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NI\SI\ Technical Memorandum )o,,~ 7

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The Relationship of Red and Photographic I nfrared Spectral Data to Grain Yield Variation Within a Winter Wheat Field

Compton J. Tucker, Brent N. Holben, James H. Elgin, Jr., and James E. McMurtrey III i{1

(NASA-TM-80328) THE RELATIONSHIP OF RED AND NBO-12532 ~ PHOTOGRAPHIC INPRARED SPECTRAL DATA TO GRAIN YIELD VARIATION WITHXN A WINTER WHEAT FIELD (NASA) 26 P He A03/MF A01 CSCL 02e Onclas

JULY 1979

National Aeronautics and Space Administration

Goddard Space Flight Center Greenbelt, Maryland 20771

G3/43 41471

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THE RELATIONSHIP OF RED AND PHOTOGRAPIUC

INFRARED SPECTRAL DATA TO GRAIN YIELD

VARIATION WITHIN A WINTER WHEAT FIELD*

Compton J. Tucker Brent N. Holben

Earth Resources Branch NASA Goddard Space Plight Center Greenbelt, Maryland 20771 USA

James H. Elgin, Jr. James E. McMurtrey III

Beltsville Agricultural Research Center Pield Crops Laboratory

Plant Genetics and Germplasm Institute USDA/SEA/ AR

Beltsville, Maryland 20705 USA

July 1979

*TIlis is a prcprint of a manuscript submitted to Photogrammetric Engineering and Re,mote Sensing

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GODDARD SPACE FLIGHT CENTER Greenbelt, Maryland 20771

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THE RELATIONSHIP OF RED AND PHOTOGRAPHIC

INFRARED SPECTRAL DATA TO GRAIN YIELD

VARIATION WITHIN A WINTER WHEAT FIELD

ABSTRACT

Two-band hand-held radiometer dat!l from a winter wheat field, collected 011 21 dates during

the spring growing SCaSOl!, were correlated with within field final grain yield. Significntlt lincal' reI a-

tiol1sl1ip8 were found between various combinations of the red and photographic infrared radiance

data collected and the grain yield. The spectral data cxplained ~64% of the within field grain yield

variation. This variation ill grain yield could not be explained lIsing meteorological data as these were

similar for all arcas of the wheat field. Most importantly, datu collected carly in the spring were

highly correlated with grain yield: a fivc-week time window existed frol11 stem elongation through

an theses in which the spectral data were most highly correlatcd with grain yield; and manifestations

of wheat canopy water stress Wt.:]'\;! rcndily appurcl1t in the spectral datn.

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CONTENTS

Page

BACKGROUND .. • • I 1> , , ~ • • • • f t t • • f , • • • • • • • t • ~ I • • , • t , • ... • • J J • • • • .II • • , • t • • • ( , ~

DESCRIPTION OF RESEARCH UNDERTAKEN 2

EXPERIMENTAL PROCEDURE • II • • • • , t • , t I , .. • • • , , • • t ~ .. t , • • * • t • t t • • ,~ • • .f , • t ... , .. 3

6 RESULTS AND DlSCUSSION t "I; • • • I , • , t • • • • • It' , • • f • , • ... ~ , • , • ) , • , t ~ • .. I I • 't - • • • •

19 CONCLUSIONS , ..... , • • , • • • 0; • • t , I • I • • Ii • • • I • • I ~ _ • t • t I ~ • • t , , I I • ,. • • • • • • , .~ • • •

19 ACKNOWLEDGMENTS

20 REFERENCES ...... . • , , • • _. • • • • t , , • • • • .. II , , • .. • • • , • • , t • I • • • , , ! • • • t • , , • ~ • • ; • •

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DACKGROUND

THE RELATIONSHIP or RED AN]) PHOTOGRAPHIC

INFRARED SPECTRAL DATA TO GRAIN YIELD

VARIATION WITHIN A WINTER WI-IEAT FIELD

A W

H.I~PRODUCIBILrI'Y OF THE ORIGINAL PAGg IS POOR

S(Jvetul "pproa~hes fOl' remote sensing of winter whl!ut yield have been proposed. Tlw Large

AI'l)U Crop Inventory Experiment (LAClE) involving NASA, NOAA, and the USDA, was perhaps

till,! most visible Hnd the best known of' variolls winter wheat yield prediction approaches. Simula·

HOll or regression moth!ls wel'c used to pl'edkt wheat yield bused upon climatic conditions (MacDonald

and I-lull, 1977).

The Eal'thSat Corp. (J 976) Ilus proposed u spring wheut yield prediction method. This approach

differs from the LACIE approach in that NOAA meteorological satellite datn were 1.Ised to estimute

grid cell pl'ccipitution which in tul'll drove the yie1d determination. Critical variables for this approach

were the calculation of soil moisture and grid cell weather from the meteorological satellite data and

the detcl'llliMtion of crop phenology (LAClE, 1978).

Idso ot 01. (l977a,b) have proposed a nwthod of winh))" whea" yield prediction lIsing the stress

degree day concept. That is, the final yield of a crop was hypothesized to be linearly related to the

accumuln ted stress degree days over some critical period. This technique was developed in Phoenix,

Arizona under irrigated conditions and is currently being evaluated in dryland wintel' wheat growing

areas.

Hammond (1975) and Morain and Williams (1975) have discllssed. monitoring wheat production

with satellite remotely sensed data. Harlan Hnd Lill (l97 5) and COlwell ot al. (1977) have evalua ted

the lise of Landsat data for inferring direct winter wheat grAin yield predictions. Spectral data were

used to estimate yield related variables such as stand density or leaf area index. Because grain yield

is lIsually highly correlated to stand density or leaf area index, a direct estimate of final grain yield

was usually possible. Colwell et al. (1977) concluded that Landsat (I.e., spectral) data explained a

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uonsidcrabll.! amollnt ofyicld variation which wus not explained by meteorological data. In tluditlon,

Landsat "h.'riwd yield predictions were us highly correlated with individual field yields as were csti-

mates llhlde using tnlditiol1nl saml)ling techniques, even, in SOllle cases, if the LUl1dsat data were

ilmllcctcd sl.'yeral weeks berol'c the field samples,

Heilnull1 et tiL (J 977) I'l.!pol'teti a teehniqw.'! 1'01' estimating winter whetlt grain yields lIsing LntHi-

sat deriwd estimates of lear area .indl.'x ~otlplcd with nn evapotranspiration model. More recently,

Wiegnnd et nl. (1979) discussed leaf arcu index estimates for winter wheat matie from Landsat datu

tlnd the implications for eWlpotnmspimtion and crop simulntion modeling lIsing these oatn,

Accurate inputs for crop canopy leaf ureH index arc a necesSIty for successful evapotranspiration

and crop simulation modeling, To achieve nCCUl'f\te leaf area index estimates from Landsat data,

"ground-truth" sampling must OCCllr which adequately samples the variability of the actual field leaf

area index for enough Landsat pixels ( ....... 0.45 ha) to satisfy basic sampling theory requiremt nts. This

has often proven difficult to achieve. Heilman et al. (1977), Kanemasu et al. (1977)) and Wiegand

ot 01. (1979) all used three grolllld samples ot' 9] em by 2 row widths ("""40 cm) to establish each field's

(>40 ha) lIMf area index. TIllls ...... l m2 was used to determine the leaf area index of a large field. This

could be a significant sOllrce of error for the evapotranspiration and crop yield modeling approaches

(Wiegand et aL, J 979). "Ground-truth" sampling must adequately account for thein situ population

variabiUty by inclusi'On of enOllgh samples to allow t'or a reasonable estimate of the actual field leaf

area index.

DESCRIPTION OF RESEARCH UNDERTAKEN

As pmt of an 'Ongoing research pr'Ogram into future satellite sensor development including selec-

tlon and evaluation of spectral bands, radiometric resolution, f1'0qllcncy of coverage, orbit selection,

ancl other considemtiol1s for vegcta tiollal applications, we have been collecting hand-held l'u(Hometcr

data from a variety of agricultmal crops in a NASA/GSFC-USDA/BARC cooperative research program.

Previous research 011 alfalfa, corn, and soybeans had demonstrated that red ,mel photographic infrared

two~band radiometer data were highly correlatGd with various l,roperties of plant canopies (Tucker

et aI., I 979a,b,c). Therefore, we decided to evaluate the applicability of these data for yield predictions

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on wheat. A companion paper Nports on the use of these smnc data for monitoring totul lilY mut·

ter accumulation (1UCkCI' et al., 1979d).

Previous work with the green leaf OJ' photosynthetically active biomass has sliggested thal this

physiologicul entity integrated the vurious biotic and abiotic effects present in the plant (!anopics

(Tuckel' ct aI., 1973; Colwell et ,,\., J 977; TlIcker et aL, 1 979n,b). Conditions whil~h adversely al'..

feeted plunt growth and development rcsul.t.ed 111 11 reduction in the photosyntheticully active biomass.

Becallse the photosynthetically active biollH1SS or green leaf area is one of the basic system variables

in primnryproductioll, monitol'illg this system vnriablc throughout the growing season should enable

inferences to be niade regarding total dry matter acculllulation and grain yield. This has been pro-

posed a.~ the Lenf Area DUration (LAD) concept (Colwell ot aI., 1977: Richardson ot al., 1979).

However, the LAD concept needs to be modified in that the photosynthetically active biomass

01' leaf area is actually the interaction between the green LAI and the chlorophyll concentration.

Expressed in other words, the photosynthetically active biomass can be defined as the interaction

between inter- and intra-leaf scattering and chlorophyll absorption which oCcurs predominately in the

greenleavcs of the plant canopy in question.

Red and photographic infrared spectral data have been demonstrated by many workers to be

highly correlated with the photosyntheticaIly active biomass of several cover types (reviewed ill

Tucker, 1979), The red spectral data are highly correlated with the in vivo chlorophyll concentra-

tion, whereas the photographic infrared data are highly correlated with LAL Thus, vnriOtls linear

~ombinations of these two adjacent spectral regions are highly related to the photosynthetically active

biomaSs (Tucker, 1979).

EXPERIMENTAL PROCEDURE

Our experiment was conducted .in a 1.2-ha soft red winter wheat (Triticum aestivum L.) field

at the Beltsville Agl'icultural Research Center, Beltsville, Maryland. The field was plowed, disked,

and planted with the cultivar 'Arthur' 0.11 October 6, 1977 at a rate of 107.6 kg/ha. A conventional

grain drill with 17.S-em row spacing was used for seeding, Before seeding the field was limed on the

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basis orson test recommendations und f\~rtilizcl' was npJ).til~d at a nitc of 33.3 kg N. 53.8 kg P, tlllU

53,8 kg K/1Ja. The following spring (carly March 1978) the crop wus topd1'l.'!ss~d with 20.4 kg N/ha.

Twenty 2- x 3-m plots were selected during the winter dormant period in the wheat field. Suh­

seq lien t1y, for cnch plot. fmH' pairs of red (o.G~i·O. 70 pm) nnd j)hotogmphic infrared <0.775-0.825 /llll)

spcctrui1'lldiancc measurements wore obtained lIsing n lHlI1d·hcld cligitnlmdiolll0ter (Pearson ct uL.

1976) 011 21 dates between March 21 j 1978 (Julian dntc 80) and Junc 23.1978 (Julian datI.! 174).

The intervals between d~ltcs I'Illlgcd from I to 9 dnys; howewr, the .wet'age intcrvul was 4.7 dnys and

the mcditui in tervnl wns n tic between fOllr Hnd five day.s (Table I).

The red and photoaraphic infl'al'ed spectral /'(\(\iullca data were lIsed to 1'01'111 the ii/red rntio and

the normuliz\~d difference (NO) of ROllse ot al. (1973) and D\!orinr ot al. (1975) where:

ND = (lR - RED)/OR + RED) (I)

The four pail'S of the spectral metlStirelllents per plot were averaged to nCCollllt 1'01' the spatial

variubility present in ctlCh plot. All spectral dnta were collected plus or minus 90 minutes Qt'local

solar noon. measured 110rmal to the ground surface at a height of'" 1 111 above the plant callopy under

sunny skies (Table J).

Thl'Ollgholit the growing season average plant height, percentage cover estinHlt~ls. and phenolog:-

icul devclopmcn t notes were recorded for the field area (Table 2), The Cj'op reached harvest maturity

in late June 1978. On June 28 j 1978 (Julian date 179) a 0.9- x 3,0-111 swath was cut with a small

sickle bar mower f.rom the ccnter of cach plot and the grain thrashed with a sma1J plot grain tlu'asher.

The grain yields were oven dried at 60°C for 72 hours and were subsequently ndjusted to 14% mois-

ture and expressed in g/m 2•

A regression approach was taken in which the avemge red mdiance, il' radiances, ir/red ratio,

nne! ND were correlated with the grain yield for each of the 21 data collection dates. In addition,

the il'/rcd ratio and the NO as a function of Julian datc and growing degree days were integrated for

four different time intervals and c01'l'eiated with grain yield,

4

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Table I Tabultlr Listing of the Days when Halld~H(lid Radiometer Data Were Collected

from the 20 2" 3 In Winter Wheat Plots In 1978

Sampling Julian Time Sequence Date (EST)

1 80 Il30-1215

2 89 1215-1300

3 , ,.,. ... ..,,; 1222·1310

4 95 1220·1245

5 97 1225·1247

6 102 1110-1135

7 104 1210-1230

8 112 1338·1415

9 118 1230-1310

to 121 1200·1230

11 123 1215·1250

12 131 1145-1210

13 139 1145-1230

14 146 1125-1205

15 152 1130-1200

16 157 1050-1120

17 161 1230"1300

18 165 1030-1125

19 ]66 1100"1200

20 170 1030"1100

21 174 1100-1130 .... "-- . Mean time between sampling dates'"' 4.7 clays Runge between sampling dlltes "" 1-9 days

....... Condi ti oils/Com men ts

(Tl!mperatllre, Sky, Wind, Etc.)

12°C, clear with no clouds, wind::: 16 kmh

Sac, clea.' with no clouds, calm, soil damp

(5°C, clear with no clouds, cnhn

17°C, a few scattered clouds, wind;;: .... 5·10 kmh

21°C, a few scattered clouds, calm

14°C, clear wi.th no clQuds~ wind::: -S kmh

20°C, clear with no clouds, wind::: 30-45 kmh

ISoC, scattered clouds, gllsty wind::: 5-20 kmh

22°C, dear with no clouds, gusty wind::: 5-30 kmh

16°e, clear with no clouds, wind::: 5-10 klllh

18°C, clear with no clouds, calm

24°C, a few scattered clouds, wind::: '" 1 0 kmh

'lODe, a few scattered clouds, wind::: < 15 kmh

19°C, a few scattered clouds, calm

22°C, clear with 110 clouds, wind:::; Sol 0 kmh, plots 33-35 cr.'.lshed by animals (decr?)

22°C, a few scattered clouds, wind = -10 kmh

26°C, clear with no clouds, calm, plots 23"35 lodged

17°C, a few scattered clouds, wind =- 25-40 kl11h

201l C, high faint cirrus, calm, bird damage to plots 21-40

20°C, a few scattered clouds, wind::: < 10 kmh

I 28°C, clear with no clouds, calm ---

Ml:dium lime between sampling dates == 4,5 clays (tic)

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Tnblc 2 Agronomic Onto Pertaining to Average Plunt Heights. Estimated Percentage

CnlloPY COWl', :lIld (.'I'Op (il'owth Stages at 11 Scl~ctcd Diltes for the :10 Wintcl' Wheat Plots from 1978

""~~.oI/-J""';!«.'"

Calendar D~l tl!

,-,~,,,-Ir.~ .. ~~.,,,, .... ~_.

04/24/7g

05/01/78

05/11/78

05/19/78

OS/25/78

06/01/78

06/06/78

06/14/78

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Julinll J)~ltc

114

121

131

139

145

152

157

165

Phlll t Height (em)

35.0

45.2

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90.8 I

112.0

112.5

114.8

115.5

06/20/78 I 171 1]1.8 . I . ,

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54

66

64

68

61

63

64

51

51

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Numerical (aftel' Zadoks. et n1.. 1(74)

34

35

44

58

73

85

85

87

89

D~sf,,'riplive

stem dongaticm. 4 til nodc ~h:tcl.·tilhl\!

stem elongation. 5th node d~tcl!tnhlc

booting, boots just visiblel

inflorescence emerges

curly milk

soft dough

soft dough

hunt dough

hard dOlf;,;h , 06/23/78 J 174 1 108.5 ;

l_~~_~:?~~~. _ ~._~~~" .. " L .. :?4.:~ . .i.. .... 51 I 92 j l'cndy for harvest ~ ' ..... " .. ..l . _ ... _ ..•.... _.~ . ., ~ ., .... 1..... .. .. ~* ~ .. ., .. _ ............... _

RESULTS AND DISCUSSION

Exumples of the radiance dnta were plotted agaillst Julian datc (Flgmc 1). The rcd radianc!.!

vs. Julian date curve showed the influence of incrensing chloJ'Ophyll absorption to -.Julian date

139 followed by a gradual decrease in chlol'ophyll absorption as a result or the onset of sellcscencc

(Figure I a). The photographic infrared radiance vs. Julian date clirve showed a grndllul increase

with time to a maximum at ""Julia)) date 139 followed by a decrease catlsed by senescencc. This

resulted from tile direct l'elationship of thc pilotogrnphic infrared radiallc0 to the foliul' density of

the plant canopy (Figure I b). The ir/l'cd radiunc(:; rutio und the normalized difference both exhibited

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160 1~

JULIAN DATE

REi'llOnUCIBU.TTY 01" 'l'Ul!.) (O) ORl<H!\Al) PAHN IS POOl~

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Figure 1. Red radiance, photographic infrnrcd radiance; ito/red radiunce ratio, und the l10rmalized difference plotted against Julian du tc for one of 20 wheat plots sampte(1. (A) Red rudiance, (B) Photographic infrared i'alliance, (e) [r/red radiance ratio. und (0) Normali7.ed diffcl'l.!/lcC (NO). Note how the ir/red radiance ratio and the normal­ized difference effcctiwly compensate for the variability in the radiance data.

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0,6 •

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60. 00. 100. 120. 140. 100. 180. 200.

JUUAN DATE

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FigUl\'2. The ir/fI..'ll radiatl\:\,' ratio (A) and th~ norU1uIiI~d dini.'I\'nc~ (il) from thn.'!..' 2- x 3-111 pInts plnttl'd uguill~t JUlian data. Tlw vI.'ftkal arrow!'. r~'prl:sl'nt l'pis()~h'!i or rainf1IlL Not\,' till' n"ipOllSt: Ill' tltl: two Slw\.'tral variuhk')o, to tIll: ot:I.'urn,'lll.'!.' or pl'I.'dpi­tution whkh ~'l1lh .. d pl'riods or wute]' strl'SS.

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to the episodes of wutcr strcss (FJgure I b). This implied that the LAll'cmnined fairly constunt while

the leaf chlorophyll density was telllporarily reduced. either through photooxidntioll, eJ17.yma tic, or

some othel' reducing mechanism (Tu';ker et nl., 1973: Tucker et al., 1979c).

The Ilex t phase of the HIl(liysis regressed each of the 4 spectral vnriablcs against thc flnal grain

yidd for cHch of the 21 data collection dntcs. OUr principal attun tion was focussed all the ir/red

ratio and the normalized tli ffcrcncc as these linear com billa tiOI1S adjust for irradiational vUl'iabili ty

(FigUl'C 3), We observed lower correlations for the first fOllr dates, higher corl'elations for dates 5

and 6, Hnt! a marked decrease in correlation with grain yield for date 7 which was attributed to very

windy conditions (Tables 1 and 3). Sampling dates 8, 9, and to were all highly correlated to grain

yield. There wus a slight decrease for dato 11, followed by high correlations for sampling date J 3

after which the correlations decreased as senescence progressed (FigUl'e 4).

We observed a 40-day period (from Julian date 112-152) dUling which otlr spectral dat1 \VOre

Illore significantly correlated to the tinal grain yield than earlier or later (Figure 4: Table 3), How-

ever, the regression relationships were not constant dUring this period, suggesting that a regional

application of these relationships was doubtful (Table 4). An alternative to this was attempted in

the form of integrating tile spectral data in tel'ms of Julian date or accumulated temperature units

(growing dC'gl'ee days or equivalent). This was a remote sensing application of the leaf area duration

concept.

The il'/rcc\ mdiancc ratio and the normalized differel1ce were integrated for folir periods during

the growing season: Julian dates 80-112; 112-152;152-174; and 80-174. We observed that the plateau

portion (Julian dates 112-152) of the growth curves, corresponding to the maximum green leaf bio-

mass present, was the most highly correlated with final grain yield (Figure 5). The normalized differ-

ence spectral data from this period explained 66% of the within field variability in final grain yield while

the normalized difference spectral data from the entire growing season (i.e., Julian dates 80-174) ex-

plained 64%ofthe same grain yield Variability (Figures 5 & 6). Spectral data alone thus explained ap-

proximately two-thirds of the variability in withirl Held grain yield. This could not be explained by mete-

orologicalmociels as the micro-climatic effects were largely identical for Ollr 1.2-l1a field. The earlier

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GRAIl YI£lIi I"~l (C)

(D)

Figure 3. The ir/red radiance ratio and l10nnalized difference plotted against grain yield for tWD sampling tl::tes.

' .... 4~;;~J.:, 1i,lf'~;' ;~> . .l: ~ .. • :,~ ~;"('...t:~ ~ *,,~ .. :,., ':$lv

'7

/ ..

JIll

• ., • •

,1/1 -

-l

1 ,

I

t i ~ ~. t'

I , ~ • [ ~

r

I r !

b l ~( I

r , f·

~ ~ i,

--~~~~------~·----__ ' ___ ~." ___ S_IF_."._~~~ .

Table 3 Correlation Coefficients for the Foll/' Rudiance Varillbles and Finnl Grain Yield

1'01' Each of the 21 Dnys where Spectral Dutn were Collected 1'1'0111 :!O 2 x 3 Plots in 1978. Refer ulso to Figures 3 and 4.

.... " .,... ~.....,.--. __ ......... _ . .,_1_. ___ ' ____ ~ __ rl! _ .. - ~

N01·,:;;:~-l Snmpling Juliull Red Ir/Red Ir S!JlIUCllCC Date Rndiul1ce RlIdial1cc Rndiancc mfferollcc

Ratio ... _ .. - ... __ t._",_-,,",_ ---,.._ ... ,,...:;.--- --- _.-._-, .... ~-~ .. -',.~---80 0.14 0.51 * 0.27 0.'27

., 89 -0.33 0.48* 0.41 0.43

3 92 ,..0.22 0.42 0.31 0.30

4 95 -0.30 0.43 0.40 0.42

5 97 .,.0.71 ** 0.78** 0.62** 0.75**

6 102 -0.71 ** 0.80 111 * 0.61'** 0.75**

7 104 -0.51 * 0.59** C..55* 0.57**

8 112 -0.75** 0.74** 0.69** 0.78**

9 118 ",0.82** 0.66** 0.73** 0.82**

10 121 -0.82** 0.54* 0.76** 0.82**

11 123 -0.78** 0.64** 0.68** 0.79**

12 13 J -0.76** 0.78** 0.75** 0.78**

13 139 -0.78** (li83"'* 0.83** 0.81 "'* 14 146 -0.73** 0.76** 0.77** 0.76**

15 152 -0.78** 0.69** 0.78** 0.78**

16 157 -0.73** 0.61** 0.71 ** 0.73**

17 161 -0.55** 0.68** 0.66** 0.66**

18 165 -0.51 * 0.49* 0.56** 0.55**

19 166 -0.57** 0.43 0.61** 0.58**

20 170 0.19 0.51 * 0.32 0.30

21 174 0.46* -·0.14 -0.48* -0.42 -----

*Indicatcs significance at the 0.05 level of probability **Indicatcs significance at the 0.01 level of probability

12

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~ 1

1

~.

, 1

i;

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l ,

j f !

~ r· I r ! ~ . f ~ t • ~ , t

r

I , r l r i

- U.l .,.~

1.1>

~ 0.9

2 0.8 0

~ 0.7

j 0.6 a:

~ 0.5 Q u. 0 0,4 ... 2 w 0.3 (j

ff w 0.2

8 0.1

0.0 80 11. 128 140 152 114

JULIAN DATE

(A)

1.0

~ 0.9

Z 0 .•

~ ! ~ a:

~ Q &I. 0 ... Z w (j

e w 0.2 0 (,) 0.1

JULIAN DATE

(B}

Figure 4. Coefficients of determination resulting from regrcssing the (A) ir/red l'1Idiance ratio or CB) normalized difference against grain yield for each of the 21 data coIIection dates. The 1'2 values presen ted in Figure 3 are plotted against julian date along with the respective 1'2 values foJ' the other 19 dates. Note how the normalized difference was more highly correlated with final grain yicld than was the ir/red radiance ratio earlier in the growing season.

13

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I III

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~

15.00 r

i

11.251 !

I 7.50 f~

I !

3.75 j...

i

COfm~j[Nrs (IF IlEfWMINO\TION FOil IIAllvtnr", OnIIlNW~IOItT

W $~UenD INrEOnnlON p~nIOPS

0.00 t ... , ... 1 ..... L I I 120. 60. 80, 100.

,~. d1

L 140.

JULIAN DATE

(A)

1.0 r--;;~[F;'I~'ENrs OF il£T(~MINATION FOR IIARVESTEO GRAIN "tln~l

VI ' 0.8 i.. SElECTED INTEORATIONPtRIOOS

z w 0.6 ~

~ c o

~ ~ a: o z

J .. L ..... 120. 140.

JULIAN DATE

(8)

!

~I --r 'r' • 40.., co

! \.l~ 1 I 1 1

160. 180.

I ..

160.

200.

1 I

j 200.

Figure 5. The r2 vnlul!s resulting from the (A) ir/red radiance ratio "0 Julian date ,lilt! (8) normnliz\!d difference _. Julian dat\" integrations for fOlll' differ­ent time periods: .Il1litln dates 80-112: 112-152: 152-174; and 80-.174. (Re­fer <Ilso to Figure 6).

15

9 .ii1~ F ., . .:.4 i

I 1

I . i I l r , I I !

r

--.~.~""'---..... ~. I ! ,..--~-.--..

IWGMTID ""*' • 1111· liD", .. ~ •. IU.II·I •• 1( • ,1_""

i • • • I • .. •

I • •

:mJ

•

• :. 22lI 240 • 211 ., 32D 340 360 3IJ 400

CRAIfiI YIELD I_""Z)

(A)

lNTEGRATED FROM 3121 - 6/23178

UJ 55.0 ;, -14.178+0.111' X ... Z w

m ,2-0,64

is 53 • J::! 47.5 :ii! ::e cc CI :r: Q

~ cr: 4ll.o CD

t!! 3

<I

32.5 I 220 250 200 310 340 370 400

GRAIN YIELD (gfm2)

(B)

Figure 6. The relationship between the (A) ir/red rudiunce ratio and (13) normalized difference integrations and finnl grain yield for the spring portion of the growing season (Refer also to Figure 5).

16

1111_

~

~

i I

11 "'I,

;

.... ~

, ~ I

_"t'_'-®e1llM'bl! __ "_. _ .. __ JiSi! ____ ..... ·_ti'!:f"_, .... ·_~~

integra tioll period of J lilian un tc 80-112 only explained 0,48(J, of'the vnl'iubility in final gruin yield

Figlll'e 5).

Any slIccessful npplicatioll or tlleSI.! rdationships: for re~iOmlll'l.'l1lotc sensing pr!.'uictioll of

winter wheat gmin yield must be tlhll' to account for within rl,;'l:!wl\ differences in winler wheat crop

phenology. From Ta bit,.' 4 W(' showed that the r\.'gfl..'~'iitlll lh.'l'jn.'t! cquiltinll coerrh:icll ts varicd I'm

I'he 40·duy pcriod (Julian t,!:.ih..'s J 11·152) wlll.'I\' hi~r}tly si,!.!Ilil'kallt rorrl.'lations between til\.! spcl..'tral

data and fillal grain yil.'ld \V~'l\" l\.'porh,'d {Tahles 3 .mtl ·lI.Ilm ... "Ih~h t I,.'rop plll.'lWlogy llini:n.mcl.!s

url.'U duration tedllliqul.' must h.c: clllployed.

Crop cah:l1dnr inJ'ol"ll1ution is f'I'I.'qtwntly lnckin!! in l.'xtra-I.'Olll1tI'Y situations. A possibl~ remotely

scnseu suhstitute for this l:ollJd bt' tlw cOlllhinntion orspe~tnd datu and ul.!cul11ultlted tempenltUJ'I.'

units (Figl1l'e 7). This follows frolll thc bask l\'liltiollsilip of tCllllk'l',llure to biological activity.

WIH,'n l'ombilled with j'l.'d [lilt! photOl:!l'lIphk infrared spcl.'tral dnta, l'eprcs!..'1l ting the plant cmlOpy

vi!!(lI' Ol' potential for .!!H1wth, this may have hl,.'twel'll rcgion and/or bL'twccn Yl.!nr comparison utility.

GROWING DEGREE DAYS

Figlln.~ 7. A possible spectral crop calendar llsing th!..' nOl'malizcc( dirJ'cJ'I.)lH.:e and nccunntlatcd tcmpcl'aturc units (I.e., gmwing degree dnys).

17

I ;1 ,I

"1

_ J

, ! i

I ; i

444=*

In the ~VCllt thut n \!omblnutioll of spcl.!trullllld Uocullllllntcd tcmpcrntllre unit dutu would be

llllsntisl'nctol'Y. f'J'lJqllcllt satellite COYCJ'(Ig~ would be required to Ilpply the I~af arCH duration conc\;lpt

for growing SIJHSOl1 intcgrntioll of splJclral (han. As shown ill Figure ;!. frequent satellite coyemge is

needed to record the ral~id onset of plnnt clIlloPY stress conditions. It is casy to vislInli7.c the situu-

fioll in Figure J if there were only 4. 5, 0,' 6 duta points inst~HlI of the 21 WI: collect~d. Obviollsly.

elltire stress periods might not he detected if only a few obsel'vutiolls were available. ':or yield COIl-

sidel'lltiol1s. the OCCllI'j'C!lCC or the stress condition is of pnramOllnt imp(wtlll1ce. If a period oc' w,lter

stl'eSs OCClll'S dul'ing hctldil1~ 01' during the grain filling period, the reduction of the f!,l'nill yicld is 111w.:h

greater than ir this SHtllc stl'CSS condition OCClIl'S at some other time.

Spl..'ctral datu arc capable of providing large-urea information highly rl!latcd to crop cOlldition

01' vigor. WIlI.Hhcr thesQ data are used in conjunction with othel' l'cmotdy sensed information, by

simulation modelers, 01' by themselves. it is appnrclH that rel110tely sensed red lind photographic

in rra red spectral da tn can supply valuable crop condition in forma tion,

18

J .~ i f

.i

't' ;~

1; ~

I r f ... [ !

l f

~ r r

f ,

. . - .

CONCLUSIONS

J, l.;'rl.'Cjllcntly collected spcctl'llJ data were shown to be highly 1'\!lat~d to l.'l'OP condition. Two perI-

ods or water stJ'~ss WCI'~ J'~adily uppal'~l1t.

2. A 40-dny period existed 1'1'0111 the beginning of stem ct0Jlgatiarl through ullthescs dUring which the

spectral dntn were highly cOl'relnted with final grain yield and explained at least 60~ of the vuriability.

3. The integrated spectral datu explnined 64~' of the vUl'iability in within field final gl'nin yield. This

c;:ouJd not be explained lIsing meteorological models us the micra-climate conditiol1s Wl!I'C very similur

for our expcrirl1cntuJ wheat field,

4. LUl'ge-scnlc extra-coUll try applicntion for OUt findings would require a spcctml crop en1cndur, We

propose u possible spectral-accUlllulated tempcrature unit crop calendar. /t' this is not feasible, wc

suggest frequent sat~llite obsel'vations Hlld applicution of the lear tU'en dUl'ation concept.

S. The combination ot'sPl!ctrul data und otltcr envirolllllentnJ or Hgronoll1it: data will improve the

pl'edktivl.l utility of yidd ro!,~c,:asting. While spectral untn un: uscl'ul by thcmselvl!S. tlll.lY nrc more

lIseful in combinution with other types or data.

ACKNOWLEDG.MENTS

We tlHlIl k Palll Pi 11 tel', .J r.\ Ray ,l (Wkson, ,l 01111 Col well, lind WaYl10 Willis for ~ncolll'tlgeJl10J1t and

suggestions on the 11H1l1l1SCript. We than I< thl! J'ollowing peopll.l for help in the colll.lctioll of data in

the Held: Chris ,lustice. Dan Kil1ll.ls, Donna Strahan, Diana Crowley. Maryann Karinch. Charley Walthall,

.Josette Pastor. Jelll.l Helkima. Steve Bogash. Don Fril.lliman. Bill .JOI1I.lS Hl1d Bill WigtOl1, We thallk Frec-

mall Smith for the loan cUhe instrument.

19

;

f ~

J ~ f i t 11' c, ] J ~

i .~ ;1

j ~, ,;II j ",. , If

-l .~ ,

i,;

i!

.:~

J 'f 4

'~

.' ,;1'. . .1'

I I r ..... r

~ f

I t

, ) :

• i-- r ............... 'I --,,-~""'"

REFFfWNC'ES

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w

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20

• .. 4.h

~ .. ,I

r-~- =-

,~ Hl tJ

I

I

, ~ ~b

i

t I r ~

f I I ~ , t t

r

I i

I' - 1 ~. :

-J T.

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i}, ,i·:"n!;t1Pft.itJ"'!'~ _

sa ""-;;::;;1111

, '

f t . ~)

r

-- - '----------..~~- -~-~-~--__ ' .... _li ... r --OJiliiiiliajijiliiliillilliiiiiilL44-

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