Red and photographic infrared spectral radiances were correlated with soybean total leaf area index, green leaf area index, chlorotic leaf area index, green leaf biomass, chlorotic leaf biomass, and total biomass. The most significant correlations were found to exist between the IR/red radiance ratio data and green leaf area index and/or green leaf biomass (r squared equals 0.85 and 0.86, respectively). These findings demonstrate that remote sensing data can supply information basic to soybean canopy growth, development, and status by nondestructive determination of the green leaf area or green leaf biomass

Fan, C. J.

Holben, B. N.

Tucker, C. J.

English

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https://ntrs.nasa.gov/search.jsp?R=19790020476 2020-03-21T22:27:46+00:00Z
NASA
x	 Technical Memorandum 80312F	 1
7
,i
1
3Assessing Soybean Leaf Area
And Leaf Biomass
By Spectral Measurements
(NASA-TH-80312) ASSESSING SOYBEAN LEAF AREA 	 N79-28647
AND LEAF BIOMASS BY SPECTRAL MEASUREMENTS
(NASA) 22 p HC A02/MF A01 	 CSCL 02C
Uncl as
G3/43 32215	 1B.N. Holben,
C.J. 'fucker,
and C. Fan
TM-80312
ASSESSING SOYBEAN LEAF AREA AND LEAF
+	 BIOMASS BY SPECTRAL MEASUREMENTS*
Brent N. 1lolben
Earth Resources Branch/Code 923
Goddard Space Flight Center, Greenbelt, MD 20771
Compton J. Tucker
Earth Resources Branch/Code 923
Goddard Space FliLghl Center, Greenbelt, Maryland 20771
Cheng Jin__ Fan
Deparlinenl ol`13iology
Morgan Slale Universily
Baltimore, Maryland 21 239
July 1979
s•,
GODDARD SPACE: E; LIGI IT CENTER
Greenbelt, Maryland
*This is a preprint ol'an article submitled to 1 1hotogrammetric Fneincering and Remote Sensing,
C-
I,
ASSESSING SOYBI?AN LEAF ARIA AND LEAF
BIOMASS BY SPI.-TTRAL MEASURI?MENTS
Brent N. I lolben, Compton J. Tucker, and Cheng-jing Fall
ABSTRACT
Red and photographic infrared spectral radiances have been correlated with soybean total Icaf area
index, ; rern Ieaf area index, chlorotic leaf area index, -!cell leaf biomass, chlorotic leaf , bionlass,
and total biomass. The most significant correlations were found to exist between the it/red
radiance ratio data and green leaf area index and/or green Ical , biomass ( 1.2 = 0.85 and 0.80.
respectively). These findings demonstrate that remote sensing claw can supply in ol-Illatioll
hasic to soybeall callopy .growill, developme nt, and status by non-destructive determination of
the green leaf area or green leaf biomass.
iii
CONTENTS
Page
I	 INTRODU(`T1ON AND REVIEW ............................................
	
1
r
	EXPERIMEN'rAL METHODS .................. ............................
	
I
	
RESULTS AND DISCUSSION ...............................................
	 2
CONCL.l1SIONS ............................................
	
,1
	
ACKNOWLEDGMENTS ...................................................
	 4
REFERENCES ......................................... 	 .................	 5
I LLUSTRATIONS
	 -
Figure	 I
I	 The green leaf area index plotted against (A) red radiance, (B)
photographic infrared radiance, (c) it/red radiance ratio, and (D)
the normalized difference. Note the >uperiority of the it/red radiance
	
ratio for predicting green leaf area .......................................
	 9
2	 The it/red radiance ratio plotted against (A) the chlorotic leaf area
	
index, (B) the green leaf area index, and (C) the total leaf area index............
	 13
3	 The relationship between estimated canopy cover and (A) the red
radiance and (B) the green leaf area index. Note how the green leaf area
index continues to increase while estimated canopy cover remains at
I OW. Estimated canopy cover is thus a poor descriptor of plant
	
canopy density for medium to high den: plant canopies ....................
	 16
TABLES
Table	 Page
	Statistical Summary of the Sampled Not Variables .........................	 0
2	 Linear Correlation Coefficients between the lin g Spectral variables
and the Plant Canopy variables. The sample sires are nolcd in
parenthesis.............................................
	 ..........	 7
4;XC D= PACE gt NK NOT' SM-D
ASSESSING SOYBEAN LEAP AREA AND LUAF
BIOMASS BY SPECTRAL MEASUREMUNTS
INTRODUCTION AND REVIEW
Previous research has reported that linear combinations of r •cd and photographic infrared
spectral data were si gnificantly correlated to the green or photosynthetically active portions of
plant canopies Fora variety of cover types (Jordan, 1969, Col weIf et al., 1977; Deering, 1978;
Tucker, 1979). In addition, other workers have applied Laodsat red and near infrared data (MSS
5 and MSS 6 or MSS 7) to a variety of vegetation. analyses (Rouse et al., 1974; Carnegie et al.,
1974; Blair and Baumgardner, 1977; Ashley and Rea. 197.5, Maxwell, 1976; Richardson and
Wiegand, 1977; among others).
Successful application of red and photographic infrared linear combination data to .I
of vegetation situations strongly supports the contention that these data are sensitive to basic
biotic properties of vegetated surfaces, We now report on an experiment: in which red and photo-
graphic infrared spectral data were collected from soybean plots and were correlated with leaf'
area, leaf biomass. and total biomass.
EXPERIMENTAL METIIODS
A soybean field (GI•ycine max (L.) Merr.) on I?linsboro sandy loran soil located oil 	 USDA
Beltsville Agricultural Research Canter, Maryland was selected for study. Sixty circular 0.25 nr'`
plots were used with four plots being sampled her week for fifteen weeks. They soybeans were
planted oil 	 20. 1978, and had a row spacing of 7o cir ► ;rnd 5 em within Ibc row. The crop
emerged in early June and reached IO(Y;" canopy cover in early Augrist. The fits( .freeie orcm
on October 11, 1975.
Each week lour pairs of red (0.63 — 0.69 	 infrared (0.775 - 0.525 lutn)
spectral data were collected for each of the four plots measured with it hand-held radiometer similar
to Pearson et al. (1976). Concurrent agronomicdata pertaining to crop development, estimated
canopy cover, number of leaves, and plant height were recorded before each of the 0.25 111  rrkots
`o.'
we're harvested, All of the above ground vegetation was immediately harvested and the total wet
biomass determined. Subsequently, the total wet biomass was stratified into wet green leas' bioniass,
wet chlorotic leaf biomass, and wet stem biomass. The wet green and wet chlorotic leaf' bionwsses
were immediately run through an automatic leaf area meter to determine the green leaf area and
chlorotic leaf area. The wet biomass fractions were then force air dried at 60°C for 48 hours
before the dry green leaf biomass, dry chlorotic feat' biomass, and dry stem biomass determi-
nations were made, The stem biomass determinations included reproductive organs.
The spectral radiance data were collected at approximately one week intervals and were
collected in direct sunlight under cloudless or partly cloudy skies between the hours of 1030-143,0
EDT!. Atmospheric conditions varied from very clear with low humidity, to hazy with high humid-
ity. A solar irradiance reading was taken from a BaSO4 panel prior to measuring each plot with
tlie hand-lield radimeter. Radiance data were used in the analysis because the two bands in ques-
tion (0.63 — 0.69 and 0.775 — 0.825 µm) are close together in a spectral sense and atmospheric
transmission characteristics are similar for both bandwidths.
The radiance data were used to form the it/red radiance ratio and the normalized difference
of (ir -re(l)/(ir+rcd) after Rouse et al. (1974). All radiance data were averaged for each plot (i.e.,
the mean of the four observations) and the averaged values used thereafter in the statistical analy-
sis. The data analysis correlated and regressed the red radiance, the photographic it radiance,
it/red radiance ratio, and the normalized difference (ND) with respect to the various plant canopy
variables measured (Table 1).
RESULTS AND DISCUSSION
The data analysis showed conclusively that red and photographic infrared spectral data were
highly related to the green LAI and the green Ica!' wet and dry hiomass (Table ?; Figure 1). The
(1'? i ,-xl radiance ratio was the most highly correlated spectral variable with the green LAI and the
green leaf biomass anti had a linear trend with respect to these canopy variables. 'fhe ND, by
c ontiparison, was decidely nonlinear with respect to the same canopy variables (Figure 1. ). This
1
2
dit'ference in nonlinc:arity for these rest and photographic infrared variables has previously been
reported for moderate to high green leaf biomass situations (Colwell et al., 1977 and Tricker et. al.,
1979a). The nonlincarity of (fie ND restricts its usefulness 1'01- high Icaf biomass situation,.
r, The. discussion of our results will henceforth focus principally upon the it/red r•adi;nrce ratio
because it was found to be the most useful spectral variable in this study (Table ?; Figure 1).
i
The red and photographic infrared radiances, while useful in sonic regards, did not compensate for
dirferences in the spectral irradiance at the tin g es or data collection (i.e., they are proportional
to the instantaneous incident spectral irradiance). We, therefore, will deal with the ratioed data
which minimizes this variation in the spectral data.
The it/red radiance ratio was more highly correlated with the green LAI than were the total
I.Al or the chforolic• [Al (Figure ,T-1111e '). 7'I1c s:.mc relaliunN101 ►(s) caisled helwcen it it/red
radiance ratio and the green leaf' biomass, chlorotic leaf biomass, and total leaf hionlass (wet and
dry, for all three cases), respectively. The it/red radiance ratio was not higlily correlated with any
ci'f the "chlorotic" variables, the total wet biomass or total dry biomass, or the wet stem hiomass.
This lurlhcr establishes the apparent fact that the it/red radiance ratio can he used for accurate
rlumlCS1111 live csliuratioIN 01' I ► roiceled iii Situ soyhcarl 91CV11 Ical ;u'ea ur hiunrazS.
Green or photosynthetically active LAI data is one of the hasic state or system level variables
s•'
for primary production modeling. This dynamic hiotic entity, which responds. rapidly to abiotic
:did/or hiotic iniluenccs, in effect integrates the various conditions affecting plant growth and
development. The green LAI also rel ► resents the 1u4e11ia1 fur I ► rnn,ny 1 ► rodrlctivily al a given
point of time. If follows that file green LAI is where I ►hotusyntln•sis orcnrs and is highly regaled
)
to the amount of primary productivity which results as are several other abiotic and hiotic vari-
ables. Remote sensing call 	 supply information which is basic: to plant growth and develop-
i.	 nlenl. 'These data, nrunilored Ihroutth little and c'on ► hined with ► Tiler Iyi ►esof da p (soil water,
climate, etc.), offer the potential to nondestructively m mifor vegetation in situ.
The commonly recorded agronomic variable "estimated crap cover" has been considered
fundamental to crop canopy condition. We also evaluated the relationship of the spectral data to
is
estimated crop cover in a high leaf' density situation. The red radiance was found to be highly
correlated to estimated crop cover. Both of these variables were asymptotic and when 100 o canopy
cover was reached the red radiance also ceased to change (Figure 3a). We observed a poor relation-
ship between estimated crop cover and the greenLA1 (Figure 3b). It is thus apparent that esti-
mated crop cover is a poor choice for quantifying in situ canopy condition for a soybean crop.
CONCLUSIONS
1. The it/red radiance ratio was found to be linearly and highly correlated with soybean
green leaf area index, green wet leaf biomass, and green dry leaf biomass.
2. The normalized difference was exponetially related to the green leaf area index, green wet
leaf` biomass, and green dry leaf biomass. The asymptotic nature of this relationship prohibits the
use of the normalized difference for estimating these plant canopy variables in high leaf density
vegetation canopies.
3. Estimated crop cover and the red radiance individually were poor choices to evaluate crop
canopy condition for high green leaf biomass situations.
4. Spectral estimation of the projected green or photosynthetically active Leaf area ol' plant
canopies by use of red and photographic infrared spectral data can provide a crucial input to
primary productivity models.
We (hank the following people for hell) in Colleclion of the field data:
Jim Elgin, Jim McMurtrey, John _Schutt, Chris Justice, Maryann Karinch, and Carlos Uscudero.
r i
RE?FERFNCES
Ashley, M. D. and J. Rea. 1975. Seasonal vegetation differences from FYTS imagery. 1'hotogram
Engr. and Remote Sensing 41:7 13-7 11).
Blair, B. O. and M. F. Baumgardner, 1977. Detection of the green and brown wave in hardwood
canopy rovers using multidate multispectral data From Landsat-1 . Agron. J. 69:808-811.
C'arneggic,D. M., S. D. de Gloria, and R. N. Colwell, 1 9'74. Usefulness of EWI'S - i allot su11)ortillg
aircraft data for monitoring plant development and range conditions in California's annual
grasslands. Burcatl of Land Management Final Report 535011-C'1'3-266 (N).
Colwell, J. E., D. P. Rice, and R. F. Nalepka, 1977. Wheat yield forecasts using Landsat data.
Ill Proc. of the i 1 th International Symposium on Remote Sensing of Enviromucnt, Uni-
versity of Michigan, Ann Arbor, p. 1245-11-54.
teen,-, D. W., 1978. Rangeland reflectance characteristics measured by aircraft and spacecraft 	 ji
sensors. Ph.D. dissertation. Texas A & M University, College Station, 338 pp.
Jord an, C. F., 1976. Derivation of leaf area index from quality of light on the forest floor.
Bcology 50(4):663-1166.
Maxwell, I;. L., 1970.  Multivariate system analysis of lilt) Itispeclral imagery. I'holograril. i llgr.
and Remote Sensin g 42(9): 1 173-1 186.
Pearson, R. L., L. 1). Miller, and C. J. Tucker, 1976.  hand-held radiometer to measure gramincous
biomass. Appl. Optics 16(3):416-418.
Richardson, A. J., and C. L. Wiegand, 1977. Distinguishing vegetation from soil background infor-.
oration. Photogram. Engr. and Remote Sensing 43(12):1541-1552.
Rouse, J. W., R. It. Haas, J. A. Schell, D. W. Deering, and J. C. Harlan, 1974. Monitoring the vernal
advancement and retrogradiation (greenwave'effvcl) of natural vekrfafion. NASA/(;Sf,('
Type Ill final report, Greenbelt, MD f1..371.
Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vct;e
IP
i	 tation. Remote Sensing of Environment 8(2):127-150.
Tucker, CJ.. J. H. E:Igin, and J. E. McMurtrey, 1979. Relationship of red and IAtoto g raphic infra
refit spectral Bala to all'alla biomass, canopy rover, and dlot ►i;hi stress. Submitted to Rclllok'
Sensing of tnvironment.
I^
F,
I's
Table 1. Statistical Summary of the Sampled Plot Variables
^^_------
Variable
_^
Sample Size Range
0 -7.61
Mean
!3.52
Standard
Deviation
1.95Green LAI 52
Green It/et Leaf
Biomass (g/m 2 ) 60 0.0-1262.4 494.77 331.70
Green Dry Leaf
Biomass (g/m 2 ) 60 0.0-292.8 121.92 81.29 
Chlorotic T_,A1 40; 0- 1.22 0.26 0.29
Chlorot.ic Wet Leaf
Biomass (g/m 2 ) 42 0.0-182.4 37.07 42.82
Chlorotic Dry Leaf j
Biomass Win') 40!,
i
0.0-47.0 9.0 11.45
Total 'LAI 40'; 0.0-7.95 3.98 2.01
Total Wei Leaf'
Biomass (g/m 2 ) 42 0.0-1310.4 631.71 _ 3 27.3 2
+ Total Dry Leaf'
I Biomass (g/1,1 2 ) 40 0.0-301.0 150.04 77.26
Wet Stem Biomass
42	 i 76.80-4749.6 2379.26 938.61
Dr4 Stem Biomass
(g%m-) 40 !	 110.4-1070.4 566.69 287.24
Total Wet Biomass
^(g/In 2 ) 60 48.0-5121.6 2337.96 1466.25
otal Dry Biomass
(g/m2 ) 60 4.8-1209.6 553.66 360.16
Prop Cover (%) 60 5-100 78.3 32.95
Canopy Variable Red IR IR/Red ND
Green LAI (52) --0.75 0.75 0.92 0.81
Green Wet Leaf Biomass,
(60) -0.67 0.79 0.93 0.82
Green Dry Leaf Biomass
I
(00) -0.68	 f 0.75 0.92
Chlorotic LAI (40) 0.02 -0.14	 j -0.3:0	 ^ 0107
Chlorotic Wet Leaf
Biomass (42) 0.01 -0.12	 ! -0.29 0.07
Chlorotic Dry Leaf
Biomass (40) 0.03 -0.20	 r -0.34 0.05
Total LAI (40) --0.84 0.85 0.89 0.84
Total Wet Leaf
Biomass (42) -0.82 0.85 0.90- 0.82
Total Dry Leaf
Biomass (40) -0.84 0.82 0.88 0.83
Wet Stem Biomass
(60) -0.32 0.00 O.OS _ 0.35
Dry Stem Biomass
(40) 0.55 -0.74 -0.74 -0.54
Total Wet Biomass
(60) -0.73 0.31 0.65 0.71
Total Dry Biomass
(60) -0.58 -0.14 0.26 0.36
Estimated Crop Cover
(60) -0.93 0.48 0.79 0.IN 7
A
s a
Table 2. Linear Correlation Coefficients between the four Spectral variables and the Plant
Canopy variables. The sample sizes are noted in parenthesis.
i
Figure Captions
Filmre 1. The green leaf area index plotted against (A) red radiance, (B) photographic infrared
radiance, (C) it/red radiance ratio, and (D) the normalized difference. Note the superiority
of the it/red radiance ratio for predicting green leaf area.
Figure 2. The it/red radiance ratio plotted against (A) the chloro(ic leaf area index, (B) the green
leaf area index, and (C) the Mal Icaf area index.
Figure 3. The relationship between estimated canopy cover and (A) the red radiance and (B) tile-
green leaf area index. Note how the green leaf area index continues to increase while esti-
mated canopy cover remains at I Orlo. Estimated canopy cover is thus a poor descriptor of
plant canopy density for medium to high density plant canopies.
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17


Assessing soybean leaf area and leaf biomass by spectral measurements

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