Semi-Annual Status Report

Period: March 1987-March 1988

to

National Aeronautics and Space Administration

NASA Grant No. NAG 5-919 j

AN UPDATE ON REMOTE MEASUREMENT OF SOIL MOISTURE OVER VEGETATION

USING INFRARED TEMPERTURE MEASUTEMENTS:

A FIFE PERSPECTIVE

Submitted by:

Toby N. Carlson

Department of Meteorology

The Pennsylvania State University

University Park, PA 16802

(KASA-CB-182926) AN OPDAIE CE BEH01E H88-241C9

EIASOfiEMBNT OF SCIL flCISTUBE CVEB VEGETATJOS

DSING INFBABED 1EKPEBAIDBE HIiSUEEBEUTS: A

IJfE fEE£PEC'IIVl Semiannual Status fieport, Oaclas

Bar. 1S67 - Har. 1S88 (Pennsylvania State G3/43 0146127

June 1988
ABSTRACT

We are undertaking a multifacet research effort consisting of model

development, image analysis and micrometeorological measurements. The object

of our research is to push beyond the present limitations of using the

infrared temperature method for remotely determining surface energy fluxes and

soil moisture over vegetation.

Model development consists of three aspects: (1) a more complex

vegetation formulation which is more flexible and realistic, (2) a method for

modeling the fluxes over patchy vegetation cover and (3) a method for

inferring a two-layer soil vertical moisture gradient from analyses of

horizontal variations in surface temperatures. . In the future, we will use

HAPEX and FIFE satellite data along with aircraft thermal infrared and solar

images, as input for the models. To test the models, moisture availability

and bulk canopy resistances will be calculated from data collected locally at

the Rock Springs experimental field site and, eventually, from the FIFE

project.
1

1, Background

For over ten years we have been interested in remote measurement of soil

moisture and surface energy fluxes using thermal infrared temperatures. We

began this investigation because of the need for providing initial soil

moisture values for atmospheric prediction models. Our technique is

conceptually very simple: a one-dimensional, time-dependent boundary layer

model is inverted in conjunction with measured radiometric surface

temperatures, obtained by satellite, aircraft or surface-based radiometers.

The model then simulates the surface fluxes, soil moisture availability and

thermal inertia. We have applied this method rather successfully to regions

ranging in size from a large city up to that of a state (Carlson et al.,

1981; Carlson et al., 1984; Carlson, 1986; Flores and Carlson, 1987). The

method demonstrates a high correlation between antecedent precipitation and

the soil moisture parameter, which is the moisture availability. Similar

results were obtained by Wetzel and his co-workers (e.g. Wetzel and Woodward,

1987).

By 1984, however, it became clear to us that in order to advance our

method beyond its current level of scientific utility, a very great deal of

additional thought and experimentation (both in terms of modeling and

measurement) would be required to overcome some serious conceptual

difficulties. The problems lie in the complex nature of the surface canopy,

particularly vegetated surfaces, and the fundamental inadequacies of

mechanistic models for representing plant behavior. Vegetated surfaces are

difficult to model because parameters are subject to the needs of a living

community of plants with differing architecture and phenological behavior.

Moreover, the energy fluxes over plant canopies are modulated by the nature

of the soil surface below and the density and fractional cover of the
vegetation. One realizes that the resolution of such complexity is elusive

because the methods available are called upon to unscramble the information

from just a few available measurements.

Beginning in 1984 we undertook to model vegetation. Cooperation with

French scientists resulted in the publication of a paper concerning the

development of a vegetation model (Taconet et al., 1986). Results of this

paper and subsequent analyses led to some interesting conclusions. First,

the vegetation parameterization improved estimates of surface energy fluxes

(and, with less certainty, the soil moisture) for a wheat canopy in a

controlled experiment involving a combination of ground micrometeorological

measurements and satellite data. It was clear, however, that our ability to

calculate reasonable values of evapotranspiration and surface sensible heat

flux, given the accuracy of the initial conditions supplied to the model, is

intimately dependent on the correct formulation of the stomatal resistance

function and a knowledge of the amount of vegetation present. Second, due to

inherent errors, changes in surface energy fluxes or soil moisture cannot be

resolved except during a water limitation phase of drying. Perry and Carlson

(1988) show that uncertainties in the model and in the ground measurements

impose an inherent and irreducible error on the infrared technique. They

suggest that a certain minimum surface temperature variance must be present

in order to calculate meaningful values of surface energy fluxes. The

situation is even more precarious over vegetation than bare soil,

particularly when the object is to measure spatial variability of surface

fluxes. Not only are plants complex in their architecture, but they are able

to regulate the water loss and so maintain a high level of transpiration

until water limitation is reached. Thus, the measured surface temperatures

or surface fluxes hardly change with changing soil moisture if the plants are
not in a state of water limitation. Numerous experimental investigations

(e.g. Turner, 1974; Turner, 1975; Thomas et al., 1975; Fisher et al., 1981;

Stewart and Dwyer, 1983; Dwyer and Stewart, 1984) show that the stomatal

resistance increases very rapidly (with associated collapse in transpiration)

as the substrate water content begins to reach a water limitation threshold.

The significance of this water limitation threshold for remote sensing is

that variations in leaf temperature theoretically become detectable by

satellite or aircraft radiometers at soil water content near or below the

water limitation threshold.

Various models based on direct experimental evidence (e.g. Jarvis, 1976;

Federer and Gee, 1976; Federer, 1979; Choudhury, 1983; Avissar et al., 1985)

describe the exponential behavior of bulk stomatal resistance (rst) near a

drying threshold and also the effect of other external parameters, such as

sunlight, vapor pressure deficit and air temperature. (Sunlight also exhibits

a threshold for stomatal resistance, but the effect is not important since it

occurs at low values of insolation.) Unfortunately, the parametric form of

the models differs widely from one model to the next and the coefficients in

these models are poorly known or pertain to one specific species, climate and

set of soil conditions. Present lack of knowledge of these parameters is

understandable in view of (1) the mechanistic nature of models that attempt

to describe the behavior of living entities in terms of simple formulae and

(2) the sensitivity of the specified parameters to phenology, soil conditions

and the past history of the plant.

2. Current research objectives

We view the present state of modeling surface energy fluxes and substrate

water content over vegetated canopies as dependent on carrying it out the
following projects: (1) The specification of the stomatal and xylem

resistance functions and the drying threshold for a few broad classes of

vegetation, (2) the modeling of a mixed canopy in which substantial areas of

bare soil and leaf surfaces are visible, (3) the determination of the

biomass, leaf area index or percentage vegetation cover and (4) the gathering

of micrometeorological measurements for model testing. These four items

constitute integral components of our proposed work. Below, we address them

in presenting an overview of research carried out during the previous year,

a) Modeling stomatal resistance

The basic framework of our boundary layer model is described in Carlson

(1986) and Taconet et al. (1986). Recently, we have reformulated the plant

model to include (1) a more realistic stomatal resistance function and (2) a

more comprehensive and precise link between stomatal resistance, plant-soil

potential differences and substrate moisture. This model architecture is

outlined in Fig. 1. The main point of departure with our earlier vegetation

model is the inclusion of a xylem (stem plus root) resistance and a flow of

moisture from soil to root. Two equations are necessary to describe the flow

of water from the soil to the atmosphere through the plants. One is the leaf

transpiration equation

, .

Vf ' r +,„

st af

and the other is the xylem water flow equation

Z + Z (2)

P §
where LeEf is the transpiration, rst is the stomatal resistance, e.,(T,) the

saturation vapor pressure at the temperature of the leaf (T.), qaf the

interplant air space specific humidity, raf the resistance to water vapor

flow from the leaf to interplant airspaces, Zp the resistance to liquid water

flow through the stem and roots (the sum of Zstem and Zroot), fy the leaf

water potential, $„ the ground water potential, Zg the resistance to water

flow from soil to root, rg the resistance to the flow of water from soil to

air and LeEg the soil evaporation; the symbol 8 (with various subscripts)

refers to the volumetric liquid water content in the subsoil at various

substrate levels. (Other symbols are not relevant to this immediate

discussion.) In (2) the flow of water from soil to leaf is prescribed in

terms of the appropriate resistances, the leaf-ground water potential

difference corrected for the gravitational decrease of the leaf-ground

potential drop (which is a function of the leaf height above soil (h) and the

density of water pw).

The bulk stomatal resistance (rst) and the average leaf resistance to

water vapor flux (rs) are given by the following expressions:

rg = aV (f^) f(S) (3)

r = r /LAI

st s

where A is a constant, V is the vapor pressure deficit in the atmosphere, LAI

is the leaf area index and the functions f('J'p) and f(S) describe the effect

of leaf water potential and solar radiation on rst. Equations (1) and (2)

resemble those used by Sellers (1985), Wetzel and Chang (1987), Federer and

Gee (1976), Federer (1979) and Jarvis (1976).
Our formulation, though superficially similar to versions in the

literature, differs somewhat in that we equate (1) and (2) and, given the

soil moisture content and the function (3), we calculate LeEf, ty, and ^R.

Fig. 2 illustrates the exponentiality of the f(iK) function and its effect on

X>

limiting the transpiration. Neglecting gravitational effects, transpiration

is proportional to the difference between the ground and leaf potentials (4/g

and 4O- The function f(iK) increases very rapidly with decreasing ty at the

JC J6 J6

right-hand side of the curve. The function form of f(i|;0) is related to the

J6

ground water content (6) via a characteristic relationship between 9 and $„,

By linking 6 with ty and ty , we have a consistent method for relating

substrate water content to radiometric surface temperature and moisture

availability.

Our version of (3) is somewhat similar to that of Avissar et al., (1985)

except that it is expressed in terms of linear functions, rather than

exponentials. For vapor pressure deficit the function increases in

proportion to V. For f(tK) and f(S), the linear function captures the

threshold effect by specifying the functions as straight lines but with very

different slopes on either side of the threshold; the linear model is

represented by the thin dashed lines in Fig. 2. This simplification allows

us the flexibility to more easily fit the function (equation (3)) to vastly

differing types of field data, while capturing the essentials of the

exponentiality. Moreover, this version allows us to solve analytically for i/>.

and the transpiration.

Although the canopy structure represented in Fig. 1 and its governing

equations appears to be unresolvable within the context of remote sensing

applications, (because of a proliferation of additional constants that are

unknowable), the solution for ip ipg and for LeEf are critically dependent on
only one parameter, which is the leaf potential threshold, denoted as <|>c in

Fig. 2. As stated above, this threshold governs the rise of leaf temperature

with decreasing substrate water content. Some experimental evidence is

available for choosing realistic values of the critical tyc by species (Korner

et al., 1979).

Our present objectives with regard to the vegetation parameterization

have been to (1) finish sensitivity tests of the formulation based on the

above equations, (2) test the model with local (single point) experimental

measurements, (3) determine realistic values of the parameters that are

appropriate for 3 or 4 major classes of vegetation under differing

atmospheric conditions and (4) use the model in larger-scale applications,

b) Workshop on stomatal resistance

A proposed workshop on stomatal resistance has occupied much of our time.

This workshop will be sponsored jointly be Penn State (using funds from this

grant) and by the Remote Sensing Laboratory, Agriculturall Research Service,

Beltsville, Maryland. The goals of the workshop are necessarily very narrow:

(1) to foster cooperation between modelers and experimenters involved in the

use or development of stomatal resistance functions for larger-scale and

remote sensing applications and (2) develop a more systematic methodology for

the parameterization of stomatal and plant resistance in boundary layer

models. An important objective is to maintain an intimate atmosphere at the

workshop in order to encourage exchange of ideas. For that reason, the size

of the workshop will be limited to not much more than 35 participants. A

projected end product of the workshop will be a dedicated volume in

Agriculture and Forest Meteorology devoted to summarizing the state of the art

in modeling stomatal and plant resistance. We view this workshop as a major

contribution to this subject.
8

The invitees comprise some of the most distinguished scientists in the

field of modeling and measurement of stomatal and plant resistances. An

significant fraction of our time has already been spent in planning the

workshop. We are currently seeking additional funds from the NSF to cover

participant travel,

c) Modeling the mixed canopy

Due to the differences in the rate of drying of a shallow bare soil

surface versus a deep root-zone layer, radiometric surface temperatures may

differ markedly between bare soil and vegetation even if vertical profiles of

soil moisture are identical. We can illustrate this idea in the radiometric

measurements made during the French HAPEX experiment. Fig. 3 shows the

radiometric surface temperatures over an area comprising bare plowed fields

(field Nl), a young corn crop (field N6) and a full oats crop (field N2).

Differences in temperature between bare soil (55-58°C), partial cover

(47.7°C) and the oats (30.7°C) is very striking. Stomatal resistance

measurements made in field N6 show that the young corn crop was not

experiencing water limitation in the root zone (Fig. A); moreover, there was

virtually no temporal variation in the minimum daily value of rst with

changing water content in the root zone; an indication that the plants were

not experiencing water stress.

On the other hand, the bare soil surface became desiccated after only

about two weeks without significant rainfall or irrigation. Gravimetric

sampling shows that the water content very near the surface was almost zero

in various fields. Below the top 5 cm, however, the soil was relatively wet

and certainly well above any water limitation threshold (the wilting point),

which was probably near 10%. The composite vertical profile of soil moisture

for the 16th of June is shown in Fig. 4.
It is clear, therefore, that horizontal variations in radiometric surface

temperature are highly modulated by the amount of vegetation present (more

precisely, the percent of unshaded bare soil cover visible to the

radiometer). The boundary layer model, no matter how clever the vegetation

formulation, must take account of this effect. Unfortunately, as stated by

Lindroth and Halldin (1986), one-dimensional vegetation parameterizations are

likely to fail as the leaf area index decreases to values of about 1.0.

Methodology

Rather than treat surface inhomogeneity as a cause for despair, we are

endeavoring to extract information concerning the vertical profile of soil

moisture from horizontal variations in surface temperature. This idea of

deriving a two-level vertical profile of soil moisture from horizontal

variations of surface temperature is new and has not been fully tested. (We

should point out that the problem of inhomogeneous surface temperatures is

not the same as discussed by Wetzel and Chang (1987).) Let us imagine a

sample of pixel measurements made in the thermal infrared and at various

solar wavelengths over a surface such as that shown in Fig. 3. Surface

temperatures are calculated from the thermal infrared radiances and leaf area

index values from the solar radiances (the technique of converting visible

radiances to leaf area index is discussed below). Preliminary inspection of

the HAPEX data suggests a distribution of surface temperature (Ts) versus

leaf area index (LAI) such as that shown in Fig. 5.

A first step in the solution of this problem was to relate visible

channel radiance, specifically the normalized difference vegetation index

(NDVI), to the leaf area index (LAI), which serves as a measure of the amount

of vegetation in our boundary layer model. We perform this comparisons over
10

relatively homogeneous surface areas such as that shown in Fig. 3. We are

currently exploring the use of various formulae relating NDVI to LAI,

including that of Bauer et al. (1985), and comparing them with our own

function derived from aircraft and ground-based measurements made during

HAPEX.

The next step is to derive the functional relationship between LAI and

fractional vegetation cover in the model. Consider a distribution of NDVI

versus LAI as in Fig. 5. Given a range of vegetation cover over relatively

uniform type of land surface, we extrapolate the curve of NDVI versus LAI

through the ordinate of the graph (for which LAI = 0) to obtain the bare soil

temperature, TDS. Leaf temperature is given by the asymptotic temperature

Tf. (The significance of these two temperatures, that of bare soil and leaf,

is discussed below.)

In principle, the value of Tf occurs at infinitely large leaf area

index. Sellers' (1985) work, however, shows that the amount of upwelling

radiance rapidly approaches saturation above a leaf area index of about 2-3.

We suggest that the canopy functions as a homogeneous cover in the asymptotic

part of the curve and as a partial cover in the region where LAI changes

rapidly with NDVI. We illustrate the relevance of this remark in regard to

Fig. 5. Above LAI* the canopy acts as a complete vegetation cover, a "big

leaf" model in which the density of leaves controls the penetration of

radiance to or from the ground surface beneath the vegetation cover. Holes

exist in the vegetation but they are small an uniformly distributed. Below

LAI* the canopy no longer behaves as a big leaf but as a series of big leaves

and bare soil patches with some fractional vegetation cover; the latter may

be a function of LAI.
11

At LAI less than LAI*, we run two models simultaneously, that for bare

soil and that for vegetation with a value of LAI in the vegetation patches

equal to some representative value, e.g. LAI*. The functional relationship

between LAI (or NDVI) and fractional vegetation cover is determined

empirically as solutions to the boundary layer model for values of LAI less

than LAI*.

We should point out that this technique may not readily apply to

low-resolution satellite imagery, because the a wide range of fractional

vegetation is unlikely over any small, homogeneous region. Its value,

however, is that the use of aircraft data may allow us to derive general

functional relationships between LAI (or NFVI) and the fractional vegetation

cover and thereby calculate the surface energy fluxes and the substrate

moisture content for any value of LAI and surface temperature, whether the

two scales (that of satellite and aircraft) can be meshed remains to be seen

from future research.

Given the information in Fig. 5 we can calculate to soil water contents,

one appropriate to the temperature of a bare soil surface (T^g) and the other

for the root zone (Tf). Further, it will be interesting to relate the

derived fractional vegetation coverage function with the value of LAI for

different states of vegetation development. Of course, we may not always

obtain a systematic relation between LAI and Ts as depicted in Fig. 5.

Absence of such a relationship simply means that the surface and root zone

moisture values do not differ significantly or that the range of LAI over the

domain is very small. Scatter may be due also to a dependence of Ts on other

factors such as soil moisture or roughness.

Preliminary results for the 16th of June 1986 suggest that soil moisture

values close to zero are necessary in the model in order to yield the high
12

temperatures of bare soil fields in Fig. 4. In contrast, high soil moistures

are required to produce the cool temperatures of the full oats canopy in field

N2. Temperatures in field N6 (leaf area index of 1.8) reflect a mixture of

bare soil and vegetation.

Deviation of leaf area indices from visible radiances

Let us now address the idea of deriving LAI values from visible

radiances. Temperature values in Fig. 3 that lie between extremes reflect

differing amounts of vegetation. Fig. 6 shows measured and derived leaf area

indices as a function of time over field N6 during the HAPEX experiment.

Derived leaf area indices were obtained by Taconet (private communication)

from two solar channels for AVHRR by computing a normalized difference

vegetation index (NDVI) by a regression equation of Bauer et al. (1985). The

large discrepancy between derived and measured leaf area indices in June is

due to contamination by surrounding bare soil pixels.

3. Image analysis

Much of our effort during the past year of the grant has been devoted to

completely revising our capabilities at image analysis and model execution.

Presently, we have in operation a fast and efficient image processing and

modeling work station. Image analysis is performed using the ERDAS system,

which has proved to be effective in use by other remote sensing groups; (Fig.

3 was produced by the ERDAS software). The system that drives the ERDAS is an

AT&T 6300 computer which is serviced by two 20 megabyte removable disks. This

system also executes the model and interfaces it with the image values.

We are analyzing aircraft images of temperature and vegetation index

(derived leaf area index) for several days during HAPEX. Infrared

temperatures can be determined from the TIMS instrument, which measures
13

infrared radiances, and the vegetation indices from the NS001 which measured

solar radiances. With the kind cooperation of Thomas Schmugge we have

received the necessary image data and have begun to analyze all the data for

several days during June, 1986. In addition, we have received the AVHRR data

from France and we are considering a reworking of Taconet's results given by

the LAI(SAT) curve in Fig. 6 in order to see if a better fit with surface

measurements can be obtained.

We are examining the water vapor corrections in order to further

substantiate the extraordinary high temperatures of the bare soil areas.

Eventually, we will examine all the available image data for selected days

during HAPEX, derive surface temperatures, leaf area indices and their

histograms and test our new ideas and model formulations as discussed above.

When the data becomes available, we will investigate the possibility of using

the 1987 FIFE images and surface measurements to study the problem of sparse

vegetation.

4. Micrometeorological measurements

Virtually every value of every parameter in any boundary layer model,

whether one-dimensional or three dimensional, has been derived from point

measurements. We regard such measurements as vital in model development. An

effort, therefore, has been devoted to obtaining supplementary

micrometeorological parameters at an agricultural site, called Rock Springs,

which is near Penn State. This measurements program was developed by

colleagues to study dry deposition, but has been expanded through our efforts

to measure soil moisture, radiometric surface temperature and certain plant

parameters: leaf area index and (starting this summer) leaf water potential

and leaf stomatal resistance. During the summer we measure surface energy
14

and momentum fluxes by a variety of systems. Each year the crop changes:

wheat in 1986, corn in 1987 and soybeans in 1988.

These measurements are highly relevant to the problem of remote sensing

of surface energy fluxes. A fundamental problem in remote sensing is that the

latter is capable only of sensing the effects of a bulk canopy resistance to

water vapor flux. We instituted radiometer measurements from a mast. These

measurements will allow us to calculate surface energy fluxes and the bulk

canopy resistance to moisture flux, rc. This canopy resistance (rc) differs

from stomatal resistance but the two are very closely related in the case of

a full vegetation cover (Taconet et al., 1986; Lindroth and Halldin 1986).

In Fig. 1, rc represents the net resistance of the vegetation canopy and soil

to water vapor flux. For large values of LAI rc approaches rst. A current

contentious issue in plant micrometeorology is the relationship between rs,

rS£ and rc. We hope that our modeling combined with field measurements will

help to resolve this issue.

We are currently developing programs that will allow us to calculate from

measurements, parameters directly related to model development and testing.

Preliminary results from the 1986 data are shown in Fig. 7. Note that rc

varies slowly with changing soil water content at high values of the water

content; the figure suggests an exponential increase of rc as the surface

water contents decrease below about 0.18 by volume. (The wilting point of

the soil has been determined independently to be about 0.12.) Since the leaf

area index was not factored into this graph, we cannot be sure if the curve

is partially an artifact due to variation in vegetation cover. Similar

curves exist for moisture availability.
15

5. Related Activities

a) Total daily evapotranspiration

One interesting study carried out as part of our present NASA grant was

to investigate a method proposed by Jackson (1977) for obtaining the total

daily evapotranspiration from remote measurements of the surface temperature

near local noon. Stated briefly, the Jackson "B formulation" is written

R~ - LeE = B(T -T )n (4)

n o o a ^ '

where B and n are constants, To the surface radiometric temperature, Ta the

air temperature, LeE the 24 hour evapotranspiration (in Wm ) and Rn the 24

hour integrated net radiation. Our study concerned an analysis of the n and

the B values as functions of wind speed, roughness, vegetation cover and

altitude of the air temperature measurement. These results are summarized in

a paper, to be submitted for publication, by Carlson and Buffum (1988?).

b) Urban areas

Although we have not been concerned with remote measurements of surface

parameters over urban areas for several years, we have had the occasion to

return to the periphery of this topic through the interaction with a visiting

scientist, Mr. Robert Gillies, who is jointly working on a doctorate in the

department of Building Science, the University of Newcastle upon Tyne, where

he is currently a staff member. He is also studying for his Master's degree

in meteorology from Penn State. Mr. Gillies, who is independently funded,

has arrived at Penn State expressly to work with our group and he will be

developing our work station capabilities to include analyses of HCMM and NOAA

images for various British cities.
16

6. Conclusion

Our efforts have been to (1) resample the visible channel radiances from

NS001 data to the infrared temperatures derived from the TIMS data for 4 days

during HAPEX. This operation will allow us to prepare diagrams such as Fig.

5, either from our own measurements or using empirical equations from the

literature (2) develop the boundary layer coding the model for calculating

soil moisture and surface fluxes for partial vegetation cover, (3) test the

model using HAPEX data (TIMS; NS001) to evaluate the efficacy of the method

and (4) use leaf area index values in conjunction with surface temperatures to

derive a fractional vegetation cover as a function of surface temperature in

the model, and (5) obtain two-layer (surface; root zone) soil moisture

profiles over patchy vegetation.
17

Figure Captions

Fig. 1 Schematic illustration of plant canopy architecture and interface

with atmospheric boundary layer above, as employed in current version

of 1-dimensional boundary layer model. Fluxes of water vapor (LeE;

left and center streams) and sensible heat (H; right stream) pass

from the ground (subscript g) or the leaves (subscript 1) to the

surface layer through air resistances (denoted by letter r with

subscripts a, b, af and ag) or stomatal resistance (rst-vapor only).

Water vapor passes from the soil to the air through resistance rTM.

Liquid water passes from the root zone to the leaves across the

hydraulic potential gap between 4»g (ground) and^i (leaf). Root and

stem (xylem) resistances are combined as Zp. Substrate water

contents are denoted by the symbol 0 and the soil surface temperature

by Tg. Leaf temperature and vapor pressure are denoted by the

symbols TI and ej_, respectively.

Fig. 2 Schematic representation of the leaf potential function

versus the leaf potential 0|>]_; solid curve), showing also the

increase in the function as the leaf potential falls below that of

the ground (i|>g) after sunrise. The leaf transpiration (LeEf) is

proportional to the difference between leaf and ground potentials

(via equation (2)). The dashed line represents the two-slope linear

approximation to the exponential used in our model . The thin

dotted line indicates the water limitation threshold potential

Fig. 3 Thermal image of Lubbon region in France in grey scale (white is

warm and black is cold) . Numbers are the mean field temperature in

degrees C.

Fig. 4 Vertical distribution of volumetric soil moisture at Lubbon on 16

June 1986, as measured by gypsum blocks in fields N6 (corn) by

neutron probe (asterisks) and by gravimetric sampling in field N5

(corn). The vertical bars at the bottom represent averages of 0-5 cm

gravimetric water content samples in four fields. The dashed line

suggests the mean vertical soil moisture profile.

Fig. 5 Schematic distribution of radiometric surface temperatures versus

leaf area index derived from normalized vegetation index values for

individual pixels (dots) and smoothed relationship (dashed line).

The bare soil temperature is T^s , the threshold temperature for

patchy vegetation is T* (the equivalent of LAI*) and the bulk leaf

temperature is Tf.

Fig. 6 Leaf area index derived from normalized difference vegetation index

(NDVI) measured by AVHRR as a function of date for field N6

(LAI(SAT)) and from direct measurement (LAI). The vertical arrow

below denotes 16 June. Values along LAI curve refer to height of

corn in meters.
18

Figure Captions (continued)

Fig. 7 Bulk canopy resistance to water vapor flux (s m"1) as a function of

volumetric water content at 5 cm depth for wheat during the summer of

1986 at Rock Springs.
19

References

Avissar, R., P. Avissar, Y. Mahrer and B.A. Bravado, 1985: A model to

simulate response of plant storvata to environmental conditions.

Ag. and Forest Meteor.. 34, 21-29.

Bauer, H.E., K.P. Gallo and C.S.T. Daughtry, 1985: Spectral estimation of

absorbed photosynthetically active radiation in corn canopies. Remote

Sensing of Environ.. 17. 221-232.

Carlson, T.N., 1986: Regional-scale estimates of surface moisture

availability and thermal inertia using remote thermal measurements.

Rem. Sens. Rev.. 1, 197-247.

Carlson, T.N. andM.J. Buffum, 1988(7): On estimating the daily

evapotranspiration from remotely sensed temperature measurements (to be

submitted to J. Glim, and Appl. Meteor.) .

Carlson, T.N., J.K. Dodd, S.G. Benjamin, J.N. Cooper, 1981: Remote

estimation of surface energy balance, moisture availability and thermal

inertia. J. Appl. Meteor.. 20, 67-87.

Carlson, T.N., F.G. Rose, E.M. Perry, 1984: Regional scale estimates of

surface moisture availability from GOES satellite. J. Agron.. 76. 972-

979.

Choudbury, B., 1983: Simulating the effects of weather variables and soil

water potential on a corn canopy temperature. Ag. Met.. 29. 169-182.

Dwyer, L.M. and D.W. Stewart, 1984: Indicators of water stress in corn.

Can. J. Plant. Sci.. 64, 537-546.

Federer, C.A., 1979: A soil-plant atmosphere model for transpiration of

availability of soil water. Water Resources Res.. 15. 555-562.

Federer, C.A. and G.W. Gee, 1976: Diffusion resistance and xylem potential

in stressed and unstressed northern hardwood trees. Ecology. 57. 975-

984.

Fisher, M.S., D.A. Charles Edwards and M. Ludlow, 19 : An analysis of the

effects of repeated short-term soil water deficits on stomatal

conductance to carbon dioxide and leaf photosynthesis by the legume

macroptilium atropurpureum cv. Siratro. Aust. J. Plant Physiol.. fJ,

347-357.

Flores, A.L. and T.N. Carlson, 1987: Etimacion de parametros del suelo

combinando un modelo de capa limite con imagenes digitales de GOES.

Meteorologica. (Argentina), XV, 77-92.

Flores, A.L. and T.N. Carlson, 1987: Estimation of surface moisture

availability from remote surface temperature measurements. J. Geophvs.

Res.. 92, 9581-9585.
20

Jarvis, P.G., 1976: The interpretation of the variations in leaf water

potential and stomatal conductance found in canopies in the field.

Phil. Trans. R. Soc. London. B, 273. 593-610.

Korner, C., J.A. Scheel and H. Bauer, 1979: Maximum leaf diffusive

conductance in vascular plants. Photosynthesis. 13. 45-82.

Lindroth, A. and S. Halldin, 1986: Numerical analysis of pine forests

evaporation and surface resistancy. Ag. and Forest Meteor.. 38. 59-79.

Perry, E.M. and T.N. Carlson, 1988: A comparison of active microwave soil

water content with infrared surface temperatures and surface moisture

availability. To be published in Water Resources Research.

Sellers, P.J., 1985: Canopy reflectance photosynthesis and transpiration.

Int. J. Remote Sensing. 6, 1335-1375.

Stewart, D.W. and L.M. Dwyer, 1983: Stomatal response to plant water

deficits. J. Theor. Biol.. 104. 655-666.

Taconet, 0., T.N. Carlson, R. Bernard, D. Vidal-Madjar, 1986: Evolution of a

surface/vegetation model using satellite infrared surface temperatures.

J. Glim, and ADD!. Meteor.. 25, 1752-1767.

Thomas, J.C., K.W. Brown and W.B. Jordan, 1976: Stomatal response to leaf

water potential affected by preconditioning water stress in the field.

Agron. J.. 68, 706-708.

Turner, N.C., 1974: Stomatal behavior and water status of maize, sorghum,

and tobacco under field conditions. II. At low-soil water potential.

Plant Phvsiol.. 53, 360-365.

Turner, N.C., 1975: Concurrent comparisons of stomatal behaviors, water

status, and evaporation of maize in soil at high or low water potential.

Plant Phvsiol.. 55, 932-936.

Wetzel, P.J. and J.T. Chang, 1987: Concerning the relationship between

evapotranspiration and soil moisture. J. Climate and Appl. Meteor.. 26.

18-27.

Wetzel. P.J. and R.H. Woodward, 1987: Soil moisture estimation using GOESVISRR infrared data: A case study with a simple statistical method. J.

Glim, and Appl. Meteor.. 26, 107-117.
21

•of

Atmospheric Boundory Loyer

Fig. 1 Schematic illustration of plant canopy architecture and interface

with atmospheric boundary layer above, as employed in current

version of 1-dimensional boundary layer model. Fluxes of water

vapor (LeE; left and center streams) and sensible heat (H; right

stream) pass from the ground (subscript g) or the leaves (subscript

1) to the surface layer through air resistances (denoted by letter r

with subscripts a, b, af and ag) or stomatal resistance (rst-vapor

only). Water vapor passes from the soil to the air through

resistance rg. Liquid water passes from the root zone to the leaves

across the hydraulic potential gap between $z (ground) and $]_

(leaf). Root and seem (xylem) resistances are combined as Zp.

Substrate water contents are denoted by the symbol 8 and the soil

surface temperature by Tg. Leaf temperature and vapor pressure are

denoted by the symbols T^ and ej, respectively.
22

Fig. 2 Schematic representation of the leaf potential function

versus the leaf potential (i^; solid curve), showing also the

increase in the function as the leaf potential falls below that of

the ground (4»g) after sunrise. The leaf transpiration (LeEf) is

proportional to the difference between leaf and ground potentials

(via equation (2)). The dashed line represents the two-slope

linear approximation to the exponential used in our model. The thin

dotted line indicates the water limitation threshold potential

(*c>-
23

ORIGINAL ?£G IS

OE POOR QITALFT32

Fig. 3 Thermal image of Lubbon region in France in grey scale (white is

warm and black is cold). Numbers are the mean field temperature in

degrees C.
24

DKKHNAE PAGE IS

OE POOR QUALITY

N2 oats

N4\

N5/Corn

NIE bare

1

*

.gypsum block (interpolated)

gravimetric (NASA)

neutron probe (INRA)

gravimetric (NASA)

corn (N5)

June 16

1

^m-L i i i i i i i* M

T^*** ~

r

*j

i

/

4/ccc m

"*" "

1

1

IIP, , , ,

ou

• f^

40

30

height

(cm)

20

10

n

0. 0.2 0.3 0.4

ev

Fig. A Vertical distribution of volumetric soil moisture at Lubbon on 16

June 1986, as measured by gypsum blocks in fields N6 (corn) by

neutron probe (asterisks) and by gravimetric sampling in field N5

(corn). The vertical bars at the bo "on: represent averages of 0-5

cm gravimetric water content samples in four fields. The dashed

line suggests the mean vertical soil moisture profile.
2

5

i

1-1o

SB <4-l 30) Ul 0> 3 -

*4) U C O

f<-J

(t > «W X 3 o> AJ T) <a c V4 -H «cb c a o 4) i-l 4J 4-1« « 4J U 0) CO 60

<4-l 4) M > 30) TJ« u N

-O l-i V 3 X w en to a )-i -o »E

C

01 -O •)<

C rl ll o O <

•«-! X J

iJ Wto 01 >4-l _( M O <U ,C VJ 4J *JC *a a> v

4) B O • v4

8 VJ O W 3

o o B & a-

•^ C m H 4)

4 S T3 0) 4>

)j O C -^ £

ui a u

(w IH 4) ~~*

o ^* ii •O Bl 3 * C « 4J 4J H O > O <8

34) a .o -o <n s c <*-i •^ r-l 41 O H 4J 4) X *J « •O —< O 41 4) y q) <g 41 3 •^ 4) 3 4) > iJ t) l-l ID l-i « « « 1-1 a) >s w e > & .c 41 4> in -^ u a j: <0 -O 4) U E o a> c DO i-tb. « « O. u
26

ORIGINAL PAGE IS

OF POOR QUALITY

2 -

I -

5 10 15 20 25 30 4 9 I4fl9 24 29 4 9

May Junt July

Fig. 6 Leaf area index derived from normalized difference vegetation index

(NDVI) measured by AVHRR as a function of date for field N6

(LAI(SAT)) and from direct measurement (LAI). The vertical arrow

belov denotes 16 June. Values along LAI cur%e refer Co height of

corn in meters.
27

ORIGINAL1 PAGE IS

OF POOR QUALITY

^N

2

X

V)

>•/

U

O

<

£

V)

55

u

e

»

I

0

o

ZDO -

240-

220-

200-

180 -

160 -

140 -

120-

100-

80-

60 -

, 40 -

20 -

a

0

a _ .. . a

a

a

0

• a

o . a ]

0

n

i I 1 1 i I i i 1 I i 1 T i 1 1 i 1

0.12 O.H 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3

VOLWATER CONTENT (CC/CC)

Fig. 7 Bulk canopy resistance to water vapor flux (s m~*) as a function of

volumetric water content at 5 cm depth for wheat during the summer

of 1986 at Rock Springs.