NASA-CR-191717

Site

muary_iy 31, 1992

-Shea,

of

, Pedro Zara, Roel Vinlng,

Mesarch

ural Meteorology

ire =ral resources

93-1

(NASA-CR-191717) RADIATION FLUXES

AT THE FIFE SITE Final Report, 1

Jan. 1991 - 31JuI. 1992 (Nebraska

Univ.) 92 p

N93-16718

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1. INTRODUCTION

-The main objective of the International Satellite Land Surface Climatology Project

flSLSCP) has been stated as "the development of techniques that may be applied to satellite

observations of the radiation reflected and emitted from the Earth to yield quantitative

information concerning land surface climatological conditions." The major field study, FIFE

(the First ISLSCP Field Experiment), was conducted in 1987-89 to accomplish this objective.

Four intensive field campaigns (IFCs) were carried out in 1987 and one in 1989. Factors

contributing to observed reflected radiation from the FIFE site must be understood before the

radiation observed by satellites can be used to quantify surface processes. Our last report

(Walter-Shea et al., 1992b) focused on slope effects on incoming and outgoing shortwave

radiation and net radiation from data collected in 1989. We report here on the final analysis of

the slope data as well as results from thermal radiation studies conducted during the FIFE

experiment. The specific areas reported ar_

1. analysis of slope effects on measured reflectance values and estimates of surface

albedo;

2. using remotely-measured surface temperatures as a means of estimating sensible

heat flux from the Konza Prairie;

3. extracting canopy temperatures from remotely-measured composite surface

temperatures;

4. modelling the measured composite temperature of partially vegetated surfaces;

5. estimating gap distribution in partially vegetated surfaces from reflectance

measurements.

_.

1
2. Slope Effects on Measured Reflectance Values and Surface Albedo Estimates

2.1 Introduction

Topographic variation and distance must be considered to properly characterize incoming

solar radiation with in situ measurements over regions (Dubayah et al., 1990). The task of

characterizing incoming solar radiation in topographic varying regions is difficult because of the

necessity of characterizing the topographic relief. Net solar radiation may be even more difficult

to describe in topographically varying areas where reflectance may vary spatially. Spatial

variability even in areas of gentle topographic relief as at FIFE influence net solar radiation

because of the dependence of downwelling irradiance on topography (Dubayah, 1992) and

because of the dependence of reflectance on topography, resulting from microclimatic factors.

The net shortwave balance has a significant effect on the total radiation balance so that

characterizing the shortwave component is critical.

Walter-Shea et al. (1992b), through measurements of upright and inverted Eppley

pyranometers mounted horizontally and parallel to the surface, found differences in calculated

albedo due to instrument position relative to the surface. The effect differed according to

topographic condition, with an average 1% overestimate for North- and West-facing slopes

(relative errors of 6 to 7%). However, the reflected radiance (Win "2) varied little so that the

incoming radiation was of most importance. Dubayah (1992) concluded that most of the

variability in net solar radiation was caused by topographic modulation of incoming beam and

not by changes in reflectance at the FIFE site. Dubayah argues that the grasses stand erect

regardless of slope and therefore the reflectance from grasses on leveled and sloped sites will

not vary.

Spectral radiant flux densities (W m -2 sr q/zm -_) of sloped and level plots at the FIFE site

were normalized to investigate the variation of reflectance factors due to topographic effects.

Differences resulting from using actual and effective radiant flux densities as measures of

incident radiation for calculating reflectance from sloped surfaces are discussed as well.

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The StarEs et al. (1991b) method of estimating albedo from remotely-sensed data

collected from azimuthal planes different from the solar principal plane and from surfaces of

varying slope and vegetative cover was tested. Algorithms were developed and tested with FIFE

87 and 88 data (Blad et al., 1990 and StarEs et al., 1991b). The algorithms were independently

tested using FIFE 89 sites 906 and 916 data (Walter-Shea et al., 1991). FIFE 89 slope data

collected at Site 966 were used to evaluate the applicability of these algorithms on areas of

varying topography and in using data collected in azimuthal planes different from the solar

principal plane.

2.2 Materials and Methods

Instrumentation and Experimental Site, A Barnes Modular Multiband Radiometer

(MMR) 12-1000 and Eppley Precision Spectral Pyranometers (PSPs) were used to collect

incoming and reflected radiation over 15 prairie vegetative plots and one bare soil plot at FIFE

experimental Site 966 (2437-BBS) in 1989. Plots were selected from hill tops (horizontal

surfaces) and from slopes with aspects aligned in the four cardinal directions and in close

proximity to each other.

The MMR collects spectral data in eight wavebands ranging from the visible to the

thermal infrared. The MMR, set with 15° field of view, was mounted on a portable, inclinable

mast three meters above the soil surface producing a target spot size of 0.8m at nadir. The

MMR was calibrated in 1989 by Dr. Brian Markham at NASA/GOddard Space Flight Center in

Greenbelt, Maryland according to the method of Markham et al. (1988). Bidirectional reflected

radiation was measured at seven to eight view zenith angles in the plane parallel to the slope

aspect at nadir, 20 °, 35 ° and 50 ° on either side of nadir and normal to the plot (if it varied from

the other viewing directions).

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Nadir-viewed MMR data were collected over a horizontally-mounted, calibrated

labsphere molded sintered polytetrafluorethylene-based (Spectralon) reference panel (Labsphere

Inc., P.O. Box 70, North Sutton, NH 03260) to estimate incident radiation in each MMR wave

band. The panel was calibrated using the Department of Agricultural Meteorology's fieldreference panel calibration goniometer (Walter-Shea et al., 1992c) following the field calibration

method of Jackson et al. (1987). This method corrects panel reflected radiation data for the

panel's non-Lambertian properties. Incoming radiation values were estimated from the panel

reflected radiation data using MMR calibration coefficients provided by B. Markham to yield

units of spectral radiance (W m"2 sr-1 #ml).

A portable A-frame was mounted with one upright-mounted Eppley PSP to measure

incoming shortwave radiation on a horizontal surface and two inverted Eppley PSPs to measure

reflected shortwave radiation component measurements (one horizontally-mounted, the other

mounted parallel to the slope). The inclined PSP was adjusted at each plot to the appropriate

angle representing the plot slope aspect.

Due to terrain roughness and equipment restrictions, a limited number of MMR and Aframe measurements were made. Approximately two hours were required to complete an entire

run (multidirectional measurements over all plots on all slopes) so that large changes in solar

zenith angle often occurred during a single run.

Experimental Procedures. kMMR nadir-viewed meas-ur_i_ents of the reference panel

were taken at the beginning of the measurement run, followed by MMR multi-angle reflected

radiation measurements over prairie vegetative and bare-soil plots. Repeated measurements from

the A-frame were made in the same plots as the MMR, immediately following or bracketing

bidirectional reflected radiation measurements. Nadir-viewed reflected radiation from the

reference panel was periodically measured during the run with a final panel reading completing

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the sequence of measurements or measured at one minute increments during the run with another

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Incoming radiation received on a horizontal surface was corrected to represent radiation

received on an inclined surface. Correction requires the effective (or local) solar zenith angle.

The effective solar zenith angle was calculated (from Iqbal, 1983) as:

cosO_ = cosBcosO, + sin BsinO,cos(ff-7) [1]

where:

= slope of surface (measured from the horizontal)

3' = surface azimuth angle (ranging in value from 0 to + 180

with east = +90 and west = -90)

O_ = effective solar zenith angle

1/ = solar azimuth

O, = solar zenith angle

arccos (sin_sin6 + cos_osC, coso_)

co = hour angle

¢, = geographic latitude

= declination angle

Incoming radiation values from the A-frame and MMR nadir-viewed reflected radiation

from the field-reference panel were cosine corrected to account for incident radiation received

at the sloped surface (values multiplied by the ratio of the cosine of the effective solar zenith

angle (0_) and the cosine of the solar zenith angle (0,), i.e., cosOJcosS,). Thus, total incoming

radiation was cosine corrected.

Reflected radiances from all surfaces were normalized by the incoming flux on the

horizontal as a means of eliminating solar zenith angle effects for comparison purposes (referred

to as level and slope reflectance factors, RF_¢j and RF,k_). Reflected radiances from sloped

surfaces also were normalized by the irradiance on sloped surfaces (corrected for effective solar

zenith angle and referred to as effective slope RF, RF,_p_,). We hypothesize that if grasses at

the FIFE site grow in the manner described by Dubayah (1992) then the interaction between

incoming beam and grass is expected to differ among the grasses growing on the slopes and

those on level sites since grasses on sloped surfaces are expected to produce "roughened"

5

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surfaces, rougher than those on level plots (assuming vegetation optical properties and canopy

structure are identical). Bright areas on the "roughened" surface are expected when 0, < 0, and

more dark areas on the roughened surface when 0, > 0,.

The method of Starks et al. (1991b) for calculating albedo using remotely-sensed data was

tested with the slope data set, collected in azimuthal directions according to cardinal directions

of N-S and E-W. These azimuthal planes did not always coincide with the solar principal plane.

2.3 Results and Discussion

Slope Effects. We hypothesize that in situations where the effective solar zenith angle,

0_, illuminating a sloped surface is smaller than the actual solar zenith angle, 0, (illuminating a

level surface) more of the tops of the grasses on the sloped surfaces is expected to be illuminated

than on the level surfaces (Fig. 2. la). Under this situation a higher radiative flux density in the

visible portion of the spectrum comes from sloped surfaces than from level surfaces. Also,

shadows cast by grasses on sloped surfaces are expected to be shorter than those cast by grasses

on level surfaces (Fig. 2. lb) which should result in a higher radiant flux density in the visible

portion of the spectrum from sloped surfaces than from level surfaces. Shadows are also

expected to play a similar role in the near-infrared (NIR) portion of the spectrum. Smaller

shadows cast on the substrate (lower in reflectance than grass surfaces) on the sloped sites (more

sunlit areas) will cause a lower NIR reflectance energy than from the level sites. Thus, when

0_ < 0,, RF,_ are expected to be higher than RFk,,,a in the visible and less than in the NIR.

RF,k,p_ _frwill be lower than RF,k_ and possibly closer in value to RF_,¢, when 0¢ is smaller than

0,. The opposite would be true for surfaces where 0¢ is larger than 0,.

There is a trend for level and slope RFs to differ as expected, indicating differences in

canopy reflectance variability with topography (Fig. 2.2 and Table 2.1), however these

differences are small (1% and 4% absolute in the visible and NIR, respectively).

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solar elevation angle. Shadows cast by grasses on the sloped surface are shorter and the sunlit grass area is greater

than on the level slope. (b) Detail of shadow cast by erect grass on the horizontal c and on the 19' sloped surface

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Site 966 for: (a) the red (band 3) and Co) the near-infrared (band 4) portions of the spectrum. 0. and 0, are the solar

zenith and azimuth angles and #v is the view azimuth angle.

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Table 2.1 Mean bias error and mean relative error between level end slope surface reflectance

factors under conditions where the effective solar zenith angle varied from the actual

solar zenith angle.

No.

plots

LAI

difference

VISIBLE NIR

MBE MRE MBE MRE

0, > 0, 45 +2.0 -0.184 -0.030 3.151 0.089

0, < 0, 25 ±2.0 -1.308 -0.228 0.355 0.003

0, • 0, 34 +0.5 0.203 -0.032 3.667 0.102

0, < 0, 20 +0.5 -1.321 -0.222 -0.166 -0.013

0, • 0, 15 +0.25 0.013 0.004 4.087 0.105

0, < 0, 9 ± 0.25 -1.603 -0.272 1.673 0.046

Various factors may be contributing to the observed differences including illumination

differences (as depicted in Fig. 2.1), species differences, vegetative cover, LAI and background

differences.

Albedo Estimates. MMR bidirectionally reflected radiation from plots in various slopes

was obtained for a total of 96 eases in 1989 (86 over grass covered surfaces and 10 over a bare

soil surface). The algorithms reported in Blad et al. (1990), Walter-Shea et al. (1991) and

Starks et al. (1991b) were used to estimate reflected and incoming components of the radiation

balance from which albedo estimates were calculated. The data represent variation in plots,

solar zenith angle and vegetation characteristics.

Reflected and incident shortwave c0m_nent estimates and albedo estimates from the

various sloped surfaces are compared to measured Eppley PSP component values (Fig. 2.3).

Statistical analyses indicate incoming solar radiation estimates agreed well with measured values

over all cases with a d-statistic of 0.98 (see Blad et al., 1990 for explanation of the statistics)

(Table 2.2).
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FIFE-S9 remotely sensed bidirectional reflected radiance data collected at Site 966. Diagonal dashed line represents 1:1

line. Solid line is a fitted regression line. Letter symbols represent as follows: B is the bare soil plot; L, N, S, E, W

are vegetated plots which are on level, north-, south-, east-, and west-facing slopes, respectively.

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Table 2.2. Statistics from comparison of estimated incoming shortwave component (using the

method of Starka et al. (1991b) with measured values. Input and validation data are

from the FIFE 89 slope dataset.

Component d r r_ MBE MRE RMSE Eu Es x s

Wm-= % Wm -= Wm-= Wm -= Wm -=

F

all grass

measured

0.98 0.97 0.94 -14.3 -1.5 55.8 53.0 17.6 748.4 147.4

759.7 160.3

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d = 1 -[_'(E,-M)2/2:(IE,-MI+ IMj-MI)=]

Ea -- estimated value at time i

M, = measured value at time i

M = mean measured value for the data set

RMSE = Root Mean Square Error

_-- I(N'Iz(pa-M_21 + (INl'_'(PrI-E_21)l]2

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P, = predicted value using values derived from least squares regression of estimated

and measured values

: aM= + b

a = slope

b = intercept

MBE = Mean Bias Error

: N'IZ(E,-M_

= Mean Relative Error

= N" [1 (E,-M_/M,][100]

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According to the MBE statistic, the model underestimated the measured incoming solar radiation

by approximately 14 W m-2; the MRE indicates the underestimate is approximately 1.5% of the

measured value. Measured and model means are comparable. The RMSE is approximately 56

W m-2 for the model. The agreement between estimated and measured values is not as good as

that reported by Walter-Shea et al. (1991), comparing data collected in 1989 but at Site 906 and

916 (with MBE of 4 Wm "2 and MREs of 0.04%). Differences may be due to the number of

cases used. Only 86 cases were used for the sloped data and approximately 155 cases for Sites
906 and 916. Also, incoming shortwave measurements on Day 166 were not coincident with

MMR measurements as was in previous reports.

The reflected compone_ntisoverestimated in all cases except in the bare soil plots.

The overestimates ranged from 12-21 Wm -2 (approximately 9-16% of the measured values).

These values are slightly better than those achieved over plots reported by Walter-Shea et al.,

1991 (MBE of approximately 23 W m "2 which is approximately 19% of the measured values),

even though the alignment in azimuths was in a plane other than the solar principal plane. Bare

soil reflected radiation was underestimated by approximately 13 Wm 2. Nadir-viewed reflected

estimates of reflected radiant flux density agreed well and often better than the estimates

obtained using the Starks et al. method. Previous tests did not indicate a significant better

estimate using nadir-viewed data (Starks et al., 1991a).

The Starks et al. method of estimating albedo did not perform as well as under

previous conditions (Table 2.3) (see Walter-Shea et al., 1991). The model performed poorly

for plots on the south- and west-facing slopes and the bare soil plots. Previous test of the

algorithm using bare soil plots indicate a similar poor performance for bare soil conditions

(Starks et al., 1991a).

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Table 2.3 Statistics from comparison of estimated reflected shortwave component (using the

method of Starks at sl. and normal-viewed values) with measured values. Input and

validation data are from the FIFE 89 slope dataset.

Component d r r= MBE MRE RMSE Eu Es x s

Wm-= % Wm-= Wm-= Wm-= Wm-=

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all grass-S* 0,83 0,92 0,85 15,3 12,1 31,8 29,1 12,7 143,3 21,7

all grass-n 0,89 0,88 0,78 -5,1 -4,7 21,2 19,6 7,9 123,3 27,5

measured 127,4 18,4

leveI-S 0,80 0,94 0,88 16,2 13,0 33,3 30,4 13,6 142,9 19,5

level-n 0,89 0,85 0,72 0,6 0,2 16,8 15,5 6,5 127,5 23,8

measured 125,4 16,7

north-S 0,88 0,94 0,89 11,9 9,2 25,3 23,2 10,1 139.4 21,7

north-n 0,88 0,95 0,90 -11,7 -10,0 25,7 23.8 9,8 115,8 24,8

measured 127,5 18,1

south-S 0,84 0,91 0,82 14,3 11,2 30,1 27,8 11,7 141,9 24,2

south-n 0,91 0,90 0.80 -1,2 -1,7 20,0 18,5 7,5 1.26,4 28,8

measured 127,5 19,5

east-S 0,73 0,93 0,87 21,1 15.6 43,1 39,3 17,6 157,9 20,9

east-n 0,92 0,96 0,93 -5.6 -5,2 21,5 20,0 7,7 131,2 29,2

measured 136,8 18,5

west-S 0.79 0.89 0.80 14.5 12.8 32.0 28.9 13.6 136.3 17.9

west-n 0.83 0.80 0.65 -7.1 -6.4 25.1 23.4 9.1 116.9 31.5

measured 119.8 17.5

bare soiI-S 0.89 0.91 0.82 -12.6 -12.6 31.8 29.4 12.0 101.4 36.9

bare soil-n 0.84 0.92 0.85 -15.9 -17.5 43.3 40.7 14.8 98.2 47.5

measured 114.1 27.4

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* -S represents values obtained using the Starks et al, (1991b) method

-n represents values obtained using normal-viewed values

2.4 Summary and Conclusions

Reflectance factors tend to differ between sloped and leveled surfaces. Although

various factors probably contribute to this (including species differences, vegetative cover and

background cover), we feel that surface roughness and thus shadows may play an important role.

13
More studies under controlled conditions are needed to find the significance of surface roughness

on reflectances from sloped grass-cover surfaces.

The incoming shortwave component was underestimated on the average by 14 Wm "2

(compared to 4 Wm -2 in previous studies). The reflected shortwave component was

overestimated by about the same magnitude as in previous studies, even though reflected

radiation was measured in an azimuthal plane other than the solar principal plane. Albedo

estimates were in poor agreement with measured values (10-17% MRE) compared to 4% MRE

with FIFE 87-89 level sites. Large errors in incoming and reflected shortwave components

contributed to the general overestimate of albedo. The cosine correction to the total incoming

shortwave and the lack of coincident measurement of Eppley PSP with the MMR may be factors

contributing to the overestimate. Bare soil albedos were greatly underestimated. Nadir derived

estimates were in better agreement but often underestimated the measured value.

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Estimation of Sensible Heat Flux From Remotely-Sensed Canopy Temperatures

Introduction

The energy balance at the Earth's surface, neglecting energy used for photosynthesis

and small miscellaneous processes, is represented by

R. + H+ _ * G_ O,

(i)

where R. is net radiation, H is sensible heat flux, X is latent heat flux, and G is soil heat flux.

When vegetation covers most or all of the soil, G - 0 and can be neglected [Brunel, 1989).

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Jackson et al. (1985) and Starks et al. (1991b) give procedures for estimating R, using

remotely-sensed multispectral and ground-based meteorological data. Sensible heat flux can

be calculated directly with eddy correlation techniques (Kanemasu et al., 1979), or with

methods that utilize the temperature gradient between the surface and the air and a

measure of the resistance of the atmosphere to heat transfer (Hatfield et al., 1984a;

Choudhury et al., 1986; Huband and Monteith, 1986b; Kustas et al., 1989; Brunel, 1989).

The latter approach requires measurements of surface temperature.

Surface temperature can be measured with an infrared thermometer (IRT).

Temperatures measured- with an IRT generally vary with the view angle of the instrument

(Fuchs et al., 1967; Nielsen et al., 1984; Huband and Monteith, 1986a, Zara, 1992). Kimes

et al. (1980) found IRT measured temperatures of a wheat canopy can vary by as much as

13°C depending on the view zenith angle. Huband and Monteith (1986a) noted that the

influence of solar position and ground cover on canopy temperatures can be minimized by

taking measurements at view angles other than nadir.

_ 15
The primary objective of the research reported here is to determine the optimum

view zenith angle, or angles, to measure surface temperatures for accurately estimating H.

3.2 Materials and Methods

Canopy temperatures were measured concurrently with sensible heat fluxes at the

First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment

(FIFE) sites 916 (4439-ECV) and 906 (2133-ECA). The vegetation at these sites was a

mixture of native grasses, with canopy heights ranging from about 0.20 to 0.85 m.

Surface temperatures were measured with four Everest 4000 Transducer-Multiplexer

Infrared Thermometers mounted so that each IRT viewed the same surface area at a view

zenith angle of 0O (nadir), 20O, 40 °, or 60 ° (Fig. 3.1). The arc on which the IRTs were

mounted pivoted around the nadir position, so the view azimuth angle could be adjusted to

any desired angle. Measurements were taken at azimuth intervals of 45 ° . With this design,

an entire set of measurements across the four view zenith angles and the eight view azimuth

angles could be taken in approximately 5 minutes.

Each IRT was calibrated before and after the experiment in controlled ambient air

temperature conditions, using a blackbody source with variable temperature output values.

Each IRT was also checked periodically during each data collection period with a portable

blackbody source to make certain it was working properly.

Radiative temperature (Tr) was adjusted for emissivity and reflected longwave

radiation to give actual canopy temperature (To) using the equation

Tci { [R_ - (1 - e c) B'] / (E c o)}°'25 (2)

Ell

I

lie

g

EB

m

I

J

!

U

w

mm

e

m

J

W

m

mE

I

mm

m

d

16 w

g
w

Thermometer Infrared I

A

=

L

Fig. 3.1 Schematic diagram of the apparatus used to measure the composite

temperatures. The arm (A) is free to revolve around the vertical axis.

where R_ is radiative flux of the canopy measured with the IRT (i.e., R_ = aT,4); B* is

background longwave (sky) radiation; _c is surface emissivity; and a is the Stefan-Boltzmann

constant. The surface emissivity was estimated using a modified pop-tent method described

by Fuchs and Tanner (1966), and B* was estimated using the aluminum plate method

described by Blad and Rosenberg (1976). IRT data were digitally recorded using

Omnidata Polycorders, and later transferred to microcomputers for processing and analysis.

Data were collected during periods when clouds did not obscure the Sun, which eliminated

fluctuating surface temperatures caused by changing solar radiation flux densities.

H was estimated using the equation

17
H = p, cp (T, - T_)Iz,, (3)

where p, is the air density; cp is the specific heat of air at constant pressure; T, is air

temperature; T c is canopy temperature; and r, is aerodynamic resistance to heat flow. We

used the approach suggested by Hatfield et al. (1984) for estimating r,:

r a = [in(z- d)/zo]21(k 2 U) ,

(4)

m

I

D

g

B

i

where z is height of meteorological observations; d is displacement height; Zo is roughness

length; U is the horizontal wind speed; and k is von Karman's constant (- 0.40).

Roughness length is described as the point where the wind speed within the canopy reaches

zero; displacement height is the mean level at which momentum is taken up by individual

components of the canopy (Rosenberg et al., 1983). Using relationships given by Huband

and Monieith (1986a, b) and Choudhury et al. (1986), d and Zo can be estimated from

canopy height (h):

d = 213 h (5)

Zo - h/a. (6)

II

I

|

!

!

II

i i

i i

D

lip

g

Hatfield et al. (1984) suggested the use of Monteith's (1973) adjustment of r, to

correct for atmospheric stability. We found that better estimates of H were obtained if r,

values were not adjusted using this approach.

For comparison purposes, values of H were estimated with the eddy correlation and

18

g

m

g

II

R

!
the Bowen ratio techniques. For H calculated by the eddy correlation method (H_:),

where w' 0 ' is the average of the product of the deviations from average of vertical wind

velocity (w) and potential temperature (0). For H calculated by the Bowen ratio method

(HB_),

H_ _ p. cp KH (6T/SZ) (a)

v

where K H is eddy diffusivity for heat; ST is temperature gradient; and tSzis change in height.

Half-hour averages of air temperature, air pressure, wind speed, and remotely-sensed canopy

temperatures were used in (3)-(8) to estimate sensible heat flux.

3.3 Results and Discussion

Estimation of Sensible Heat Flux. In 1989, field measurements of canopy

temperatures from different IRT view angles were taken on 5 days at two locations at the

FIFE site. Hourly and daily averages of H were calculated with (3) using canopy

temperatures, corresponding air temperatures and wind speeds from nearby automated

weather stations. H was estimated with eddy correlation techniques on days 209 (July 28),

216 (August 3), and 220 (August 8), and with Bowen ratio techniques on days 218 (August

6) and 219 (August 7). Sensible heat fluxes calculated with the different approaches are

given in Table i.

19
TABLE 1. Values of Sensible Heat Flux Calculated by Hatfield et al. (1984) Method

Using Canopy Temperature Data Obtained at Various IRT View Zenith

Angles Compared to H Calculated by the Eddy Correlation (I-I,_) or Bowen

Ratio (HBR) Methods.

View Zenith Angle (degrees)

H.. or

Time 0 20 40 60 Hea

LT (W m -2)

Day 209 (July 28)

Daily Avg. -140 -134 - 76 - 54 - 94

0800 -35 - 27 - 60 - 58 - 36

0900 - 30 - 27 - 60 - 58 - 36

1000 -112 -108 - 62 - 53 - 86

1100 - 91 - 86 - 53 - 32 - 87

1200 -250 -236 -121 - 82 -125

Day 216 (August 3)

Daily Avg. 19 - 13 56 59 - 72

1000 41 32 60 75 - 94

1300 -30 -56 5 4 -76

1400 - 11 - 40 39 37 - 60

1500 79 21 122 126 - 66

1600 83 22 121 109 -37

Day 218 (August6)

Daily Avg. -144 -140 -106 - 49 -176

1000 -125 -117 -109 - 65 -158

1100 -141 -138 -112 - 68 -176

1200 -129 -130 -103 - 40 -202

1300 -150 -149 -149 -121 -184

1400 '187 -178 -116 - 51 -178

1500 -190 -181 -131 - 51 -161

1600 -100 -101 - 66 - 15 -138

Day 219 (August 7)

Dai_Avg. -270 -265 -229 -104 -217

1100 -263 -251 -220 -102 -196

1200 -268 -256 -223 -103 -207

1300 -286 -289 -246 -110 -251

1400 -287 -287 -247 -123 -235

1500 -283 -271 -234 -109 -200

1600 -191 -182 -155 - 37 -153

g

m

I

g

il

m

i

m

I

I

g

|'

|

w

I

W

g

I

l

W

W

m

I

S

U

20

g

m

III
View Zenith Angle (degrees)

Hec or

Time 0 20 40 60 HeR

LT ON m -2)

Day 220 (August 8)

Daily Avg. -230 -223 -192 -139 -116

1100 -156 -149 -126 - 90 -118

1500 -271 -262 -227 -165 -115

1600 -192 -187 -160 -117 -115

On days 209 and 219, days of moderate wind speeds (Table 2), H calculated using

To measured at 40 ° agreed best with either H_ or HBR as compared to H estimated with T c

measured at the other view zenith angles. Estimates of H made with T c data taken from any

of the view zenith angles, except for 60 ° on day 219 and 0 ° and 20 ° at 1200 on day 209,

agreed reasonably well with H= or FIBs.

r

v

r _

TABLE 2. Average Wind Speeds for the Measurement

Period of Each Day.

Day Site Time Interval Wind Speed

LT m s-1

209 916 0815-1215 3.61

216 916 1115-1615 5.57

218 906 1015-1645 4.92

219 906 1145-1615 3.81

220 916 1215-1615 1.71

On day 220, a calm day, the best estimates of H were made using the 60 ° view

zenith angle. Relatively poor agreement between Hcc and H estimated from the 0° and 20 _

Tc values was observed while H calculated with the 40 ° data tended to overestimate H

compared to Hcf, but not so poorly as for the 0° and 20 ° data. On days of relatively high

wind speed, that is, days 216 and 218, the best agreement between H calculated using T c and

H_ or HBR was obtained using the 0 ° and 20 ° data. In almost all cases there was a tendency

21
to underestimate the magnitude of the heat flux away from the canopies on these windy

days. This was especially true on day 216. On that day, the eddy correlation calculation of

H_ always showed a sensible heat flux from the canopy to the air (negative sign), whereas

H calculated with T c data generally suggested heat flow to the canopy from the air. If r,

values used to calculate H from (3) were stability-corrected using the Monteith (1973)

approach, the estimates of H on the windy days agreed slightly better with H_ or HBR. On

days with moderate or especially low wind speeds, however, the stability correction produced

estimates of H that agreed poorly with H= or Has.

It appears that the best view zenith angle to use for estimating H is dependent on wind

speed. At wind speeds of less than about 4 m s1, good estimates of H can be made using

surface temperatures made at the 40 ° view zenith angle, whereas for wind speeds greater

than about 5 m s1, measurements of Tc at 00 or 200 work best.

The reason the view zenith angle changes with wind speed can be explained as follows.

Mean tilt angle measurements suggested an erectophile type canopy (i.e., leaves that tend

to be vertically oriented). The canopy did not completely cover the ground. Therefore, at

the view angles of (30 and 20 ° and with the leaves in their normal position, the IRT would

view the soil surface. The soil surface, especially at high solar elevations, would be warm

relative to the vegetative cover, and the composite temperature of vegetative plus soil

surfaces sensed by the IRT would be higher than the temperature of the vegetative surface.

Therefore, using the temperature data taken at the (3° and 20 ° view zenith angle to calculate

H would result in H rates that were too high. At the view zenith of 40 ° and 600, the IRT

would view mostly vegetation and would therefore sense a temperature more representative

of the heat exchanging surface. At the 60 ° view zenith angle, the IRT would see a higher

proportion of shaded leaves than at the 40 ° angle so the surface temperature would be

22

J

il

m

m

I

I

m

t

U

!

g

m!

i

qIB

Up

u

m

Q

iiJ

U

g

Bg

!

g
k

v

w

=,

slightly cooler than the T c representative of the heat exchanging surface and result in

underestimation of H, as occurred on days 209 and 219.

With the higher wind speeds the canopy became more planophile, that is the leaves

were more horizontally oriented, owing to the bending of the grass leaves. This reduced the

amount of soil surface viewed by the IRT at the small zenith angles and, at the same time,

it probably increased the amount of shadowed leaves seen by the IRT at the larger view

zenith angles. As a result of this canopy distortion by the wind, the best angle to measure

Tc for estimating H changed from 40 ° to a more vertically oriented angle.

3.4 Conclusions

Estimates of sensible heat flux made with remotely-sensed surface temperatures were

calculated and compared to sensible heat flux values measured with micrometeorological

techniques. Overall, the best estimates of H were made using canopy temperatures

measured at the instrument view zenith angle of 40°. This finding is consistent with those

of Zara (1992) who found that the average canopy temperature was obtained by making IRT

measurements at a view zenith angle of about 40 °. There was considerable variability,

however, insofar as identifying the optimum view zenith angle with which to view the canopy

at a specific time and under specific wind conditions.

Results suggested that the wind speed affected the optimum view zenith angle. Days

with average wind speeds above 5 m s "I coincided with the days on which the nadir and 20 °

view zenith angles provided canopy temperatures that resulted in the best estimates of

sensible heat flux. On days with average wind speeds of less than 4 m s"1, the 40 ° and 60°

view zenith angles provided the best estimates of sensible heat flux. Wind speed should

therefore be considered when selecting the appropriate view zenith angle for measuring

canopy temperature to estimate sensible heat flux over prairie grasslands.

23
4. Extracting the Canopy Temperature from the Composite Temperature

Measured by a Remote Sensor

4.1 Introduction

The use of remotely-sensed canopy temperature to infer soil and vegetation

characteristics has been demonstrated in a number of studies. Canopy temperature

can be used as an indicator of soil moisture status (Jackson, 1983) and plant water

stress (Idso et al., 1977; Jackson et al., 1977a, 1977b) and hence, as a guide to

irrigation scheduling (Jackson et al., 1977b; Gardner et al., 1981; Clawson and Blad,

1982) and as a means of assessing or predicting crop yields (Idso et al., 1977, 1979,

1980; Walker and Hatfield, 1979; Gardner et al., 1981; Smith et al., 1985).

With the advances in science and technology, it is now possible to measure the

surface temperature on large spatial scale from airborne or spaceborne platforms.

Studies to measure canopy temperature on a large spatial scale, however, have shown

varying degrees of success (Heilman et al. 1976; Millard et al. 1978; Soer 1980; Price

1982; Cheevasuvit et al. 1985; Pierce and Congalton 1988). Success in measuring

canopy temperature has been reported for surfaces with full vegetative cover but it

has been difficult to estimate the canopy temperature when the cover is incomplete.

Much of this difficulty results because the temperature measured by a remote

sensor is a composite temperature, i.e., the integrated temperature of the surfaces

exposed to the instrument (e.g., canopy and its background). Previous studies have

shown that the sensor view angle, canopy structure and percentage of ground

coverage can affect the response of the thermal sensor (Hatfield, 1979; Kimes,

1980,1983; Kimes et al., 1980, 1981; Heilman et al., 1981; Parsons, 1985; Balick and

Hutchinson, 1986; Paw U et al., 1989 among others). Depending on the percentage

[]

m

II

i

m

i

il

!

I

g

m

I

U

|

[] !

g r

J

m T

m

B

m

J

m

I

I

24

J
r

W

L_

of ground cover and viewing angle, the composite temperature can differ from the

canopy temperature by as much as 13K (Kimes et al.,1980a; Hatfield, 1979).

Most managed and natural ecosystems are only partially vegetated during most

part of the year. Therefore, to fully utilize the potential of thermal remote sensing

technology in the management of our ecosystem, procedures to isolate the canopy

temperature from the composite temperature are needed.

The problem of extracting the canopy temperature from the composite

temperature has been investigated in many studies. Heilman et al. (1981) reported

that the canopy temperature can be estimated from the composite temperature

obtained from nadir view if percent ground cover, soil temperature, soil and canopy

emissivities and sky irradiance are known. Other canopy temperature models with

varying degree of complexity are also available (e.g., Sobrino and Caselles, 1990;

McGuire et al. 1989; Choudhury and Idso, 1984; Kimes et al., 1981; 1980a; Welles

et al., 1979; Jackson et al., 1979; Deardoff, 1978; Sutherland and Bartholic, 1977).

However, the information required by these models may make them too complicated

to implement on an operational basis.

The measurement of surface temperature by remote means is based on the

principle of energy transfer by radiation, a process by which energy is transferred

without the use of an intervening medium. According to the Stefan-Boltzmann law,

the energy emitted is proportional to the fourth power of the absolute temperature

of the body. For a blackbody (perfect absorber and radiator), the energy emitted (E)

by a body (Wm -2) is given as:

E = aT 4 (4.1)

where T is the absolute temperature (K) and tr is a constant (5.67x10 "aWm2K4).

25
U

Earth surface features are rarely, if ever, perfect absorbers or radiators thus,

the energy measured by an instrument will include a reflected and emitted

component. In this case the flux of radiation reaching a sensor (Es) is

Es = _¢rT4 + (1-E)B* (4.2)

where: e is the emissivity and B* is the incoming longwave radiation (Fuchs and

Tanner, 1966). Most thermal sensing instruments are fitted with filters that allow

only the 8-14#m wavelength band to reach the sensor. In this radiation band, the

emissivity of leaves and plant canopies is high. Idso et al. (1969) and Blad and

Rosenberg (1976) reported emissivities of plant canopies greater than 0.97-0.98.

Considering the high emissivity of plants and the factors involved in the measurement,

Jackson (1983) suggested that for a surface temperature of 30°C, the error involved

from assuming E=i would not generally exceed 1.0°C,

For first order approximation of actual surface temperature only direct line

emission from the source to the sensor need be considered (Kimes et al., 1980a).

The flux of radiation from a composite scene (e.g., canopy and background) to the

sensor can then be approximated as:

TO' = (1-fO)Tc' + fOTb' (4.3)

where: Te is the composite temperature (K) measured from view zenith angle e,

Tc and Tb are the radiant temperatures of the canopy and background respectively

(K); fe is the fraction of the background in the field of view of the instrument in the

general direction of e or the so-called gap fraction or probability of gap (PGAPO).

Kimes et al. (1980a) tested the validity of Eq. (4.3) and reported close

agreement between the theoretical and the observed values. To solve for Tc in Eq.

---z_

u

l

ml

m

III

J

II

Z

ii

=-

!

I

I

I1+

m

i

II

g r

g :

U

III

I

m

Ii

26 I

g
v

v

(4.3), TO, fO or PGAPO and Tb must be known. TO can be measured and PGAPO

may be measured or estimated. The variable Tb is as difficult, if not more difficult

to measure than To. To avoid the determination of Tb, Kimes (1981, 1983)

suggested using multiple view angles. Use of multiple view angles, however is not

economically feasible (Jackson et al. 1990).

Since the ultimate use of remotely-sensed temperature would probably come

from aircraft of spacecraft, a procedure to isolate the temperature of the component

of interest should be developed. The objective of this study was to develop a model

simpler than the ones cited above which can be used on an operational basis to

estimate the canopy temperature from the composite temperature as measured by

a remote sensor.

4.2 Materials and Methods

The study was conducted at the University of Nebraska Agricultural Research

and Development Center, Mead, Nebraska (41°9'N latitude and 96°30'W longitude)

during the summer of 1990. Measurements were made over several plots of warm

season range grasses seeded on Sharpsburg silty clay loam soil. The vegetation

consisted of a mixture of big blue stem (Andropogon gerardii Vitnam), little bluestem

(Schizachyrium scoparium (Michx.) Nash), indian grass (Sorghastrum nutans (L.)

Nash), switch grass (Panicum virgatum L.) and sideoats grama (Bouteloua

curtipendula (Michx.) Torr.).

During the experimental period, depending on the weather condition, as many

plots as possible were selected to achieve a wide range of percent ground coverage.

The plots had uniformly growing vegetation and an area of about 10m 2. Some of the

plots were located in areas that were under different grassland management practices

27
W

e.g., different cycles of burning or mowing, while others were in areas that were in

their natural conditions. As such, background conditions differed from one plot to

another, from one without debris to one covered with litter and dead plant material.

Surface soil moisture conditions varied from moist to dry as estimated from visual

observations. At the end of the experimental period, 14 plots were sampled in eight

days during the period May to July.

Measurements. Composite temperatures (TO) were measured with four

Everest 4000 Multiplexer-Transducer (Everest Interscience, Inc.) while canopy

temperature (Tc) and background temperature (Tb) were measured with a handheld

infrared thermometer (Scheduler Plant Stress Monitor, Ohio Standard Oil). The

infrared thermometers (IRT) have 15 ° field of view (FOV), bandpass of 8-14 _m,

accuracy of 0.5K and resolution of 0.1K.

The four transducers were mounted on an arc at angles of 00, 20 °, 40" and

60 ° from the vertical (Fig. 3.1). The arc was made such that the field of view of each

instrument was centered on the same point on the ground at a distance of 3.3 meters.

It was supported on one end by a semi-circular arc and was free to revolve around

the vertical axis. The height of the arc was adjusted to maintain a distance of 3.3m

above the average height of the vegetation.

During the measurement periods the arc was centered over the plot.

Composite temperature readings were taken with the arc aligned towards each of the

eight cardinal compass directions. Immediately after the composite temperature

readings were takenl canopy and background temperatures were measured with a

handheld IRT. Canopy temperature was measure d by placing the handheld IRT very

close to the canopy in an almost horizontal position. Mean canopy temperature was

28

m

I

m

R

II

I

g

m

n

W

m

n

D

U

U

II

I

m

I

m

8

ai
r

w

obtained by taking the average of eight readings taken from the eight major compass

directions. Mean background temperature was obtained by taking the average of

several readings taken from different points on the ground with no foliage in the

instrument field of view. Air temperature was also monitored by another sensor in

the Scheduler. All measurements were replicated twice.

Field checking of the instruments was done by comparing the IRT readings

to a blackbody calibration source (Everest 1000, Everest Interscience, Inc.) before

and after each set of readings. The IRTs were also calibrated in the laboratory

before and after the experimental period. There were no significant changes

observed in the calibrations.

Measurements were made on clear days or during periods when no clouds

obscured the sun. It took about five minutes to finish each set of readings. All

measurements were made during mid-day, 1200-1500h. This time is considered

optimum for many remotely-sensed temperature applications (Gardner et al. 1981;

Millard et al., 1978).

Plant parameters measured were leaf area index (LAI) and gap fractions

(PGAPO) and canopy heights. LAI and PGAPO were measured using the LiCor

Plant Canopy Analyzer (LAI 2000, LiCor Inc., Lincoln, NE). This instrument consists

of an optical sensor and a control unit. The sensor is made of five concentric rings

of radiation detectors. The lenses in front of the detectors cause the five rings to see

different portions of the sky. The sensor has a filter that rejects light greater than

0.490/zm, thus minimizing the light reflected and transmitted by leaves from reaching

the detectors. The control unit records light readings above and below the canopy

at five angles and calculates LAI and PGAP0 from light interception measurements.

29
4.3 Results and Discussions

Data gathered throughout the experiment are presented in Tables 4.1, 4.2 and

4.3. Table 4.1 presents the grassland management practices (Treatments), percent

ground cover (GC), LAI and canopy height. Table 4.2 presents the gap fractions

(PGAPO) from the four view zenith angles. Leaf Area Index (LAI) ranged from 0.66

to 3.81 while gap fractions at nadir ranged from 0.17 to 0.86. Canopy height ranged

from 0.15 to 0.65m. Table 4.3 presents the temperature measurements made on each

plot. The measurements were made on different days with different environmental

conditions and air temperatures ranging from 297 to 315K during the time of

measurement. Canopy temperatures ranged from 295 to 314K while background

temperatures ranged from 307 to 332K.

I

i

I

I

i

i

i

I

I

i

i

I

I

[] i

I

i

I

I

m

I

I

I

30

a

I

I
Table 4.1. Management practices (Treatment), percent ground cover (GC), Leaf

Area Index (LAI) and plant height.

PLOT Treatment 1 GC LAI HEIGHT

(%) (m)

1 a 14 0.66 0.15

2 a 25 1.35 0.20

3 b 37 1.58 0.25

4 c 45 2.08 0.45

5 d 67 2.66 0.55

6 b 60 2.76 0.60

11 e 27 1.42 0.25

12 e 41 1.65 0.35

13 f 85 3.81 0.65

14 c 52 2.15 0.60

15 g 35 1.56 0.25

16 b 57 2.36 0.50

17 b 43 1.75 0.40

18 h 70 2.79 0.60

a- Burned in Fall of 1989

b- Mowed in Fall of 1989 and litter left behind

c- Control, natural condition

d- Mowed in Summer of 1989 and litter left behind

e- Burned in Summer of 1989

f- Burned in May 1990

g- Mowed in Fall of 1989 and litter removed

h- Mowed in Summer of 1987 and litter removed

w

w
I

Table 4.2 GAP Fractions (PGAPO) measured from 0 °, 20 °, 40 ° and 60 ° view

zenith angles.

PLOT PGAP0 PGAP20 PGAP40 PGAP60

1 0.856 0.781 0.713 0.385

2 0.745 0.669 0.521 0.314

3 0.630 0.486 0.339 0.216

4 0.550 0.395 0.238 0.134

5 0.432 0.286 0.158 0.080

6 0.400 0.254 0.153 0.094

11 0.748 0.690 0.422 0.269

12 0.591 0'463 0.343 0.219

13 0.174 0.095 0.055 0.031

14 0.479 0.327 0.232 0.121

15 0.648 0.526 0.386 0.244

16 0.429 0.322 0.217 0.113

17 0.573 0.432 0.259 0.157

18 0.300 0.222 0.147 0.069

From the 14 plots, five (2, 4, 13, 15 and 16) were selected for use in the

subsequent discussion and model development. These plots represented the range

of ground covers (i.e., (I'PGAP at nadir)xl00) that were observed in the field, from

very sparse (25% ground cover) represented by Plot 2 to almost full cover (86%

ground cover) represented by Plot 13. Plots 4, 15 and 16 have intermediate ground

cover, 45, 35 and 57% respectively (see Table 4.1). Soil surfaces of plots 2 and 4

were relatively moist while plots 13, 15 and 16 were relatively dry. Weather

conditions during the time of measurement varied from relatively cool with air

temperatures of 297.9 and 307.5K for plots 2 and 4, respectively to relatively warm

32

U

l

I

i

mi

!

ii

n i

I

m

I

m

i |

D

m

B

Z

g

m
z

Table 4.3 Composite temperatures (TO) measured from 0 °, 20 °, 40 ° and 60 ° view zenith angles,

canopy temperature (Tc), background temperature (Tb), and air temperature (Ta). All units are

in IC Measurements were made on eight days during the period May-July.

PLOT TO T20 T40 T60 Tc Tb Ta

.................................................... g ............ _ .......................................

1 307.96 306.70 302.40 300.82 295.50 311.83 297.61

2 308.31 306.96 303.28 299.61 295.62 309.51 297.91

3 310.65 308.69 306.31 302.29 299.84 312.27 304.28

4 308.98 307.88 305.61 302.45 300.93 312.47 307.52

5 310.79 310.53 307.68 305.94 304.80 314.66 307.43

6 304.35 304.09 301.21 300.56 299.70 307.16 303.59

II 326.27 325.27 321.46 317.02 313.80 332.80 314.30

12 322.18 320.27 317.00 313.77 311.40 329.20 314.75

13 309.00 308.89 308.45 307.92 307.40 312.50 313,85

14 319.74 318.59 315.65 313.10 311.70 324.65 314.35

15 320.98 319.52 316.81 312.35 309.65 326.65 313.85

16 318.82 317.42 314.73 312.30 311.25 324.20 315.70

17 320.36 318.68 316.15 312,70 310.85 326,55 314.50

18 313.90 313.29 311.06 310.37 309.50 315.20 314.55

w

r_

with air temperatures greater than 313K for plots 13, 15 and 16 (see Table 4.3).

Data from the remaining nine plots served to validate the model.

Variation of Composite Temperature with View Angle. Figure 4.1 shows a

graph of composite temperature (TO) as a function of view zenith angle for the five

selected plots. Since the data were gathered on different days with different

environmental conditions, there are differences in TO. For example, Plots 2 and 13

were measured on two different days with an air temperature difference of about 16K

but their composite temperatures at nadir differed by less than 1K. On the other

hand, Plots 15 and 16 were measured on the same day with approximately the same

air temperature but their composite temperature at nadir differed by about 10K. In

general, for measurements made under similar weather conditions, TO increased

33
i330

320

310

300

I I I I

290 i i u l

0 20 40 60

VIEW ZENITH ANGLE

--_ .......... , ...... _ - _ _-- _

Figure 4.1. Composite temperatures (TO) as a function of view zenith angle for

thedifferentp!ots (o)P2, (o)P15, (,,)P4, (')P16 and (O)P13.

?

with a decrease ground cover or _. At low percentage of ground cover, absorption

of solar radiation by soil and canopy is enhanced (less shading) resulting in a higher

soil and canopy temperature Compared to plots with higher percentage of ground

cover. _e high proportion of warm soil in the field of view of the instrument in

addition to the warm canopy results in a higher composite temperature.

34

Z

m

w

m

I

Ul

!

il

W

D

m__

ii

m

W

m

M

l

I

g
=

w

For a particular plot, TO decreases with increasing view angle. This trend is

expected because at increasing view angle the proportion of the background in the

field of view of the instrument is decreased while that of the canopy is increased.

Since the background was at a higher temperature than the canopy (Table 4.3) the

resulting composite temperature was lower. There are also differences in the

magnitude of changes of TO from one plot to another. The variation of TO with

view angle decreases with increasing ground cover. Plot 2, which had the sparsest

canopy, showed the greatest variation of TO with view angle. For this plot TO

decreased by about 8K from nadir to the 60 ° view angle. Plot 13, which was almost

at full cover, showed very little variation (about 1K) with view zenith angle.

The variation of TO with view angle from plot to plot can be explained by the

magnitude of the change in the proportion of the canopy and background in each

view angle (Fig. 4.2). At low ground cover (e.g., Plot 2) the change in the proportion

of gap with view angle was higher as indicated by the steeper slope of the line. At

increasing ground cover the rate of change becomes less as indicated by the

decreasing slope of the lines.

Deviation of Sensor Response from the Canopy Temperature. Information

concerning the magnitude of the deviation of TO from Tc that might be expected

from a given ground cover condition is important if one is to infer canopy

temperature from composite temperature measurements. Deviations of TO from

To, i.e., (TO-Tc) as a function of view angle for the five selected plots are shown in

Fig. 4.3. For the sparsest canopy (Plot 2), TO differed from Tc by about 5K when

viewed at 60 ° and more than 13K when viewed at nadir. For canopies with nearly

35
0.8

0.7

0.6

0.5

_ 0.4

n_

0.3

0.2

0.1

0.0

I I I I

L I I I

0 20 40 60

VIEW ZENITH ANGLE

Figure 4.2. Gap distribution (PG_0) as a function of view zenith angle for plot

(o)a2, (e)P15, (,,)P4, (..)P16) and (e)P13.

complete cover (Plot 13) there was very little variation with view angle. For a

particular view zenith angle, (TO-Tc) decreased with increasing LAI or ground cover.

Differences in the magnitude of deviations (TO-Tc) from one plot to another

were due to differences in PGAPO and (Tb-Tc). This can be readily explained by

_ _ ?

rewriting Eq. (4.3) so that

TO4-Tc4 = PGAPO(Tb4-Tc 4) (4.4)

i

i

i

|

i

i

i

m

I

m

g

g

i

m

z

lib

i

li

36 i
A

¢.

!

I15

10

5

0

I I I I

I I I I

0 20 40 60

VIEW ZENITH ANGLE

w

=

Figure 4.3. Deviation of the composite temperature from the canopy temperature

(TO-To) as a function of view zenith angle for plot (o)P2, (e)P15, (•)P4, (re)P16

and ( •)P13.

Equation (4.4) suggests that the deviation of TO from Tc will be greatest when

PGAPO and/or (Tb4-Tc 4) are high. PGAPO and (Tb-Tc) are directly related to each

other, i.e., when PGAPO is high, (Tb-Tc) is also high. This phenomenon is so

because at high PGAPO the differential heating between the ground and canopy is

enhanced, creating a wide difference between the ground and canopy temperature.

37
m

J

At low PGAPO, less solar radiation reaches the soil and the differential heating is

suppressed (Kimes et a1.,1980).

g

m

I

0.20 I I I I |

0.15

0.10

0.05

0.00 j _ i ,

0 20 40 60

VIEW ZENITH ANGLE

i

i

BB

i

m

!

I

u

Figure 4.4. Deviation of the composite temperature relative to the canopy

temperature (Tdr) as a function of view zenith angle for plots (o)P2, (e)P15,

(,,)P4, (')P16 and ( * )P13.

i

i

Figure 4.3 gives an indication of the absolute deviations for the range of

conditions in which the data were gathered. However, for another set of

environmental conditions the above deviations may be quite different. Transforming

the deviations into values relative to the canopy temperature (i.e.,

38

i

II

m

B

I
7

W

m

Tdr = (ZO4-Tc4)]'Tc 4) will give Fig. 4.3 a wider range of applicability. The relationship

of Tdr to view angle is shown in Fig. 4.4. Although Figs. 4.3 and 4.4 look similar, the

information derived from the two figures is different. Figure 4.3 gives deviations that

are absolute while Fig. 4.4 gives relative deviations that change with environmental

conditions.

Modeling the Canopy Temperature. To be able to use Eq. (4.3) in operational

situations, simplifying procedures and assumptions need to be made. Equation (4.3)

can be rewritten as:

where:

Tdr = PGAPO((Tb'fTc')-I) (4.5)

Tdr = (TO'-Tc' )/To 4 (4.6)

The variables on the right hand side of Eq. (4.5) are directly related to each

other. The ratio, Tb4/Tc 4 increases with increasing PGAP (Fig. 4.5). For a given plot

Tb4/Tc 4 is constant during the angular measurement period. At another weather

condition, Tb4/Tc 4 may be assumed to be relatively constant. Due to the nature of

the heating process and to differences in heat capacities between the soil and

vegetation it is very likely that a change in Tb will have a proportionate change in To.

This phenomenon can be substantiated by the data from Kimes et al. (1980a) which

indicated that for a particular plot Tb/Tc stayed relatively constant for the dawn

measurements and those made near noon. With this, it follows that at other weather

condition Tb/Tc can be assumed to be relatively constant.

dropped from Eq. (4.5) and replaced by a constant (C), i.e.,

Tdr -- CPGAPO

Thus, Tb4/Tc _ can be

(4.7)

_=: 39
A

¢J

I--

\

/n

Iv

1.8 I I

1.7

1.6

1.3

1.2

m

mm •

I I

1.1

0.0 0.2 0.4 0.6 0.8 1.0

PGAPO

Figure 4.5. Relationship of the ratio of Tb/Tc to PGAPO for the five Selected

plots.

The constant (C) can be evaluated by regression techniques. Once C is known, Tc

can be calculated from Eq. (4.6).

Tdr values for each zenith view angle were calculated for the five plots and

plotted against PGAPO (Fig. 4.6). As expected from Eq. (4.7), a direct linear

relationship existed between Tdr and PGAPO (r2=0.97). Using a linear regression

technique, C was found to be 0.231.

40

!

m

g

m

m

m

II

II

m i

)

z

E

m

i

m

il

i

g

ii
i =

=

w

0.15

0.05

Tdr - 0.23 I*PGAPO

0.00

0.0 0.4 0.6 0.8

PGAPO

w

Figure 4.6. Relationship of relative temperature difference (Tdr) to PGAPO. The

solid line is the equation Tdr -- 0.231PGAPO.

Combining Eqs. (4.6) and (4.7) and solving for Tc yields:

Tc = TO(1.0+0.231PGAPO) _zs (4.8)

The variables on the right hand side of Eq. (4.8) may be obtained by remote means

as will be shown in See. 6. Thus the canopy temperature, Tc can be estimated from

Eq. (4.8) using only remotely-sensed information.

Leaf Area Index (LAI) may also be used in Eq. (4.6) instead of PGAP0. The

two variables are related to each other by the following equation:

41
PGAPO = exp(-kt,M/cosO) (4.9)

where LAI is the leaf area index; 0 is the zenith view angle;and k is the proportion

of the canopy projected in the general direction of O. Using Eq. (4.9) in place of

PGAPO, however,makesthe model crop specificbecausethe variable k dependson

the geometric structure of the crop.

0.20

0.15

0.05

1

¢

0.00 ' J"

1 2

I ° I ' I ' I ' I '

Td r - 0.527*EXP(-0.804*LAI/COSO)

i 1 i

3 4 5 6 7

LAI/COSO

_: .... -i7

Figure 4.7. Relationship of relative temperature difference (Tdr) to LAI/cosO.

The solid line is equation Tdr = 0.527exp(-0.804LAI/cosO).

A plot of Tdr as a function of LAI/cos0 is shown in Fig. 4.7. The equation

which fits the data (r_=0.98) is

U

U

I

i

i

i

i

i

[]

il

Um

II

[]

J

g

i

i

m

42 j
w

Tdr = 0.527exp(-0.804LAI/cosO)

Substituting Eq. (4.10) into Eq. (4.6) and solving for Tc yields:

Tc = TO(1.0+0.527exp(-0.804LAI/cosO) "°'z5

(4.10)

(4.11)

LAI can be estimated from remotely-sensed data as has been demonstrated in

a number of studies (e.g., Asrar et al., 1985; Hatfield et al., 1984; Gardner et al.,

1986). Thus, Eq. (4.11) can be used to estimate Tc by remote means.

w

w

- 43
320

aoo

©

©

290

290 300 310 320

Tc, observed (K)

Figure 4.8. Comparison of canopy temperature estimated from

Tc -- TO(1.0+0.231PGAPO) °'z5 with the observed values. The solid line is the 1:1

line.

Model Validation/Testing. Equations (4.8) and (4.11)were applied to the rest

of the data gathered in this experiment. Comparisons of the estimated canopy

temperatures with the measured values are shown Figs. 4.8 and 4.9. Good agreement

was obtained between the estimated and observed Tc values with the d index

(Willmott, 1982) of 0.99 for both equations and RMSE-0.54 and 0.56 for Eq. 4.8 and

4.9, respectively.

44

t

II

II

i

J

i

[]

m

i

il

i

[]

!

ii

mm

m

g

W

I

i

!1

m

U '

B

m
w

L_

7 -

i

320

A

310

"O

@

4_

m

E

m

W

@

] i I I

290

290 300 310 320

Tc, observed (K)

Figure 4.9. Comparison of canopy temperature estimated from

Tc = TO(1.0+0.527exp(-0.804LAI/cosO) -°'z_ with the observed values. The solid

line is the 1:1 line.

Equation 4.8 was further tested using the data from Matthias et al. (1987).

Their data were obtained from cotton (Gossypium hirsutum L. Deltapine 61) grown

in a portable wooden box (0.5 X 0.5 X 0.28m) containing Vinton loamy fine sand.

The plants were 0.15m tall and had six leaves/plant with a total area of 0.025m 2. The

data set consists of composite temperatures obtained from nadir view, canopy

temperatures and gap fractions. Composite temperatures were varied by viewing

different proportions of canopy and background at nadir. This was done by moving

45
the box across the stationary field of view of the instrument. Results of the validation

is shown in Table 4.4. Good agreement was obtained between the modeled and

measured canopy temperatures with a RMSE of 0.53.

Table 4.4. Composite temperature (TO), Gap fraction (PGAP), measured canopy

temperature (Tc), modeled canopy temperature (Tcmod) estimated from equation

(4.8) and difference between the measured and modeled canopy temperature (TcTcmod). Experimental data were obtained from Matthias et al., (1987).

TO PGAP Tc Tcmod Tc-Tcmod

°C °C °C °C °C

33.20 0.78 21.2 20.87 0.33

30.80 0.63 21.2 20.65 0.55

27.20 0.34 21.2 21.57 -0.37

25.50 0.31 21.2 20.45 0.75

4.4 Summary and Conclusions

Sec. 4. documents the angular variation of composite temperature measured

by a remote sensor for grassland vegetation. Depending on the percentage of ground

cover and zenith view angle from which the measurement was made, the composite

temperature may differ from the canopy temperature by as much as 13IC The

deviations were found to be linearly related to the gap distribution in the canopy and

exponentially related to LAI/cosO. Two models, Eq. (4.8) and Eq. (4.11), were

developed to esiimate the canopy temperature using primarily remotely-sensed

information. Good agreement was found between the model estimates and measured

canopy temperature with RMSE of 0.54 and 0.56 for Eq. (4.8) and Eq. (4.11),

respectively. Application of the model to limited data from the literature indicated

Z

i

g

m

I

i

m

i

m

m

i

m

i

m

U

g

i!

u

m

J

i

m !

m

g

46 i
good agreement between the estimated and the measured canopy temperature

(RMSE = 0.53).

Although the models were developed from data acquired from plots with a

wide range of ground cover and environmental conditions, further studies are needed

to assess the stability of the empirical but physically based coefficients. The models

presented here were developed for grass covered surfaces but a similar procedure

may be used to develop models for agricultural crops and other natural ecosystems

(e.g., forest).

==

L

_ z

w

47
5. Modeling the Mean Composite Temperature of Partially Vegetated Surfaces

5.1 Introduction

Remotely-sensed surface temperature can be used to help estimate sensible

heat flux (H) and latent heat flux (LE) over large areas. In an approach described

by Jackson (1985), H is estimated using the bulk transfer equation given as:

H = aCp(Ts-Ta)/ra (5.1)

where p is the density of the air (kg m3), Cp specific heat of the air (J kglKl), Ts

and Ta are the surface and air temperature (K), respectively, and ra is the

aerodynamic resistance to heat transfer (sml). LE is estimated as the residual in the

energy budget equation given as:

-LE = R, + H + S

where Ro is the net radiation and S is the soil heat flux.

(5.2)

Using this approach,

satisfactory estimates have been reported for surfaces with essentially full vegetative

cover (Hatfield et al., 1983; Seguin and Itier, 1983; Reginato et a1.,1985; Moran et

a1.,1989), but difficulties arise when a surface is only partially covered with vegetation

(Kustas et al., 1987).

Most of the work done to overcome the difficulties in estimating H has

focused on adjusting the resistance to heat transfer (Kustas et al., 1989; 1990). This

adjustment however, does not seem to be applicable in all circumstances as suggested

by their recent findings where the modeled sensible heat flux disagreed with the

measured flux not only in magnitude but also in sign. Hence, factors other than the

aerodynamic resistance may need to be considered.

The reliable evaluation of sensible heat flux using Eq. (5.1) by remote means

is based on the assumption that the temperature measured by a remote sensor (Ts)

48

J

i

I

i

J

t

m

i

U

m

|

m

u!

tn

v

m

lib

i

m

==

IB

[
r

w

w

w

w

w

is the aerodynamic temperature, i. e., the average temperature of all the canopy

elements weighted by the relative contribution of each element to the overall

aerodynamic conductance (McNaughton, 1988; Moran et al., 1989). Due to

directional characteristics of radiometric temperature, Huband and Monteith (1986)

stated that the radiometric (i.e., temperature obtained by a radiometer) and

aerodynamic temperature may not be the same for a crop canopy. Choudhury et al.

(1986) found that the two are nearly the same for near neutral conditions, but

radiometric temperatures were higher than the aerodynamic temperatures for stable

conditions and lower for unstable conditions.

The disagreement between radiometric and aerodynamic temperature is

further complicated when the surface is only partially vegetated. In this case, the

temperature measured by a remote sensor is the integrated temperature of the

surfaces exposed to the instrument. Currently, for most large area application

studies, the surface temperature is measured at nadir view. At this view angle the

temperature measured may be dominated by the temperature of the soil surface

which is usually higher than the temperature of transpiring vegetation during the day.

Thus, for partially vegetated surfaces H is commonly overestimated and LE is

underestimated (Moran et al., 1989). Clearly, the temperature measured at nadir will

not likely represent the temperature of the heat exchanging surface.

The dependence of remotely-sensed surface temperature on viewing angles

and directions has been reported in a number of studies (e.g. Fuchs et al., 1967;

Kimes et al., 1980, 1981; Heilman et al., 1981; Parsons, 1985). However, the question

of which directional temperature measurement best represents the surface

temperature of partially vegetated surfaces for use in radiation or energy balance

49
studies has not been fully addressed. Procedures for obtaining the representative

surface temperature to use for a given application and degree of ground cover are

lacking. The objective of this study is to develop a procedure for obtaining/estimating

the mean composite temperature of partially vegetated surfaces from directional

radiometric measurements.

5.2 Materials and Methods

The study was conducted at the University of Nebraska Agricultural Research

and Development Center, Mead, Nebraska (41°9'N latitude and 96°30'W longitude)

during the summer 1990. Measurements were made over several plots of warm

season range grasses seeded on Sharpsburg clay loam soil. The vegetation consisted

of a mixture of big blue stem (Andropogon gerardii Vitnam), little bluestem

(Schizachyrium scoparium (Michx.) Nash), indian grass (Sorghastrum nutans (L.)

Nash), switch grass (Panicum virgatum L.) and sideoats grama (Bouteloua curtipendula

(Michx.) Tort.).

During the experimental period, depending on the weather condition,

measurements were obtained on as many plots as possible to achieve a wide range

of percent ground coverage. Each plot had uniformly growing vegetation and an area

of about 10m 2. Some of the plots were located in areas that were under different

grassland management practices e.g., different cycles of burning or mowing, while

others were in areas that were in their natural conditions. As such, background

conditions differed from one plot to another, from one without debris to one covered

with litter and dead plant material. Surface soil moisture conditions varied from

moist to dry as estimated from visual observations. At the end of the experimental

period, I4 plots were sampled in eight days during the period May to July.

50

II

i

II

m

J

II

g

i

II

I

m

l

m

I

[]

m

m

m

I

i

I

Ill

11

i

[]
r

w

r

Measurements. Composite temperatures (TO) were measured with four

Everest 4000 Multiplexer-Transducer (Everest Interscience, Inc.) while canopy

temperature (Tc) and background temperature (Tb) were measured with a handheld

infrared thermometer (Scheduler Plant Stress Monitor, Ohio Standard Oil). The

infrared thermometers (IRT) have 15 ° field of view (FOV), bandpass of 8-14 #m,

accuracy of 0.5K and resolution of 0.1K.

The transducers were mounted on an arc at angles of 0 °, 20 °, 40 ° and 60 °

from the vertical. The arc was made such that the field of view of each instrument

was centered on the same point on the ground at a distance of 3.3 meters. It was

supported on one end by a semi-circular arc and was free to revolve around the

vertical axis. The height of the arc was adjusted to maintain a distance of 3.3m above

the average height of the vegetation.

During the measurement periods the arc was centered over the plot.

Composite temperature readings were taken with the arc aligned towards each of the

eight cardinal compass directions. Immediately after the composite temperature

readings were taken, canopy and background temperatures were measured with a

handheld IRT. Canopy temperature was measured by placing the handheld IRT very

close to the canopy in an almost horizontal position. Mean canopy temperature was

obtained by taking the average of eight readings taken from the eight major compass

directions. Mean background temperature was obtained by taking the average of

several readings taken from different points on the ground with no foliage in the

instrument field of view. Air temperature was also monitored by another sensor in

the Scheduler. All measurements were replicated twice.

51
Field checking of the instruments was done by comparing the IRT readings

to a blackbody calibration source (Everest 1000, Everest Interscience, Inc.) before

and after each set of readings. The IRTs were also calibrated in the laboratory

before and after the experimental period. There were no significant changes

observed in the calibrations.

Measurements were made on clear days or during periods when no clouds

obscured the sun. It took about five minutes to finish each set of readings. All

measurements were made during mid-day, 1200-1500h. This time is considered

optimum for many remotely-sensed temperature applications (Gardner et al. 1981;

Millard et al., 1978).

Plant parameters measured were leaf area index (LAI) and gap fractions

(PGAPO) and canopy heights. LAI and PGAPO were measured using the LiCor

Plant Canopy Analyzer (LA12000, LiCor Inc., Lincoln, N'E). This instrument consists

of an optical sensor and a control unit. The sensor is made of five concentric rings

of radiation detectors. The lenses in front of the detectors cause the five rings to see

different portions of the sky. The sensor has a filter that rejects light greater than

0.490 _m, thus minimizing the light reflected and transmitted by the leaves from

reaching the detectors. The control unit records light readings above and below the

canopy and calculates LAI and PGAP0 from light interception measurements.

Data gathered throughout the experiment are presented in Tables 4.1, 4.2 and

4.3. Table 4.1 presents the grassland management practices (Treatments), percent

ground cover (GC), LAI and canopy height. Table 4.2 presents the gap fractions

(PGAPO) for the four view zenith angles. Leaf area index ranged from 0.66 to 3.81

while gap fraction at nadir ranged from 0.17 to 0.86. Canopy height ranged from 15

52

m

m

R

ill

m

n

m

Ii

I

I

i

g

m

z

I

g

U

m

il

m

l

l

m

g
v

to 65 cm. Table 4.3 presents temperature measureiiients made on each plot. The

measurements were made on different days with different weather conditions with air

temperature ranging from 297 to 315K during the time of measurement. Canopy

temperature ranged from 295 to 314K while background temperatures ranged from

307 to 332K.

From the 14 plots, four plots (Plots 2, 4, 13 and 16), representing the range

of ground cover conditions for the experimental plots, were selected for use in the

subsequent discussions and model development. Plot 2 was the sparsest (25% ground

cover) while Plot 13 had greater than 86% ground cover. Plots 4 and 16 had

intermediate ground cover of 45 and 58%, respectively. Plots 2 and 4 had relatively

moist soil surface while Plots 13 and 16 were relatively dry. Data from the remaining

nine plots served to validate the model.

In this study it is assumed that the mean of composite temperature readings

taken at different zenith view and azimuth angles represented the temperature of

partially vegetated surfaces. The mean composite temperature (Tm) for each plot

was obtained by taking the average of 33 readings taken from different view zenith

angles and azimuthal directions. Then, the composite temperature reading for each

zenith view angle (TO) was divided by Tm thus, transforming the data into relative

values (Tr) with respect to the mean (i.e., Tr=TO/Tm). This normalizing procedure

made the data comparisons easier.

5.3 Results and Discussions

A graph of Tr against zenith view angle for four selected plots is shown in Fig.

5.1 A striking feature observed in Fig. 5.1 is the convergence of the lines towards the

40* view angle and a relative temperature of 1.0. Data to the left of 40 ° view angle

53
1.2 1 I I I

E

I-

\

I1.1

1.0

0.9

0.8

0,7 i i 1

0 20 40

VIEW

I I

60 80

ZENITH ANGLE

Figure 5.1. Variation of composite temperature (TO) relative to the mean surface

temperature (Tm) for the different plots (e)P2, (A)P4, (=)P16 and (0)P13.

had relative temperatures greater than 1.0 indicating a surface temperature higher

than Tm. The reverse is true for data to the right of 40 ° view angle. The relative

deviation from Tm (i.e., [Tr-1 ]) increases as the view departs from the 40 ° view

angle with maximum deviation at the two extreme zenith view angles. Deviations are

i :

large for sparse canopies and small for canopies with almost full cover. At the 40*

view angle the surface temperature is close to the mean i.e. Tr= 1.0. This suggests

54

m

i

g

u

m

I

m

I

m

m

g

g

g

z

W

ii

m

m

g

I

mi

g
L_

k

that the mean temperature can be obtained by viewing the surface from or near a

40* zenith angle. At this angle the canopy and the background are presented to the

instrument in more or less equal proportions (Table 4.3). At view angles less than

40", the proportion of the background increases while that of the canopy decreases.

Since the background often has a higher temperature than the canopy, the composite

temperature is higher than the mean. The reverse is true for view zenith angles

greater than 40 ° .

The significance of Tm as the representative composite temperature of

partially vegetated surfaces can be judged from the following corroborative studies:

Blad et al. (1990) compared the measured outgoing longwave radiation and sensible

heat flux with those estimated from Composite temperature measurements taken from

different view zenith angle. They concluded that reasonable estimates of outgoing

longwave radiation and sensible heat flux can be obtained from composite

temperatures measured at the 40 ° view zenith angle. Brunel (1989) used remotelysensed composite temperatures measured at 45 ° view zenith angle to estimate

evapotranspiration and reported good agreement between the estimated and

measured values. Moran et al. (1989) reported that composite temperature

measured from nadir overestimated the aerodynamic temperature. Huband and

Monteith (1986) compared radiative crop temperatures measured at 55 ° zenith view

angle with aerodynamic temperatures estimated from profiles of temperature and

wind speed and concluded that the composite temperature was consistently 1.0 K

lower than the aerodynamic temperature. Hatfield et al. (1984) used remotely-sensed

temperatures measured from a view zenith angle of 60 ° to estimate sensible heat flux

and reported that it underestimated the composite temperature. The above citations

55
suggest that use of radiometric temperatures obtained from view zenith angle other

than 40" or 45 ° for radiation and energy balance studies overestimated/

underestimated the actual values while those taken from or near the 40 ° view zenith

angle closely approximated the actual values. From the above findings and those of

this study it is reasonable to conclude that Tm is the representative temperature to

use for estimating longwave radiation or sensible heat flux using remote sensing

techniques.

From Fig. 5.1 it is evident that the mean surface temperature will be

overestimated or underestimated by as much as 20% depending on the degree of

ground cover and viewing angle. This translates into an error of about 6°C for a

surface temperature of 30°C. An error of this magnitude can lead to an error of

more than 250 W/m 2 in H (Moran et al., 1989).

Modeling the Mean Surface Tem_erature. The

sensor temperature response (Trm), i.e.,

(Trm= (TO4-Tm4)/Tm 4) (5.4)

was calculated for the different view zenith angles. A scatter plot of Trm with

PGAPO is shown in Fig. 5.2. When TO was measured at 40°view zenith angle, the

relative deviation remained very close to 0 regardless of canopy gap fraction. For

view zenith angles less than 40 °, Trm increased with increasing gap fractions while

for angles greater than 40 ° it decreased.

For measurements taken at view zenith angles of 0 ° and 20 ° a direct linear

relationship between Trm and PGAPO was apparent (Fig. 5.2). The model

Trm = 0.106PGAPO (5.5)

relative deviation of the

u

ii

i

I

U

|

i

|

m

m

I

u

II

m :

i

m

m

g

i

I

I

i

B

56 l

I
0.10 ' I ' I ' I '

|

EIm

I0.05

0.00

-0.05

0 0

• 0 • •

O_

0

-0.10 , I , J , t ,

0.0 0.2 0.4 0.6 0.8

PGAPG

=_

Figure 5.2. Scatter plot of Trm versus PGAPO for the different view zenith angles

(o)00, ( • )20 °, ( =)40 ° and ( • )60 ° .

was fit to the data. About 95% of the variation in Trm can be explained by the

variation in PGAPO (Fig. 5.3). The mean composite temperature, Tin, can thus be

calculated as:

Tm = TO(1.0 + 0.106PGAPO) °25 (5.6)

where TO refers to a 0 ° or 20 ° reading.

For measurements taken at 60 °, a negative linear relationship exists between

Trm and PGAPO (Fig. 5.4). The model:

Trm = -0.009 - 0.146PGAPO (5.7)

57
I

0.10 ' I ' I ' I ' 1

E

im

I-.

0.08

0.08

0.04

0.02

0.00

0.0

Trm - 0.106-PGAPO

- R=- 0.95 _ * -

A o

0.2 0.4 0.6 0.8

PGAPO

Figure 5.3. Relationship of Trm taken from nadir (o) and 20 ° (,,) view zenith

angle to PGAPO. The solid line is the equation Trm = 0.106PGAPO.

m

i

m

i

I

II

i

II

|

ww

!

g

I

m

i

i

Z

I

was fitted to the data with an r 2 = 0.83. Tm can be calculated as:

Tm = T60(0.991 - 0.146PGAPO) °z_ (5.8)

Equations 5.6 and 5.8 were applied to the data of the remaining nine plots

acquired in this study. A comparison of the Tm estimated using Eqs. 5.6 and 5.8 with

2 ......... _? : : : . • _ ; 2- - _'2 2 , £ :

the observed values is shown in Figs. 5.5 and 5.6, respectively. Good agreement was

......... ..... = i_

obtained with a RMSE of 0.39 for both models.

i _:ii_ii_!!:iii- ̧_

58

l

m

U

|

i

m

m
r

i

f

7

0.00

-0.01

Q

I I I

0

Trm - -0.009 - 0.146*PGAPO

r' - 0.83

0

®

J

PGAPO

Figure 5.4. Relationship of Trm measured from 60 ° view zenith angle to PGAPO.

The solid line is the equation Trm -- -0.009 - 0.146PGAPO.

5.4 Summary and Conclusions

The dependence of remotely-sensed surface temperature on viewing angle and

direction has been observed in many studies, but the question of which of the

directional temperature measurements should be used for a given application has not

been fully addressed. Results of this study suggest that the mean composite

temperature may represent the surface temperature used to estimate sensible heat

flux and emitted longwave radiation from partially vegetated surfaces. Mean

composite temperature (Tin) can be obtained by viewing the surface from a zenith

59
angle of about 40*. Temperatures observed at other view angles can differ from Tm

by as much as 20%, depending on the percentage of ground cover and zenith view

angle. Tm can be estimated from remotely-sensed temperature measured from

various view zenith angles with the models:

Tm = TO(1.0 + 0.1055PGAPO) "°_

for measurements taken at view zenith angles less than 40" and:

Tm = T60(0.993 -0.1461PGAPO) °z_

for temperatures taken at 60 ° view zenith angle.

I

m

II

i

U

l

i

g

:

II

g

g

60

il

I

g i

!

U

m

J
325

32O

315

miss I.-

u}

,,, 310

g

I305

I I I 1

300 I I l J

300 305 310 315 320 325

Tm, OBSERVED (K)

Figure 5.5. Comparison of mean temperature (Tm) estimated from

Tm=TO(1.0+0.106PGAPO) -°-25with observed values for the (o)nadir and (e)20 °

view zenith angles.

w

61

w
325

320

315

m 310

305

300

300

t I

305 310 315 320 325

Tm, OBSERVED (K)

Figure 5.6. Comparison of Tm estimated from Tm=T60(0.991-0.146PGAPO) "°'25

with observed values. The solid line is the 1:1 line.

l

I

m

m

m

m

i

g

I

I

g

m

ii

z

I

lib

III

g

Ell

g

62

ii

i

II
=

, Estimating Gap Distribution in Partially Vegetated Surfaces from Remotely-Sensed

Information

6.1 Introduction

The probability of gap (PGAPO), defined as the probability that a beam of

radiation will pass through a canopy uninterrupted and hit the background at a point

is an important parameter in remotely sensing the temperature of the components

(canopy and soil) of partially vegetated surfaces. It greatly influences the signal

reaching the remote sensor as demonstrated in the Sees. 4.0 and 5.0 and in the

studies of Kimes (1980), Heilman et al. (1981), Norman and Welles (1983), Kimes

et al. (1985), Matthias et al. (1987) and Sobrino and Caselles (1990). PGAPO is a

complex function of the distribution and density of plants, leaf area index and leaf

angle distribution and varies with viewing angle and direction.

The gap distribution in plant canopies may be estimated using photographic

techniques or by direct measurement (e.g., using a Licor LAI 2000, LiCor, Inc., NE).

These techniques are probably inadequate to meet the gap distribution data needed

to estimate the canopy temperature over large areas. The most common way of

solving for PGAPO is to treat the vegetation in terms of certain geometrical shapes

that can be mathematically described (Jackson et al. ,1979; Norman and Welles,

1983). However, the parameters are difficult to quantify because natural surfaces are

complicated targets.

Remote sensing may be used to estimate gap fractions or PGAP in the

canopy. Goel (1988) reviewed several canopy reflectance models that may be used

for estimating biophysical parameters by inversion technique. The inversion

technique, however, requires multiple view angles. It is not yet economically feasible

63
to obtain instantaneous reflectance data from several view azimuth and zenith angles

using aircraft or spacecraft (Jackson et al., 1990).

Several vegetation indices (e.g., Normalized Difference Vegetation Index

(NDVI), Ratio of NIR to RED, and others) have been used to relate agronomic

variables to reflectance measurements (Pearson and Miller, 1972; Wiegand et al.,

1974; Gardner et al., 1985). Most formulae are based on ratios or linear

combinations of reflectance in different wavebands and exploit differences in the

reflectance patterns of green vegetation and other objects. Simple empirically based

models to estimate gap fraction at nadir/near-nadir (i.e., percent ground cover) from

remotely-sensed information are available but models for estimating the angular

distribution of gap are lacking. The development of pointable and off-nadir viewing

sensors makes it desirable to model the angular distribution of gaps in plant canopies.

The objective of this study is to develop a method for estimating the angular

distribution of gaps in plant canopies using remotely-sensed information for use in

remote sensing of the canopy temperature of partially vegetated surfaces.

6.2 Materials and Methods

The data used in this study were obtained from two experiments that were

conducted in 1989 at the Konza Prairie near Manhattan, Kansas (Blad et al., 1990)

and in 1990 at the University of Nebraska Agricultural Reasearch and Development

Center, Mead, Nebraska (Cornell, 1991). The data sets include canopy bidirectional

reflectance factors (RFs), leaf area index (LAI), gap fractions (PGAPO), mean tilt

angle (MTA) and canopy height.

Bidirectional reflectance factors were derived from reflectance measurements

obtained with a Barnes 12-1000 Modular Multiband Radiometer (MMR). The MMR

64

g

ii

m

II

m

g

ti

|

il

II

il

g

|

m

ql

i

I

Ill

g

=_

II
z

produces a voltage proportional to the scene radiance in seven wavelength bands

from visible to middle infrared and one in the thermal infrared region. A highly

reflective molded halon panel was used as a reference panel. Reflectance factors

were calculated using the procedure described by Robinson and Biehl (1979).

In 1989 measurements were made at two different sites (sites 916 and 906) of

the First International Land Surface Climatology Project Field Experiment (FIFE).

There were five vegetated plots and one bare plot at each site. Dominant vegetation

species in each plot were big bluestem (Andropogon gerardii Vitnam), switchgrass

(Panicurn virgatum L.) and indian grass (Sorghastrum nutans (L) Nash). At site 916

(Grassl) measurements were made on days 216 and 220 and at site 906 (Grass2) on

days 218 and 219. Reflectance measurements were made from seven view zenith

angles (-50 °, -35 °, -20", 0 °, 20 °, 35 °, and 50*) in the solar principal plane.

The 1990 experiment was conducted in an irrigated alfalfa (Medicago sativa

L.) field. The soil was a Sharpsburg silty clay loam. Reflectance factors were

obtained with the MMR from 11 view zenith angles at 10" intervals from -50* to 50 °

in the solar principal plane. Measurements were made over four plots on 18 days

from May through September, encompassing three cutting cycles of alfalfa.

Leaf area index (LAI), gap fraction distribution (PGAPO), and mean tilt angle

(MTA) were measured with a LiCor LAI 2000. This instrument consists of an optical

sensor and a control unit. The sensor is made of five concentric rings of radiation

detectors. The lenses in front of the detectors cause the five rings to see different

portion of the sky. The sensor has a filter that rejects light greater than 0.490/_m,

thus minimizing the light reflected and transmitted by leaves from reaching the

65
detectors. The control unit records light readings above and below the canopy and

calculates LAI, PGAPO and MTA from light interception measurements at five

angles.

Reflectance data collected at mid-day (1200-!500h) in the red (0.630-0.690/_m)

and near infrared (0.760-0.900/zm) wavelength bands corresponding to MMR channel

3 (CH3) and MMR channel 4 (CH4) were used in this study. The two bands were

chosen for their contrasting reflectance characteristics over soil and vegetation.

Reflectance factors in these two channels were used to compute the ratio (SRO)

SRO = CH4/CH3

and the logarithm of the ratio (LRO)

LRO = Ln(CH4/CH3)

6.3 Results and Discussions

(6.1)

(6.2)

Table 6.1 presents leaf area index, average height of vegetation and mean tilt

angle (leaf inclination angle) for five plots of each cover type, i.e., GRASS1, GRASS2

and ALFALFA. There was a very limited range of LAI for the grass plots ranging

from 1.68 to 2.37 forGRASS1 and i.51 to 2.45 for GRASS2. A wider range of LAI

was measured for ALFALFA plots from 0.41 to 4.57. The plot number for

ALFALFA refers to a day of measurement and the data represent the mean of the

measurements from different areas made during the day. Gap fraction distributions

for the three cover types are shown in Figs. 6.1-6.3. In the subsequent discussions,

comparisons made between each cover type will refer to plots with about the same

LAIs.

m

J

I

i

i

ii

II

II

II

Z

II

|

|

lIB

II

m

66

I

i
w

Table 6.1. Leaf Area Index (LAI), canopy height (HGT) cm. and mean tilt

angle (MTA) of the different plots.

GRASS1 : GRASS2 : ALFALFA

PLOT LAI HGT MTA LAI HGT MTA LAI HGT MTA

1 2.03 20 65 1.59 33 61 4.57 68.7 43

2 2.22 25 61 1.51 30 58 0.80 16.3 60

3 2.07 35 59 2.45 39 46 1.39 36.7 50

4 2.37 40 62 1.80 28 59 2.29 42.5 48

5 1.68 25 67 1.98 32 56 0.41 11.9 67

.... m

0.8 l ! I I I

F

w

0.6

L

"< 0.4

0.2

0.0 I I --- I I t

0 20 40 60 80

VIEW ZENITH ANGLE

Figure 6.1. Angular distribution of gaps for Grassl plots (o)P1, (*)P2,

(•)P3, (u)P4, ( 0 )P5.

-- 67
I

0.8

0.6

0.4

0,2

0.0 t [ I I t

0 20 40 60 80

VIEW ZENITH ANGLE

Figure 6.2. Angular distribution of gaps for Grass2 plots (o)P1, (e)P2,

(*)P3, (,')P4 and (*)P5.

R

II

i

BI

J

m

U

I

'_ *O I I ! t I

Ik

,(

0.8

0.6

0.4

0.2

0.0

0 10 20 S0 40 80

VIEW ZENITH ANGLE

Figure 6.3 Angular distribution of gaps for Alfalfa plots (o)P1, (e)P

2, (,)P3, (a)V4, and (*) P5.

m

il

El

J

|

|

m

l

il

i

|

68 i
w

--i i

Trends in Reflectance Patterns in Channel 3. Trends in bidirectional reflectance

patterns for CH3 are shown in Figs. 6.4, 6.5, and 6.6. A curve for the bare soil

reflectance is included in Fig. 6.4 for comparison. The dips at (-35*) of Fig. 6.4 and

at (-20*) of Fig. 6.5 could be due to the shadow of the instrument. Directional

reflectance patterns of partially vegetated surfaces showed approximately the same

trends as the bare soil reflectance. That is, reflectance decreased in the forward

scatter direction (away from the sun) and increased in the back scatter direction

(towards the sun) with a peak at about 20-40* in the backscatter direction. Alfalfa

plots showed the least variation in reflectance with view angle followed by Grass2 and

Grass l plots. For all plots reflectance decreased with increasing ground cover.

16

12

, 1 I I I 1 I I

-50 -35 -20 0 20 :15 SO

VIEW ZENITH ANGLE

(-) BACKSCATTER (÷} FORESCATTER

Figure 6.4. Percent reflectance for CH3 as function of view zenith

angle for Grassl plots (o)P1, (e)P2, (-)P3, (I)P4, (_)P5 and (v)Soil.

Solar zenith angle is 33*.

69
1o

-50 -35 -20 0 20 35 60

VIEW ZENITH ANGLE

(-) BACKSCATTER (+) FORESCATTER

Figure 6.5. Percent reflectance in CH3 as a function of view zenith

angle for Grass2 plots (o)P1, (e)P2, (,)P3, (n)P4 and (I)P5. Solar

zenith angle is 23"

18

10

|,

0

i i ; i 1 i i i I + i

• _ I I J j_ J 1 L t

-SO-40-80-'JO-I0 0 10 l0 80 410 SO

VIEW ZENITH ANGLE

1-).CXSC,TrB_ (+1FORESCATTtn

Figure 6.6. Percent reflectance in CH3 as a function of view zenith

angle for Alfalfa plots (o)P1, (e)P2, (,)P3, (re)P4 and (#)P5. Solar

zenith angle ranged from 17-35".

I

I

i

|

m

i

i i

__ !

m +

|

i j

i

g

II

I

d

+

H

m

i

70

-- =

Em
= ,

w

L

L :

Differences in trends in the forescatter and backscatter directions are due to

differences in illumination condition of the canopies in these two directions. As the

view angle is increased in the forescatter direction, more shaded components come

into view of the instrument causing a decrease in the overall reflectance. In the back

scatter direction, the strong back scattering characteristic of soil and vegetation

caused an increase in reflectance.

Differences in the magnitude of response to increasing view zenith angle

between each cover types can be explained by the changes in the proportion of the

soil and vegetation that is viewed by the sensor (Figs. 6.1, 6.2 and 6.3). For alfalfa

plots the proportion of ground viewed by the sensor changes very little with view

angle compared to Grass1 and Grass2 plots that had more or less the same LAI as

alfalfa. Thus, the contribution of the soil to the overall reflectance changed very little

for alfalfa, while for Grass1 and Grass2 the contribution of the soil greatly diminished

as the view zenith angle increased.

Reflectance Patterns in Channel 4. Trends in reflectance patterns for channel

4 differed for each type of vegetation (Figs. 6.7, 6.8 and 6.9). Grassl showed an

increase in reflectance in both the forward and backscatter direction from a minimum

near nadir. Grass2, on the Other hand, showed a marked increase in reflectance in

the backscatter direction and a slight tendency for decrease reflectance in the

forwardscatter direction with a minimum at about 20*. Alfalfa decreased in

reflectance from the backscatter to the forward scatter direction with a minimum at

about 30 ° to 40*.

71

w
III

lU

E

e0

46

sO

15

i i r i i !

I I ! I I , ] ]

-80 -38 -20 0 20 la 80

VIEW ZENITH ANGLE

(-) BACKSCAlr'TER (*) FORESCkTI'ER

Figure 6.7. Percent reflectance in CH4 as a function of view zenith

angle in the solar principal plane for grassl plots (o)P1, (e)P2, (,)P3,

(..)P4, (*)P5 and (v)Soil. Solar zenith = 33*.

i

I

i

m

i

m

i

g

z

!

SO

4O

30

2O

10

i i l | I i i

-80 -35 -20 0 10 35 60

VIEW ZENITH ANGLE

(-] BACKSCATTER (*) FORESCATTER

Figure 6.8. Percent reflectance in CH4 as function of view zenith angle

in the solar pricipal for Grass2 plots (o)P1, (e)P2, (,)P3, (..)P4,

(•)P5. Solar zenith angle is 23 °.

_=

B

g

i

i

J

Z

B

il

i

m

l!

72

I
w

70

6O

SO

i.°

30

2O

10

I I I I ! t I l I I I

__l I I I I I I I I I

-ilO-40-.tO-|0-lip • 10 |0 liO 4,0 i0

VIEW ZlENi't'H ANGLE

(-) BACK$CATTER (*) FORESCATTER

Figure 6.9. Percent reflectance in CH4 as a function of view zenith

angle in the solar principal plane for Alfalfa plots (o)P1, (*)P2, (,)

P3, (m)P4 and ( * )P5.

=

w

w

Differences in reflectance pattern trends in the forescatter direction can be

explained by the differences in the geometric structure (mean tilt angle) of each types

of vegetation (Table 6.1). Canopies with more vertical components (indicated by

higher mean tilt angle) allow deeper penetration of radiation into the canopy (less

shading) than do canopies with more horizontal components. In the forescatter

direction the portion of shaded components viewed by the sensor increases as the offnadir view angle increased. This results in lower reflectance. This shading effect can

be off-set by another mechanism explained as follows: In the NIR bands, vegetation

canopies are characterized by high reflectance and high transmittance. For canopy

structures with more vertical components the underside of the leaves are viewed by

the sensor as the off-nadir view is increased. Since canopies have high transmittance

in the NIR band, some of the radiation transmitted through the leaves can reach the

sensor directly resulting into a higher signal recorded by the instrument. This effect

73
is observed at viewing angles greater than the leaf inclination angles.

RATIO (Ctt4/CH3). Ratios of CH4/CH3 for the different cover types are

shown in Figs. 6.10, 6.11, and 6.12. Compared to the individual bands the ratio

showed a very definite pattern. It increased with increasing off-nadir view angle. The

scattering effect of the soil appears to cancel out as suggested by the nearly constant

ratio for the bare soil. The anisotropic scattering properties of the canopies are still

apparent as indicated by_h_asyminetry in the forescatter and backscatter directions.

The response to changing view angle differed for each type of vegetation. Alfalfa

showed the least response while Grassl showed the greatest change with viewing

angle. For each cover type the ratio increased with increasing amount of vegetative

cover.

16

12

___ I 1 | I I 1 _.....--l_

-50 -35 -20 O 20 35 80

VIEW ZENITH ANGLE

(-) BACKSCATTER (+) FORESCATTER

Figure 6.10. Ratio of CH4/CH3 as a funciton of view zenith angle in

the solar principal plane for Grassl plots (o)P1, (e)P2, (-)P3, (re)P4,

(,)PS,

m

I

i

I

=

!

J

m

U

mlW

m

m

ii@

u

m

g

m

m

74

s
i I I I i I I

I L I I I I I

-80 -35 -20 0 20 35 80

VIEW ZENITH ANGLE

(-) BACK$CATTER (+) FORESCATTIER

Figure 6.11. Ratio of CH4 to CH3 as a function of view zenith angle

in the solar principal plane for Grass2 plots (o)P1, (e)P2, (A)P3,

(re)P4 and (0)PS.

w

E

3O

20

10

I I I I I I I I I I I

0 ! I l I l I I I I I I

-60-40-10-20-10 0 10 |0 30 40 80

VIEW ZENITH ANGLE

(-) BACKSCATTER (+) FORESCATTER

Figure 6.12. Ratio of CH4/CH3 as a function of view zenith angle in

the solar principal plane for Alfalfa plots (o)P1, (e)P2, (,)P3, (m)P

4 and ( • )P5.

75
Relationship of PGAP0 to Reflectance Factors (RF) and Ratio (Ch4/CH3).

As the sensor is moved from nadir to off-nadir view angles the proportion of soil in

the field of view of the instrument is decreased. In spectral bands where the soil is

more reflective than the vegetation (e.g., infrared) one expects the overall reflectance

to decrease with increasing view angle. The reverse is true for wavelength bands in

which the canopy reflects strongly (e.g., near infrared). The anisotropic scattering

properties of the soil significantly influence the observed directional reflectance in the

visible band and tend to dominate the reflectance distribution for sparse canopies.

From reflectance pattern trends for the individual bands it appears that there

is no definite relationship between RFs and PGAPO. The ratios of CH4 to CH3

have a definite relationship to PGAPO by virtue of PGAP's relationship to view

angle. From the view angle responses, however, it follows that there will be a

different relationship between the ratio and PGAPO which depends on the cover

type.

The relationship between the CH4/CH3 ratio and PGAPO is presented in Fig.

6.13. Due to a very limited range of LAI sampled for grass plots, only the data for

alfalfa are shown. Because of differences in response in the backscatter and

forescatter directions, the plotted points were designated as measured from the

forescatter or backscatter directions. The ratio exhibited a negative exponential

relationship with PGAPO as shown by the equation,

PGAPO = 1.115exp(-O.154SRO) (6.3)

(r: = 0.98). A slightly different equation was developed for each view direction. The

equations,

i

J

I

III

II

i

I

B

11

m

m

u

B

U

il

i

76

D
w

PGAPO -- 1.134exp(-0.164SRO)

with r2=0.97 and

PGAPO = 1.096exp(-0.144SRO)

(6.4)

(6.5)

with r2=0.98 fit the data for the backscatter and forescatter directions respectively.

w

w

,It

1.0

0.6

0.6

0.4

0.2

O.0

! I

_- I.lS4EXP(-O.IIkI, Imi_)

R" - O.OII

_

10 20 30

8RO

Figure 6.13. Relation ship of PGAPO to ratio (SRO). The symbols

(o) and (e) refer to data taken from the backscatter and forescatter

directions respectively.

The logarithm of the ratio (LRO) on the other hand showed a strong linear

relationship with gap (Fig. 6.14). The equation,

PGAPO = 1.052- 0.333LR0 (6.6)

fitted the entire data set with r2=0.97. For the backscatter and forescatter directions,

the equations

PGAPO = 1.058- 0.346LRO (6.7)

with r2=0.97 and

PGAPO = 1.059 - 0.324"LR0 (6.8)

77
with r:=0.98 fitted the data for the forwardscatter and backscatter directions,

respectively.

2

/\

_o . 3UtO

0.6 o_. o.le

0,4 oe._

0.2 o

o.o , ,, i u_e _ ___

0 1 2 3

LRe

II

=

i

i=i

II

B

m

m

im

i

II

i

Figure 6.14. Relationship of PGAP0 to the logarithm of the ratio

(LRO). The symbols (o) and (o) refer to data taken in the

backscatter and forescatter directions respectively.

i

I

Equations 6.3 and 6.8 were applied to the remaining 13 alfalfa plots.

Comparison of the measured PGAPO with the estimated values using the SRO and

the LRO models are shown in Figs. 6.15 and 6.16. The scatter in the data points

could have been caused by the changes in soil moisture status, amount of litter on the

ground and structural changes in the vegetation during the 137 days of the data

gathering period. Overall, there is a fairly good agreement with a RMSE of 0.09 for

both models.

78

J

m

i

il

M

i

iii

i

z

m

ii
w

i

1.O

0.8

0.6

0.2

0.0

0.0

_o

oo_../,,./g - . • -

OOe __

000_

%.

-_o_ • •

_.1

0.2 0.4 O.0 0.8 1 .(

OBSERVED PGAPe

Figure 6.15. Comparison of PGAPO estimated from

PGAPO=l.llexp(-0.154SRO) with the observed balues for the (o)

backscatter and (e) forescatter directions. The solid line is the 1:1

line.

L

w

L.

w

_'_7_

6.4

1.0

0.8

i 0.6 -- 0.4

O.2

0.0

0.0

oI_- °

QO I _ _

O_--' I 1 I

0.2 0.4 0.6 0.8 1.0

OBSERVED PGAPO

Figure 6.16. Comparison of PGAPO estimated from PGAPO=l.052-

0.333LRO with the observed balues for the (o) backscatter and (e)

forescatter directions. The solid line is the 1:1 line.

Summary and Conclusions

This study provided a good opportunity to compare the directional reflectance

79
patterns of surface covers which differed in geometric structure. The type of surface

influenced

directions.

the spectral reflectance in

Alfalfa, with a planophile

the forescatter and backscatter viewing

canopy structure was less responsive to

changing zenith view angle than grass, an erectophile canopy. There was a unique

relationship between the directional reflectance and view zenith angle for each cover

type.

Directional reflectance patterns of sparse canopies are strongly influenced by

the backscattering properties of soil. This influence can be observed even at

moderate percentages of ground cover. The CH4/CH3 ratio did not seem to be

influenced by the backscatter effects of the soil but, the anisotropic scattering effects

of vegetation were still observed.

The ratio (SRO) and the logarithm of the ratio (LRO) exhibited a strong

relationship with PGAPO. The high coefficient of determination (r 2) indicates that

it should be possible to estimate gap distribution in plant canopies from directional

reflectance measurements.

80

I

I

i

i

g

I

i

m

g

i

m

I

m

g

m

I

l

g

ii

W

u

il

m
w

;,._,

7. Summary and Recommendations

Reflectance factors tend to differ between sloped and leveled surfaces. Although

various factors probably contribute to this (including species differences, vegetative cover and

background cover), surface roughness and thus shadows may play an important role. More

studies under controlled conditions are needed to find the significance of surface roughness

on reflectances from sloped grass-cover surfaces.

The incoming shortwave component was underestimated on the average by 14 Wm 2

(compared to 4 Wm "2 in previous studies). The reflected shortwave component was

overestimated by about the same magnitude as in previous studies, even though reflected

radiation was measured in an azimuthal plane other than the solar principal plane. Albedo

estimates were in poor agreement with measured values (10-17% MRE) compared to 4%

MRE with FIFE 87-89 level sites. Large errors in incoming and reflected shortwave

components contributed to the general overestimate of albedo. The cosine correction to the

total incoming shortwave and the lack of coincident measurement of Eppley PSP with the

MMR may be factors contributing to the overestimate. Bare soil albedos were greatly

underestimated. Nadir derived estimates were in better agreement but often underestimated

the measured value.

Sensible heat flux (H) estimated with remotely-sensed surface temperatures were

calculated and compared to sensible heat flux values measured with micrometeorological

techniques. Overall, the best estimates of H were made using canopy temperatures

measured at the instrument view zenith angle of 40 ° . There was considerable variability,

however, insofar as identifying the optimum view zenith angle with which to view the canopy

at a specific time and under specific wind conditions.

Results suggested that the wind speed affected the optimum view zenith angle. Days

with average wind speeds above 5 m s1 coincided with the days on which the nadir and 20 °

view zenith angles provided canopy temperatures that resulted in the best estimates of

sensible heat flux. On days with average wind speeds of less than 4 m s"1, the 40 ° and 60 °

81
view zenith angles provided the best estimates of sensible heat flux. Wind speed should

therefore be considered when selecting the appropriate view zenith angle for measuring

canopy temperature to estimate sensible heat flux over prairie grasslands.

The temperature measured by a remote sensor is a composite temperature of the

surfaces (e.g., canopy and its background) exposed to the instruments. It varies with view

angle and direction. In this study we addressed the problems encountered in thermal remote

sensing when the surface is partially vegetated, i.e., how can one isolate the canopy

temperature from the composite temperature and how does one obtain composite

temperature which is representative of the surface.

Results indicated that the composite temperature (TO) can differ from the canopy

temperature (Tc) by as much as 13 K depending on the percentage of ground cover and the

view zenith angle. The deviations of the composite temperature from the canopy

temperature were found to be linearly related to the gap fraction (PGAPO) along the

viewing direction and exponentially related to the leaf area index divided by the cosine of

the view zenith angle (LAI/cosO). Two models, Tc = TO*(l+0.231*PGAPO) -°'25 and

Tc = TO*(l+0.527*exp(-0.804*LAI/cosO) -°'z5 were developed to estimate the canopy

temperature from the composite temperature. Good agreement was obtained between the

estimated and observed values (RMSE < 0.56). Inputs to these two models can be acquired

by remote sensing techniques. The directionality of remotely-sensed temperature was

investigated to address the problem of obtaining the representative composite temperature

of partially vegetated surfaces. It was assumed that the mean or average of the temperature

readings taken from different azimuthal and zenith view angles would represent the

temperature of partially vegetated surfaces for radiation/energy balance studies. Results

indicated that the mean composite temperature can be obtained by viewing the surface from

a zenith angle of about 40 °. Temperatures measured from other view zenith angles can

differ from the mean temperature by as much 20% depending on the view zenith angle and

percentage of ground cover. Temperatures observed from view zenith angles less than 40 °

82

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overestimated the mean temperature while those observed from zenith view angle greater

than 40* underestimated the mean temperature. The relative deviation of the directional

temperatures from the mean was linearly related to the gap fraction along the view

direction. Two models, Tm = TO*(1.+ 0.105*PGAPO) "°'25 and

Tm- T60"(0.99- 0.146*PGAPO) "°'z_ were developed to estimate the mean composite

temperature from directional temperatures measured from view zenith angles less than 40*

and from view zenith angle of 60 °, respectively. Good agreement was observed between the

estimated and measured values (RMSE=0.39).

Corroborative evidence obtained from the scientific literature suggests that for

applications (e.g., radiation/energy balance studies) which use remotely-sensed surface

temperatures as an input, better agreement between the estimated and actual values of

longwave radiation or sensible heat fluxes can be obtained if the temperature used in the

calculation is adjusted to the mean temperature (Tm).

The distribution of gap (PGAPO) in plant canopies is an important parameter in

remote sensing of the surface temperature of partially vegetated surfaces. The possibility

of estimating gap distribution from directional reflectance measurement was investigated to

enable the predictions of canopy temperature and mean composite temperatures using

remotely-sensed information as the primary input. Directional reflectance data obtained in

the solar principal plane for two cover types (prairie vegetation and alfalfa) which differ in

canopy structure were analyzed. Results indicated that for sparse canopies the directional

reflectance in the red (0.630-0.690#m) and NIR (0.760-0.900/_m) wavelength bands,

corresponding to MMR CH3 and CH4 respectively, are strongly affected by the anisotropic

scattering properties of the soil background and vegetation. The asymmetry of the

directional reflectance patterns in the forescatter and backscatter direction suggest that the

directional reflectance in the individual bands measured in the solar principal plane was not

a good predictor of gap distribution. Taking the ratio of the two bands (CH4/CH3)

effectively canceled out the anisotropic scattering effects of the soil background. The gap
distribution was strongly related to this ratio. The high coefficient of determination (r 2) for

the PGAPO vs. SR suggests that it is possible to estimate gaps from reflectance

measurements.

The temperature models developed in this study were based on data collected from

prairie vegetation and are intended for applications that require mid-day temperature

measurements. Although the models are empirical, they have a theoretical basis and hence

may be applied to similar cover types. For example, they can probably be applied to small

grain crops which are planted or drilled in narrow rows but they may not work for crops

grown in wide rows. It is recommended that the models be tested on agricultural crops

and/or models developed for other surface cover types using similar procedures. Solar

zenith angles other than the one used in this study should be considered in developing the

models.

The validity of the mean composite temperature (Tm) as the representative

temperature to use in radiation/energy balance studies of partially vegetated surfaces needs

further verification and be validated through future experiments.

The estimation of gap distribution from reflectance measurements needs further

study. As indicated by the results of this study, a unique relationship exists between PGAPO

and directional reflectance for each cover types. Therefore, it is recommended that models

to estimate gap distribution in plant canopies be developed for other cover types, taking into

consideration solar azimuth and zenith angles other than the ones used in this study.

The results of this study provide another step towards the operational use of remote

measurements of surface temperature for radiation and energy balance estimates and for

the use of remotely-sensed plant temperature to assess vegetative conditions.

II

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