COMPTON J. TUCKER*LEE D. MILLER

NASA/Goddard Space Flight CenterGreenbelt, MD 20771

Soil Spectra Contributions toGrass Canopy SpectralReflectanceThe contribution of a soil-litter background to the compositegrass canopy spectra can, in some grassland cases, beextracted and quantified.

INTRODUCTION

T HE CONTRIBUTION of the underlying soilspectra to the composite canopy spectral

reflectance of vegetated surfaces has re­cently been addressed (Colwell, 1974; Dris­coll and Spencer, 1972; Richardson et al.,1975; Wiegand et al., 1974). Remotelysensed data of vegetated surfaces could beanalyzed more accurately if the contributionof the underlying soil spectra were knownand thus possibly could be removed from thecanopy spectral radiance to yield more in­formation about the vegetation. It would

spectra-optical techniques for measuringaboveground standing crop biomass of grass­lands. Early in the study it became apparentthat the soil or background spectra domi­nated low biomass grass canopy spectralradiance or reflectance. It was thereforenecessary to examine the contribution of thesoil spectra to the composite grass canopyspectral reflectance as a function of biomassand wavelength.

BACKGROUND

Let us assume that the soil background(realizing that the "soil" background is actu-

ABSTRACT: The soil or background spectra contribution to grasscanopy spectral reflectance for the 0.35 to 0.80 fJ-m region was inves­tigated using in situ collected spectral reflectance data. Regressionanalysis was used to estimate accurately the unexposed soil spectralreflectance and to quantify maxima and minima for soil-green vege­tation reflection contrasts.

generally be impractical to take detailed soilspectra measurements of the area in ques­tion because the vegetation canopy obscuresthe soil surface and the time involved forthese in situ measurements is usually pro­hibitive.

The work reported in this article was partof a larger effort sponsored by the u.S. In­ternational Biological Program's GrasslandBiome. The data were collected with the ob­jective of evaluating non-destructive

"National Academy of Sciences-NationalAcademy of Engineering Research Fellow.

ally a soil-litter background) has a charac­teristic spectra for a given particular area(Figure 1). The soil spectra for the fieldstudy site was a monotonically increasingfunction with wavelength over the spectralrange of 0.35 to 1.00 fJ-m. The dry soil surfacewas more highly reflective than the wet soilsurface. Dry refers only to the uppermostlayer as determined by visual inspection.

The plant canopy on the grassland soil sur­face will be viewed as some statistical en­semble of foliage elements superimposedover the soil-litter background. The densityof the ensemble offoliage elements will be a

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,

Vol. 43, No.6, June 1977, pp. 721-726.721

722 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1977

function of biomass. The incoming spectralirradiance will interact with the grass canopyand, depending upon the vegetational den­sity or biomass, can also interact with thesoil background. The interaction(s) with thesoil background become less and less as thevegetational density or biomass increasesuntil the asymptotic spectral radiance or re­flectance is reached (Tucker, 1977). In­creases in the vegetational density orbiomass effect no change in the canopyspectra when the asymptotic spectralradiance or reflectance has been reached.This can be explained because the canopy isof sufficient density and thickness to preventthe penetration of the incident spectral ir­radiance to lower biomass levels of thecanopy. Hence, the incident spectral ir­radiance does not interact with additional(and lower level) biomass. As the vegeta­tional density increases to the point wherethe spectral reflectance begins to asymptoteat a given wavelength, the soil spectra con­tribution to the canopy spectra is minimal atthat wavelength. When the canopy is of suf­ficient density or biomass to result in theasymptotic spectral reflectance, there is nosoil spectral reflectance contribution to thecomposite canopy spectral reflectance. Thusthe relative contribution of the soil spectra tothe composite canopy spectra is inversely re­lated to the biomass or vegetation density.

The asymptotic spectra for green grasscanopies were quite different from those forthe soil surface at the study site (Figure 1).As plant growth and development result inincreasing amounts of green plant materialabove the dry soil surface, the canopyspectra changes. In regions of the spectrumwhere absorption occurs, the compositecanopy spectra decreases and approachesthe asymptotic green reflectance spectra. In

OO.r-----------------,...

GREEN VEGETATION PLOT 20074(TOTAL DRY BIOMASS =530 g/m2)

\-----_...--------_......--_.--_._-----

~.36 OAO 0.45 0.50 0.56 O.eo 0.86 0.70 0.75 0.80 0.85 0.90 0.95 1.00

WAVELENGTH hIm)

FIG. 1. Spectral reflectances for dry soil, wet soil,and the asymptotic green reflectance. The dry soiland wet soil are for five bare soil plots measuredwhen dry and wet, respectively. The asymptoticgreen reflectance curve is from a plot of bluegrama grass having a total dry biomass of530 g/m2 .

the near infrared region of -0.71 to 0.74 J.Lm,the composite canopy spectra does notchange appreciably. In spectral regionswhere minimal or no absorption occurs, suchas the -0.74 to 1.20 J.Lm region, the compos­ite canopy spectra increases and ap­proaches the asymptotic green reflectancespectra. Discrimination of vegetationbiomass, for example, is strongly dependentupon the soil surface-vegetation spectral re­flectance or radiance contrast. For thisreason, some wavelengths are far superior toothers for discrimination of green vegetationbiomass (Tucker and Maxwell, 1976).

The effectiveness of some wavelengthsdecreases while that of others increaseswhen the soil surface is wet (Figure 1). Thesoil-vegetation reflectance contrast de­creases in the red and blue regions while itincreases in the photographic infrared re­gion. This has also been reported by Colwell(1974).

Theoretical considerations indicate thatthe soil spectra can be extracted by regress­ing canopy spectral reflectance againstsome measured biophysical characteristic ofthe canopy such as total biomass, greenbiomass, brown biomass, chlorophyll, andleaf water concentration. The simple modelused for this extraction was a general linearregression model of the form:

Reflectance). = !3o). + !31)' (plot characteristic)+ Error). (1)

Note that the Reflectance)., !3o)., !31)., and Er­ror). are all functions of wavelength.

METHODS AND ANALYSIS

STUDY LOCATION

The experimental results reported hereinwere obtained on native shortgrass prairie atthe IBP Grassland Biome Pawnee Site, thefield research facility of the Natural Re­source Ecology Laboratory, Colorado StateUniversity, located on the USDA Agricul­tural Research Service Central Plains Ex­perimental Range about 35 miles northeastof Fort Collins, Colorado. Field measure­ments were made in the Ecosystem StressArea (ESA) on control, irrigated, and/or ni­trogen fertilized plots.

Prairie vegetation is dominated by variousspecies of grasses. One species, blue grama(Bouteloua gracilis (H.B.K.) Lag.), com­prises about 75 percent of the dry weight ofthe gramineous vegetation at the PawneeSite (Uresk, 1971). For this reason, plots ofblue grama grass were selected for ex­perimentation purposes.

SOIL SPECTRA CONTRIBUTIONS 723

In situ measurements of spectral reflec­tance were obtained with the field spec­trometer laboratory designed and con­structed for the IBP Grassland Biome Pro­gram to test the feasibility of spectro-opticallymeasuring the above ground plant biomassand plant cover (Miller et aI., 1976).

DATA USED

Forty III m2 plots of blue grama grass weresampled in situ by spectroradiometric meas­urement over the 0.350 to 0.800 Mm regionat every 0.005 Mm interval with the mobilefield spectrometer laboratory. All measure­ments were made normal to the ground sur­face and were sampled in early September,1971.

Immediately after the reflectance meas­urements were completed, each plot wasclipped of all standing vegetation. The clip­ped vegetation was placed in a plastic bag,qUick-frozen on dry ice, and subsequentlytaken into the laboratory for weighing,chlorophyll extractions, and drying.Laboratory determinations were made fortotal wet biomass, total dry biomass, leafwater content, dry green biomass, drybrown biomass, and total chlorophyll content(Table 1).

REGRESSION ANALYSIS

A regression approach was undertaken toapproximate the relationship(s) existing be­tween the six sampled canopy variables andthe spectral reflectance at each 0.005 Mm in­terval. Standard regression notation afterDraper and Smith (1966) will be used anddenoted as a function of wavelength by thesubscript A.

Statistically, both variables were sampled

with error. However, the reflectance var­iance for all 40 replications was approxi­mately the same. The reflectance measure­ments were considered as 91 independentregressions over the 0.350 to 0.800 Mm spec­tral region because the reflectance was meas­ured at a fixed wavelength. Because of thestatistical model used, reflectance could betreated as the dependent variable eventhough it was sampled with error for a fixedwavelength. The biomass and spectral re­flectance measurements were considered asa bivariate normal distribution of the form

and, for fixed X = x,

whereYx = measured spectral reflectance;X =measured biomass, chlorophyll, leaf

water, etc.; and(T~, = measurement error associated with

spectral reflectance.Although the regression model used was a

simple linear regression model, the interac­tion of solar irradiance with a plant canopy isvery definitely nonlinear. However, thefunctional relationship between spectral re­flectance and the biophysical plot variablessampled for the experimental plots in ques­tion could be accurately approximated by alinear model. If the range of total wetbiomass values for the experimental plotswould have exceeded 500 gim 2 , then a non­linear model would have been necessary.

TABLE 1. STATISTICAL SUMMARY OF THE BIOPHYSICAL CHARACTERISTICS OF THE SAMPLE PLOTS.

A STATISTICAL DESCRIPTION OF THE VEGETATIVE CANOPY CHARACTERISTICS OF THE 40 1/4 m 2 SAMPLE

PLOTS OF BLUE GRAMA SAMPLED IN EARLY SEPTEMBER, 1971.

Coef. ofSample Range Mean SD variation SE

Wet total 70.83- 261.31 134.40 51.44 21.25biomass (g/m2 ) 491.22Dry total 41.50- 168.55 90.81 53.88 14.36biomass (g/m2 ) 337.84Dry green 17.12- 89.38 50.15 56.11 7.93biomass (g/m2 ) 185.04Dry brown 20.40- 82.41 48.54 58.90 7.68biomass (g/m2 ) 186.42Leaf water 28.03- 92.75 50.93 54.91 8.05(g/m2 ) 190.80Chlorophyll 53.02- 319.58 238.73 74.70 37.75(mg/m2 ) 778.97

724 PHOTOGRAMMETRIC ENGINEERING & REMOTE SE SING, 1977

WAVELENGTH h.m)

FIG. 2. F value curve resulting from the simplelinear regression model analysis of variance forthe regression between reflectance and dry greenbiomass for each of the 91 wavelength intervalsbetween 0.35 and 0.80 p.m. The horizontal (-5-)line represents the 0.5 percent level of signifi­cance for 1 and 38 df.

The regression model in Equation 1 wasestimated at the 91 0.005 /Lm intervals be­tween 0.350 to 0.800 /Lm using the 40 repli­cated measurements of spectral reflectanceand the canopy variables. The simple corre­lation coefficient (r), the coefficients of de­termination (r2 ), the resulting regressionmodel analysis of variance F values (F) (Fig­ure 2), and the final regression equations ateach of the 91 0.005 /Lm wavelengths werecalculated (Table 2). These statistics, ex­pressed as functions of wavelength, definedthe relative sensitivity on a spectral basis be­tween the various biophysical variables ofthe sample plot and the plot's spectral reflec­tance.

The regression equation intercept (/3ox in

Equation 1) was considered as the contribu­tion of the soil surface to the compositecanopy spectral reflectance. Restating Equa­tion 1 we have

CANOPY RFLx = SOIL RFLx + /31X(BIOMASS (g/m2)) (4)

The terms "CANOPY RFL" and "SOILRFL" were percentages, the biomass (in thisexample) was in glm2, and /3/A has units ofpercent/(glm2) or the weighting coefficient ofthe respective biophysical plot variable.

The regression model simplifies when thebiophysical plot variable was zero to the ex­pression:

CANOPY REFLECTANCEx= SOIL REFLECTANCEx (5)

The relative contribution of the underlyingand somewhat obscured soil surface was re­lated to the composite canopy spectral re­flectance and plot variable by the generallinear regression model with a minimum ofassumptions.

These assumptions included the follow­ing:

(1) The biomass range of the sampledgramineous canopies varied from 0.0 glm2

to 500 g/m2 (Table 2),(2) A linear relationship existed between

canopy spectral reflectance and the re­spective plot variable, and

(3) The canopy spectral reflectance was meas­ured in situ.

RESULTS

The estimated regression equation inter­cepts plotted as a function of wavelengthclosely resemble a soil spectra curve (Figure3). This curve was considered as the result-

TABLE 2. TABULAR RESULTS FOR THE SIMPLE LINEAR REGRESSION BETWEEN REFLECTANCE AT0.775 p.m AND THE DRY GREEN BIOMASS CLIPPED FROM 40 1/4 m2 IN SITU SAMPLE PLOTS OF

BLUE GRAMA.

Wavelength = 0.775 p.m:Correlation coefficient (r)Coefficient of determination (r2 )

Standard error of the estimate

Regression equation: Estimated reflectance (0/0) = 16.5695 + 0.0527* (Green Biomass-glm2 )

= 0.8493= 0.7213= 1.6657

SourceRegressionResidualTotal

df1

3839

Analysis of variance tableSum of Meansquares squares

272.8683 272.8683105.4277 2.7744378.2959

F ratio98.3517

P(F>comp F)0.0000

SOIL SPECTRA CONTRIBUTIONS 72550,.---------- --,

45

40

35

interesting to note that the minimum valuesfor /3110. lie in the 0.67 to 0.69 J.Lm region ofstrong in vivo chlorophyll absorption whilemaximum values for /3110. lie in the 0.75 to 0.80

30

2550,.--------------,

-O·~.3S':---,-OL40---.,.O....,..--,O..L.50,--O:-'.56=--.,-'O....,.--,JO...'---,-O.L70---.,.O":::.75----:-'O..

WAVELENGTH (pm)

-0.05

30

15

40

35

~

zw~ 25

20

-0.04

-0,03

...~ 0.01U~ o.oo,I---~-------__t--.,

8 -0.01

X -0.02

0.02

0."

0."

0.05

0.06,.-----------__.,,--

----. Plusond m,nul(±)on. sTonClOf'd a,,,lolion45 fur ~ '-Ofl ,~",o (urns

--s-- M.a" ...I1~I... (;U'...s

---1-- I""1'C1!P15 ploUe<! ..., WO....'-"9IFl

0.45 0.50 0.55 0.60 0.65 0:70 0.75 080

WAVELENGTH ("m)

FIG. 4. Comparison among the mean of six com­puted soil spectral reflectances and the mean ofsoil spectral reflectance measured in the samearea. -S- represents the mean spectral reflectanceof 25 soil curves. - represent plus and minus onestandard deviation of the mean. The + curve rep­resents the mean of the six regression interceptcurves. Note the close similarity between the twomean curves from 0.350 JLm to 0.800 JLm. The meansoil curve was formed by averaging 25 soil spectralreflectance curves for five bare soil plots with fiverepetitive curves per plot. The intercept meancurve was formed from the individual interceptcurves (Figure 3) resulting from the simple linearregression between spectral reflectance and theplot variables for the 40 plots of blue grama grass.

FIG. 5. f3 .. values plotted as a function ofwavelength for the 910.005 JLm intervals between0.350 and 0.800 JLm. The f31A values were derivedfrom the series of regressions between spectral re­flectance and the dry green biomass for the 40plots sampled. Maxima and minima values for f31Arepresent the greatest SOil-dry green biomass re­flectance contrast(s).

040 Q45 050 055 060 0.65 0.70 075 0.80

WAVELENGTH (/1m)

ing series of soil spectral reflectances fromEquation 5.

The spectral intercepts (i.e., /30/0.) curvesutilizing each of the six different availablebiophysical measures of the vegetationcanopy variables were very similar, as in allcases the underlying soil surface was in factthe same and should have the same spectralreflectance (Figure 3).

Regardless of the biophysical measure ofthe canopy used, the regression interceptsclosely resembled those of the other five in­tercept curves. All six of these curves in turnclosely resembled the independently meas­ured spectral reflectance of bare soil meas­ured in the same area (Figure 4).

It was interesting to note that the soilspectra estimate was reasonably accurate inregions of the spectrum where there was alack of regression significance betweencanopy spectral reflectance and the respec­tive plot variable(s). The coefficient of de­termination (r2 ) values and F ratios (Figure2) resulting from the regression betweencanopy spectral reflectance and dry greenbiomass indicated a lack of statistical signifi­cance in the 0.530 to 0.600 J.Lm and 0.700 to0.740 J.Lm regions of the spectrum. However,the estimate of the soil spectra in these sameregions was as accurate as in other areas ofthe spectral interval studies where strongsignificance existed. The accuracy of the soilspectra estimate was apparently not depen­dent upon regression significance.

The plot of /3, /0. as a function of wavelengthindicates the departure from the soil spectrafor green vegetation (Figure 5). Maxima andminima correspond to those wavelengthswith the greatest soil-green vegetation con­trast for the soil present at the study site. It is

20

FIG. 3. Comparison between the spectral inter­cept curves for the six independently sampledcanopy variables. Note the very close similaritybetween the six curves.

15

726 PHOTOGRAMMETRIC ENGI EERING & REMOTE SENSING, 1977

j.l.m region of enhanced photographic in­frared reflectance.

An interesting extrapolation of Figures 1and 5 may explain some of the findings ofRouse et al. (1974) who reported greatergrassland biomass mapping utility usingLandsat MSS6 (0.70 to 0.80 j.l.m) than MSS7(0.80 to 1.10 j.l.m). If we assume that the soilspectra data presented in Figure 1 applies tothe American Great Plains test areas used byRouse et al. (1974), it is apparent that greatersoil-green vegetation contrast occurs inMSS6 (0.70 to 0.80 j.l.m) than MSS7 (0.80 to1.10 j.l.m). This could explain the greater util­ity ofMSS6 vs. MSS7 for Rouse et al.'s (1974)study areas.

SUMMARY

(1) The contribution of a soil-litterbackground to the composite grasscanopy spectra can, in some grasslandcases, be extracted and quantified.

(2) Applied regression analysis was foundto accurately estimate the soil spectralreflectance and to quantify thewavelengths of maximum soil-greenvegetation reflectance contrast.

(3) The near infrared soil-green vegeta­tion reflectance contrasts may explainwhy Landsat MSS6 has been found insome situations, superior to MSS7 forbiomass monitoring.

ACKNOWLEDGMENTS

I wish to thank Mike Garratt and Lee Mil­ler for suggestions on the manuscript.

This paper repOlts on work supported inpart by National Science Foundation GrantsGB-7824, GB-31862X, GB-31862X2, GB­41233X, and BMS73-02027 A02 to the Grass­land Biome, U.S. International BiologicalProgram, for "Analysis of Structure, Func­tion, and Utilization of Grassland Ecosys­tems." This is a drastically revised version ofa paper presented at the 3rd Annual Remote

Sensing of Earth Resources Conference(1974).

REFERENCES

Colwell, J. E. 1974. Vegetation canopy reflec­tance. Remote Sensing of Environment 3:175-183.

Draper, . R., and H. Smith. 1966. Applied regres­sion analysis. John Wiley and Sons, NewYork, N.Y. 467 p.

Driscoll, R. S., and M. M. Spencer. 1972. Multi­spectral scanner imagery for plant canopycommunity classification. Proc. Eight Int.Symp. on Remote Sensing of Environ. 11:1259-1278.

Miller, L. D., R. L. Pearson, and C. J. Tucker.1976. Design of a mobile field spectrometerlabor:ltory. Photogramm. Eng. Remote Sens­ing 42(4): 569-572.

Richardson, A. J., C. L. Wiegand, H. W. Gausman,J. A. Cuellar, and A. H. Gerbermann. 1975.Plant, soil, and shadow reflectance compo­nents of row crops. Photo. Eng. and RemoteSensing 41(11): 1401-1407.

Rouse, J. W., R. H. Hass, J. A. Schell, D. W. Deer­ing, and J. C. Harlan. 1974. Monitoring thevernal advancement and retrogradation(greenwave effect) of natuml vegetation.Type 111 Final Report, Goddard Space FlightCenter, Greenbelt, Md. 342 p.

Tucker, C. J. 1977. Asymptotic nature of grasscanopy spectral reflectance. Appl. Optics16(5): 1151-1157.

Tucker, C. J., and E. L. Maxwell. 1976. "Sensordesign for monitoring vegetation canopies".Photogramm. Eng. and Remote Sensing41(11): 1399-1410.

Uresk, D. M. 1971. Dynamics of blue gramawithin a shortgrass ecosystem. Ph.D. Diss.Colorado State Univ., Fort Collings. 52 p.

Wiegand, C. L., H. W. Gausman, J. A. Cuellar, A.H. Gerbermann, and A. J. Richardson. 1974.Vegetation density as deduced from ERTS-lMSS response. Third ERTS Symposium,NASA SP-351. Vol. I, Sect. A, U.S. Govern­ment Printing Office, Washington, D.C. 93­116.

Workshop Proceedings AvailableAt no cost, a limited number of copies of the Proceedings for the U. S. Army Engineer

Topographic Laboratories/American Society of Photogrammetry Workshop for Environmen­tal Applications of Multispectral Imagery, 11-13 November 1975, are available on request.Those desiring copies of these Proceedings should write to the Commander and Director,U. S. Army Engineer Topographic Laboratories, ATTN: ETL-LO, Fort Belvoir, VA 22060.