hy- draulic properties. Wuter Resour: Res., 14,601-604, 1978.

Cooper, H.J., E.A. Smith, and W.L. Crosson, Limitations in estimating sur- face sensible heat fluxes from surface and satellite radiometric skin tem- pe.ratures. J. Geophys. Res., 100,25419-25427, 1995.

Cracknell, A.P., The Advunced Very High Resolution Rudiometer, Taylor & Francis, London, 1997.

De Ridder, K., Land surface processes and the potential for convective pre- cipitation. J. Geophys. Res., 102, 30085-30090, 1997.

De Ridder, K., Soil moisture monitoring with the SSM/I. Summed to Re- mote Sens. Environ., 1999.

De Ridder, K., and G. Schayes, The IAGL land surface model. J. Appl. Meteonl., 36, 167-182, 1997.

Acknowledgements. The FIFE data were obtained from the FIFE System Disk, G.R., R.M. Rabin, K.P. Gallo, and C.M. Neale, Regional-scale com- Information Staff, and the SSM/I imagery was provided by the National Snow and Ice Data Center. Alan Betts gave helpful comments on the site-

parisons of vegetation and soil wetness with surface energy budget prop-

average ground data, and Jeffrey Newcomer on the AVHRR-LAC imagery. erties from satellite and in-situ observations. Remote Sensing Reviews, 12,355-382, 1995.

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