Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements

REMOTE SENS. ENVIRON. 36:45- 53 (1991)

Estimating Grain Yield of Maturing Rice Canopies Using High Spectral Resolution Reflectance Measurements Michio Shiba yama and Tsu yoshi Aki yama National Institute of Agro-Environmental

Sciences (NIAES), lharaki, Japan

T he grain

yield of paddy rice was estimated from high spectral resolution rejectance iti data (400-1900 nm interval) taken approximately 1.5 months after heading (approximately physiological maturity of the grain) during three crop seasons (1985, 1986, and 1987). Spectral data were smoothed by simple moving averages 20 nm and 40 nm wide and the second derivatives of R and logil /R’ were calculated for 60 central wavelengths between 480 nm and 1680 nm. The multiple linear regressions for these 60 central wavelengths and also for all the combinations of normalized difference indices from them versus grain yield were obtained. The smoothed and the second derivatives of reflectance and the normalized differences for each season were individually used to derive regression equations to estimate the remaining two seasons’ yields. Normalized differences computed from second derivatives of R were more e_ffctive and stable for multiseasonal estimations of yield than th,e smoothed reflectances or the second derivatives of the reflectances and log(l / R). This result oglers a method to evaluate the grain yield of crops whose grain weight increases during senescence and is therefore afleeted

Address correspondence to Michio Shibayama, National Inst. of Agro-Environmental Sciences, Department of Environmental Planning, Kannondai 3-I-1, Yatabe, Tsukuba, Ibaraki 305, Japan. Receiced 30 April 19.90; revised 13 December 1990. 0034-4257/ 91/ $3.50 OElsecier Science Publishing Co. Inc., 1991 6.55 Avenue of the Americas, New York, NY 10010

by the weather the season.

and agronomic

conditions

late in

INTRODUCTION Spectroradiometers using diffraction gratings or circular variable filters (CVF) have been utilized in both the laboratory and the field to search for wavelengths useful for remote observations of vegetation (Learner et al., 1973; Ferns et al., 19841. The relatively high spectral resolution of spectroradiometers over a wide wavelength range provides more detailed information than can be obtained by bandpass radiometers (Ferns et al., 1984). In the field of near infrared reflectance spectroscopy (NIRS) the first and second derivatives of the spectra are used to improve the precision and reproducibility of spectroscopic measurements. The NIRS technique is now routine for laboratory analysis of the quality of forages, grains, and foods (Norris et al., 1976; Hymowitz et al., 1974; Iwamoto, 1980). Card et al. (1988) predicted leaf chemistry from log(l/R) and the first and the second derivatives of log(l/ RI, where R was measured by a spectroradiometer with an integrating sphere attachment and a stepwise multiple regression program. They concluded that the above-mentioned technique might be useful for extracting vegetation canopy biochemical information by remote sensing.

45

46 Shibayamaand Akiyama

A high wavelength resolution scanner is planned for a future series o f earth observation satellites (NASA, 1987). Demetriades-Shah and Kanemasu (1989) and Demetriades-Shah et al. (1990) presented the effectiveness of derivative spectra to detect plant water and disease stress from reflectance data taken from the 400-1100 nm range at 2 nm intervals. They showed that the derivative procedure suppressed the effect of soil background reflectance and made it possible to extract information about plant leaves, from the overall canopy reflectance. In our study, data were extracted at the central wavelength of 5 nm and 10 nm intervals of continuous 400-1900 nm spectra of rice canopies approaching maturity during three cropping seasons. Simple filtering and derivative techniques were applied and the resulting spectra were tested for their ability to estimate grain yield of two seasons using regression models computed from the remaining season. Our experiment was conducted 1.5 months after heading when the grain was at about physiological maturity. It has been shown that rice grain yield can be predicted by radiometric biomass estimation at an earlier stage than used in this study (Miller et al., 1983; Patel et al., 1985; Wiegand et al., 1989). Miller et al. (1983) pointed out that the near infrared to red ratio, [R(760-900)/R(630-690)], taken at the stages of panicle differentiation or heading, could be used to forecast rice grain yield through the correspondence between dr3, matter and grain yield, but that this ratio was of no value during grain filling and grain maturation because the reflectance ratio could not estimate rice canopy biomass during this period. However, this is an advantage for our study because, at this late stage, reflectance is not well correlated with total biomass, so that we can test whether the new derivative spectra are unambiguously sensitive to the grain content of the canopy. On the other hand, Matsushima et al. (1954) reported that kernels of medium maturing rice cultivar could be aborted until 33-38 days after heading. Grain yield of commercially produced rice in Japan is often affected by the weather, pests, and water controls during the grain filling and maturation stages, and no appropriate growth/yield model exists for this period. Therefore, the possibility of spectral estimation for grain yield needs to be investigated again using high

resolution spectral reflectance data after the fruit bearing percentage has been determined.

MATERIALS AND METHODS

Radiometric and agronomic data were taken in 1985, 1986, and 1987. Plant Materials

In 1985, rice plants (Oryza sativa L.), including three Japonica type cultivars and two IndicaJaponica crossbred cultivars, were grown in 20 concrete lysimeters (3 reX3 m, 1 m in depth) located at NIAES, Tsukuba. Ten plots were transplanted early (6 June), and 10 plots were transplanted late (3 July). Normally fertilized and nonfertilized treatments were established for each cultivar and planting date. In addition, a different cultivar of rice was transplanted into each of three 50 m x 10 m concrete enclosed paddies at NIAES, and each paddy was divided into four subplots that received different amounts of fertilizer. In 1986, the Japonica type cultivar (Koshihikari) was transplanted on 23 May into the 20 lysimeters; treatments were for four fertilizer levels each replicated five times. Another Japonica type cultivar was grown in the three concrete framed paddies; one was treated uniformly, the second paddy had four fertilizer levels, and the third one had three subplots of planting pattern (30 c m X l 5 cm, 15 cruX30 era, and 21 cruX21 cm row and plant spacings, respectively). In 1987, a Japonica cultivar, Nipponbare, was transplanted on 6 May into 22 plots. Four fertilizer levels applied on two different dates established eight treatments with two replications and three of the remaining six normally tbrtilized plots were shaded after heading to give three difJ~rent levels of light deficiency stress. The planting density was approximately 22 plants/m 2 for each of the three seasons. Grain Yield

From the lysimeter plots, two sets of nine or 16 hills were harvested from each paddy, and three to 10 sets of nine hills were harvested from each subplot of concrete framed paddies to determine grain yield. The yield samples were air-dried, and

ttigh Resolution Spectra of Rice 4 7

threshed to grains with hull, and their weight expressed on the basis of 14% water content,

Radiometric Measurements The optical fiber light guides of the speetroradiometer (Shibayama et al., 1988) were held looking vertically down from a height of about 2.5 m above the canopy surf:ace resulting in a sampled area of about 60 em in diameter. Radiometrie readings were made at 5 nm intervals in the wavelength range 400-900 nm and at 10 nm intervals in the range 900-1900 nm. Wavelength resolution was 2.8 nm in the former wavelength range and 5.6 nm in the latter range, respectively, Twenty-two seconds were required to complete one scan and the readings were repeated five times for each subplot. The mean of five repetitions was divided by the readings from a reference panel sprayed with Kodak White Reflectance Coating (BaSO4), to compute reflectance Rx, where the subscript x denotes the wavelength (nm). The reference panel observations were made under the same ambient conditions as the canopy observations but the reference panel was not calibrated in the field (Jackson et al., 1987). The radiometric measurement dates were 26 September for the early planting, 19 October for the late planting in 1985, and 22 September in 1986. In 1987, measurements were made on 3 October. These dates corresponded approximately to physiological maturity of the grain 1.5 months after heading.

Treatment of Spectra Spectra consisting of R~ at 5 nm intervals were smoothed bv a simple moving average of five consecutive observation points which correspond to four 5 nm intervals for the 400-900 nm range. For the 900-1900 nm range, R~ at 10 nm intervals were smoothed by a simple moving average of three consecutive observation points which correspond to two 10 nm intervals. The smoothing bandwidth realized was 20 nm. This spectrum is designated by a function a2o(Rx) i n the text. The simple moving average of nine and five consecutive observation points (eight and four intervals) was also determined and is designated by the function a4o(Rx) (40 nm smoothing bandwidth). The second derivatives of the smoothed spectra of

20 nm and 40 nm smoothing bandwidths are designated by the functions b2o(R~) and b4o(Rx) , respectively. The approximate second derivative of each wavelength point was calculated by the equation (Iwamoto, 1980; Card et al., 1988), b(R~.) = a(R,. ,,,,.) + a(Rx+,,,. ) -ea(R~),

(1)

where x - d w and x + dw indicate the center points of lower and upper moving average bandwidths adjacent to the bandwidth of the central wavelength x. Likewise the functions c2o(R x) and C4o(R:,) designate the second derivative of log(1/Rx), which corresponds to absorption (Norris et al., 1976; Card et al., 1988). The more consecutive wavelength intervals used for smoothing, the more continuous is the second derivative (the smaller the peaks and valleys) but the more distorted the smoothed spectrum is from the observed spectrum (Iwamoto, 1980). Several moving average intervals or bandwidths were examined in our data, and the 20 nm smoothing bandwidth and the 40 nm smoothing bandwidth were chosen because these gave good noise suppression, but still emphasized important characteristics of the reflectance signatures. Sixty wavelengths which are the central values of the moving bandwidth were selected from the smoothed spectral data (480-1680 nm) for analyses. Normalized difference (ND) indices were calculated for the 60 wavelengths as designated by

ND,,---

a(R 2) '

ND,,= a(Rxi) + a(R,.2)+0. 4 ,

ND(.=a(R,.i)+a(R,.2)+2,

(2)

(3) (4)

where subscripts a, b, and c denote moving average reflectance, second derivatives of smoothed reflectance spectra, and second derivatives of log(1/Rx), respectively. ND (Deering, 1978; Tucker, 1979) calculated from a visible red and a near infrared band is a widely used vegetation index. We decided to check all the combinations of two band difference/sum ratio available for the 60 wavelengths. ND in this paper is used as a

48

Shibayama and Akiyama

general term for any two band difference/sum ratio according to Jackson et al. (1980). The constants 0.4 and "2 in Eqs. (3) and (4) were used to avoid "division by zero," absolute values were used to avoid negative values, and Rxl and Rx,2 refer to the individual pairs of 60 smoothed refleetanees at wavelengths xl and x'2. Regression analyses were conducted using the model,

0.5

A

0.4 0.3 0.,2 0.1

O.%o 600 I

Y = b o q- b l X 1 + I).2X 2 q- b:3X 3 " ' " b i X i + e,

(5)

where Y is the grain yield, X i is the smoothed spectral reflectance or the ND index, b i is the partial regression coefficient, and e is the error term. Independent variables (X i) were selected by a backward elimination stepwise multiple regression program for three seasons' pooled data of the smoothed spectral data and yields. Since the number of possible wavelength combinations for calculating ND was large (e.g., 1770), a forward selection multiple regression method (no elimination) was chosen for selecting ND variables. The computer program added the ND variable that had the highest correlation with the residual calculated from the equation of the previous step. The addition of terms ended when no additional ND was significant at 0.05 level by a Student's t-test. The wavelengths selected for the pooled data were used to build the multiple regression equations anew for the 198.5 data set ( N = 30). The 1986 and 1987 yields were estimated by these equations; then the simple correlations between observed and estimated yields for 1986 and 1987 were calculated. In the same way, equations from 1986 data set were also applied for estimating 1985 and 1987 yields, and then equations from 1987 data were applied for estimating 1985 and 1986 yields.

RESULTS

AND

DISCUSSION

Plants grown in 13 of the lysimeter paddies in 1986 lodged when a typhoon struck 15 days after average date of heading (2 September). Therefore, the number of treatments available for analysis was 30 in 1985, 15 in 1986, and 22 in 1987. Figures 1A-1C show examples of the smoothed reflectance spectrum, R x of a matured

I

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I

I

I

I

800 1000 1200 1 4 0 0 1600 1800

o.a [3 02 ~4

~

L

0.1 0.0 -0.1 -0.2 '

i

I

I

I

I

)

400 600 B00 1000 1200 1400 1600 lflO0

0.6 0.4

C

0.2 0.0 ¢)

-0.2 -0.4 -0.6 I I I I ) I I -08 ' •400 600 800 1000 1200 1400 1600 1800

Wavelength Figure 1. The spectral reflectance (R) of a matured rice canopy (cultivar Nanking 11) (A), its approximate second derivative (B), and the approximate second derivative of h)g(1/R) (C). The smoothing bandwidth for moving average is 40 nm.

rice canopy (cultivar Nanking 11) [a4o(Rx)], the second derivative of R e. [b4o(Rx)], and the second derivative of log(1/R x) [c40(Rx) ] at 60 wavelengths. The pattern of b4o(R x) as a function of wavelength is basically similar to the second derivative of sugar beet canopy reflectance presented by Demetriades-Shah and Kanemasu (1989) in the corresponding wavelength range of 480-1100 nm. Several small peaks found in their data in the wavelength ranges of 600-650 nm and

High Resolution Spectra of Rice 49

800-950 nm were missing or replaced with medium scale single peaks in our data. Plant materials used were different but the difference of wavelength resolutions of the two spectroradiometers and the spectral bandwidth or wavelength step used in the derivative computations are the principle reasons for the slight differences between our derivative spectrum and that of Demetriades-Shah and Kanemasu (1989). In Figures 2A and B, the correlation eoeflqeients (r) between the processed refleetances and the grain yields in the pooled data are plotted versus 60 wavelengths selected. Figure 2A shows

?~0I~-n w i n d o w w i d t h 0.4 A 0.3 0.2 0.1 0.0 -0.1 -0.2 Ii

I

-0.:3

-0.~01

I

600

I

I

I

I

I

t

flO0 1000 1200 1400 1600 1BOO

Wavelength (ran) 4 0 r a n wi_ndow w i d t h 0.4 [3 0.3 0.1

i

-0.1 -0.2

" /

"", , "

-0.3 .,~

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-0. 00 600 600 1000 1200 1400 1600 1800

Wavelength (ran) Figure 2. Simple correlation coefficient (r) curves for grain yield. The curves resulted from processing spectral reflectance at 60 central wavelengths between 480 nm and 1680 nm and regressing each versus plot grain yield: (- - -) smoothed by a moving average designated a(R~); ( - ) second derivative of smoothed spectrmn [b(Rx)]; ( . . . ) second derivative [c(R~)] of absorption (log(1/R~)); (A) 20 nm smoothing bandwidth; (B) 40 nm smoothing bandwidth.

the results of the calculations using a2o(Rx), b20(Rx), and c.20(Rx) (moving average bandwidth of five observations at 5 nm intervals and three observations at 10 nm intervals). Correlations of grain yields with a4o(Rx), b4o(Rx), and c~,l(R~) (moving bandwidth of nine observations at 5 nm intervals and five observations at 10 nm intervals) are shown in Figure 2B. The eorrelograms of the second derivative spectra [ b( R., ) and c(R x)] have stronger peaks and valleys than the smoothed reflectance spectra [a(R~)]. The position of peaks did not move when the smoothing bandwidth changed from 20 nm to 40 nm; however, the absolute values of r for b(R.,.) and c(R.,.) changed. The highest r = 0.35** was observed between the yield and b2o(Ri22o) (Fig. 2A) but any single wavelength's reflectance was not sufficient for estimating grain yield. On the other hand, direct relationships (r e = 0.63-0.81) were reported between senescent grass canopy biomass and reflectance at wavelength in the 500-800 nm range (Tucker, 1978). For rice canopies high leaf area and biomass are associated with high values for reflectance in the near- and mid-infrared during the vegetative growth stage (Martin and Heilman, 1986; Shibayama and Akiyama, 1989). However, in the maturing stage higher yielding plots did not necessarily have the highest reflectance in the maturing stage. The 3-year combined data sets ( N = 67) of refleetances processed by the three methods and two smoothing band widths ( 3 × 2 = 6 data sets) were individually regressed against grain yield by a multiple regression program. Selected central wavelengths, the coefficients of determination (Re), and the probability level of the F statistic for entering and removing terms are summarized in Table 1. Table 2 presents the wavelength pair combinations selected from the 60 wavelengths considered as expressed by Eqs. (2), (3), and (4) and gives the R e for those selected. The wavelengths selected for the pooled data (Tables 1 and 2) were also used in equations for each individual year and equations for that year were used to estimate yield for the two remaining years. That is, although the wavelengths or the combinations of wavelengths to calculate ND indices were common, the partial regression eoefflcients were obtained anew from each season, and the equation was applied in the other two seasons. The simple correlation coefficients (r) between

,50 Shibayamaand Akiyama Table 1. Selected Central Wavelengths

( n m ) from 3-Year

Pooled Data ( N = 67) in M u l t i p l e Regression Analyses. 20 nm

Smoothing Bandwidth

a(B~) b(B~) c(B9

R2 pt,

40 nm

Smoothing Bandwidth

a(Bx)

b(B~)

c(B~)

500" 10(~) 680 1240 980 1520

920 680 880 1220

1180 12110 1240

1(12(I 1220 1600 700 720 680 760 1420 540 660 940

1160 1220 1180 740 1300 1420

1020 780 660 620 1100 640 1460 1220 800 500 1200

0.54 0.01

0.37 0.01

0.21 0.01

0.56 0.05

0.50 0.05

0.63 0.01

"The wavelengths re placed in order of significance of the

partial regression coefficients. /'Probability level of the F statistic for entering and removing terms.

Table 2. Selected Central Wavelengths ( n m ) for N D s from 3-Year Pooled Data in Forward Selection Multiple Regressions" 20 n m

R2

Smoothing Bandwidth

40 nm

Smoothing Bandwidth

N D ,,

NDI,

N D ,.

ND ,

ND[;

ND,.

1140, 1320' 1200, 1220 540,600 1280,1300 820,840 1240,1260 780, 920 480,1400

680,960 700, 1460 620,1220 640, 1660 1480,1500 640, 1260 620,640 1120,1560 740,1320

720,920 980, 1580 680,1380 620,1220 1180,1600 680,1440 1060,1660 640,1500 1240, 126(/

1140,1200 740, 1/)611 1520, 1540 1020, 1160 820, 840 760, 800 1380, 1460 540,620

1000, 1120 540, 1080 920, 1660 580, 1260 840, 1580 980, 1480 700,1360 720, 1080 1220,1240 580,1640

620, 10611 580, 780 1020, 1220 560,1480 920,1020 780,1060 1080,110/) 620, 1020 1380, 1400 700,940

0.57

0.73

/).77

0.63

0.71

0.77

"Probability level of the Student's t statistic for entering terms is 0.05, /'The 10 central wavelength pairs used to estimate yield in Figure 3. ' T h e wavelength pairs are placed in order of significance of the partial regression

coefficients.

observed and estimated yields are summarized in Tables 3 and 4. These results indicate that yields cannot be estimated even from multiple linear regression equations that use the a(R,), b(Rx), and c(R x) of both 20 nm and 40 nm bandwidths. The equation based on 1985 observations gave r = 0.49** between C2o(Rx) and grain yield and r = 0.73*** for C4o(Rx). But equations based on c(R x) for the other years gave nonsignificant correlations. Similarly, correlation coefficients between grain yield and estimated yield from a(Rx) and b(R x) were weak and unstable among base years used for

developing the equations. Even though one can utilize derivative techniques, the direct relation between yield and the spectral data observed for the senescent or nearly senescent canopies may be too weak to be significant. Regression equations using ND produced higher correlations than regression equations based on the smoothed or the derivative spectra directly (Tables 3 and 4). Estimation ability was best for ND b, where the wavelengths used are determined from the second derivative of the reflectance spectrum, b(Rx). The wavelength bandwidths over which the spectra were smoothed affected the

High Resolution Spectra of Rice 51

Table 3. Simple Correlations ( r ) between Observed and Estimated Grain Yield of Rice for Various Combinations of Base Years and Test Years for Moving Averages of 20 nm Smoothing Bandwidth

Base Test )'ear Years a(B,)

(N)

B(B,) c(R,)

(N)

"85 (30) "86 (15) '87

'86, '87 .19 (:37) '85, "87 .36** (52) '85, "86 .48***

(22)

ND,

(,9

NDI,

NO

(r)

-.04

.49**

,61"**

.83"**

.68"**

.41"*

.27

.29*

.58*** .33*

.20

.19

.42**

.65*** .29

(~5)

*Significant at 5% h've]; **, 1~ level; ***, 0.1% lmel.

Table 4. Simple Correlations ( r ) between Observed and Estimated Grain Yield of Rice for Various Combinations of Base Years and Test Years for Moving Averages of 40 nm Snloothing Bandwidth

Base Test Year )'ears a(B,) b(B,)

(N)

(N)

"85

"86,'87

(3o)

(:37)

'86 (15) '87 (22)

'85,'87 (52) "85.'86 (45)

e(R,)

ND,

(r)

ND~

ND.

(r)

.26

.48**

.73*** .64*** .77***

.14

.14

,12

.68*** .48*** -.04

.16

.21

.17

.29

.77***

.64***

.70***

*Significant at 5% lexe]: **. l% level: ***, {).1% level. "Scatter plots shm~n in Figure 3.

correlations rather erratically. Correlations were particularly low for ND, and ND,. (20 nm in smoothing bandwidth) and ND c (40 nm in smoothing bandwidth) when equations developed for 1986 data were applied to 1985 and 1987 yields. Although ND calculation of a(R x) and c(R.,.) improved the estimates of yield, they were still not good enough for nmltiseasonal application. Multiple regression equations of ND/, from b(B,) gave relatively favorable and stable estimates of yield in this study. The highest correlation (r = 0.83***) was observed for 1986 and 1987 yield estimated from the 1985 equation (Table 3). Figures 3A, B, and C show the scatter diagrams of observed and estimated yield by the equations constituting ND/, (40 nm in smoothing bandwidth, Table 4). The 1986 and 1987 yields estimated from the 1985 data are shown in Figure 3A versus observed yields. Similarly, the 1985 and 1987 estimates fi'om the 1986 data and observed yields are shown in B, and the 1985 and 1986 yields estimated from the 1987 data are presented in C.

Although the simple correlation coefficients 0.77***, 0.48"**, and 0.77***, respectively, are highly significant statistically, they account, at most, for only 59% of the variation in yield. The root mean square of error (RMSE) varied from 61.9 g / m 2 to 94.1 g / m 2. It is not appropriate to conclude that multiple regressions using ND indices of derivative spectra can be immediately used for yield prediction, but its superiority over usual spectral indices or linear regression equations using simple band reflectances is significantly shown by the correlation coefficients in Tables 3 and 4. We speculate that noise components such as a seasonal drif~ of reference panel parameters, solar angle effect, and inhomogeneities among the canopy sites viewed may be removed by the derivative treatment and that some principal peaks of spectra may be emphasized by the ND calculations. Reflectance in the near infrared wavelength region, 900-1300 nm, and visible region (500-700 nm) was important in the linear regression equations (Tables 1 and 2). The visible range responds to the loss of chlorophyll and browning that accompanies ripening and senescence (Idso et al., 1980). R12,2o a n d / o r its adjacent wavelength reflectances frequently appeared as important variables in the regressions (Tables 1 and 2). Rim o and R ieoo were reported to be useful for biomass estimation around heading stage (Shibayama et al., 1988), and Rle6o was used to estimate rice yield by regression (Shibayama and Munakata, 1986). The wavelength range 900-i300 nm includes some water absorption bands and the overtone absorption bands of CH, NH, and OH groups that compose organic materials (Kaye, 1954). Iwamoto (1980) showed that starch absorbed at 1220 ran. Therefore, starch in the ripening grains and stems may affect the spectra somewhat. Derivative treatment and ND calculation described above may possibly reveal the hidden, interfered peaks of spectra. In this study, the radiometric measureinents were made only at one time, at maturing stage. By comparison, Idso et al. (1980) estimated grain yield of wheat and barley from the senescence rates estimated from the transformed vegetation index six (TVI6) from reflectances in the spectral ranges 500-600 nm and 600-700 nm. They reported a correlation coefficient r = 0.78 between the wheat grain yield and delta TVI6 per day. This method, however, needs at least two measurement

52 Shibayamaand Akiyama

1000

I000

a

8OO

RMSE=61.9 g / m 2 0

/"

1-=-.4[] ""

P,kKgE=94.1 g / m 2 ×

")0

X Xx

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600 u~

400

.'0"

4O0

X,'"

..O

x 196.5

• 19116 o 19117 ' 2OO

00

, , 4OO 800

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Grain yield ( g / m

1#/CI

o 1987 00

,

I 400

I , , 8OO 8OO 1000

(g/m =)

2)

°

°

&

4

4

F

I

I

I

x

Figure 3. Observed and estimated grain yield of rice. (A)

00

200

400

800

fl00

1000

Grain yield ( g / m 2)

dates to obtain the senescence rate of a crop canopy. The results in this study, however, indicate that field radiometric measurements can be applied not only for leaf area or biomass estimations but also for more complex plant information such as grain yield from measurements on one date using a radiometer with high wavelength resolution over a wide wavelength range and proper calibration equations. The results seem to make it possible to delay observations for spectral estimation of rice yield until 1.5 months after heading

Estimation of 1986 and 1987 yields by a linear regression equation built from 1985 data• T(~n N1)s of b(R.,.), (NDb, Table 2), were used in the equation. The fimction b(R x) designates the second derivative of reflectance (R x) smoothed by a moving average of 40 nm bandwidth. (B) Estimation of 1985 and 1987 yields with an equation of ten NDbs of b(R x) taken in 1986. (C) Estimation of 1985 and 1986 yields with an equation of ten NDbs of b(R x) taken in 1987. RMSE = root mean square of error.

(maturity of the grain)• The methodology also needs to be tested for its ability to detect important plant and crop information such as physiological stresses, water content, or nutrient conditions. This work was financially supported by the Ministry of Agriculture, Forestry and Fisheries, Japan under the "'Green Energy Program of Research• '" The technical assistance in computation given by Y. Murakami and S. Morinaga and the field assistance of K. Matsumoto, M. Tobita, T. Omizu, and H. Uchiyama at NIAES, Tsukuba, Japan, are gratefully acknowledged. The authors thank Dr. C. L. Wiegand, USDA / ARK Weslaco, Texas for his helpful reviews of the manuscript.

High Resolution Spectra of Rice 53

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