Modular Optoelectronic Scanner (MOS-B) spectrometer data over parts of Northern India was evaluated for wheat crop monitoring involving (a) sub pixel wheat tractional area estimation using spectral unmixmg approach and (b) growth assessment b3 red edge shift at different phenological stages. Red shift of 10 nm was observed between crown root initiation stage to flowering stage. Wheat fraction estimates using linear spectral unmixing on Feb. 13. 1999 acquisition of MOS-B data bad high correlatiol7 {0.82) with estimates from Wide Field Sensor (WiFS) data acquired on same date by IRS-P3 platfonn. It was observed that live bands 14.5.8.12.13 MOS-B bands) are saffieient for signature separability of major land cover classes viz. wheat, urban, wasteland, and water based on purely spectral separability, criterion using Transformed Divergence (T.D.) approach. Higher number of bands saturated the T.D. values. [n contrast, performanee of sub pixel fractional area estimation using unmixing decreased drastically for eight bands (4.5.6,728.9. 12,13 MOS-B bands l chosen from optimal band selection criteria in comparison to full set of 13 bands. The relative deviation between area estimated from Wifs and MOS-B increased from 1.72 percent when all thirteen bands were used in unmixing to 26. I0 percent for the above eight bands

Dadhwal, V. K.

Navalgund, R. R.

Singh, R. P.

English

Publications of the IAS Fellows

@ 
Photonirvachak 
Journal of the Indian Society of Remote Sensing, Vol. 27, No. 3, 1999 
Wheat Crop Inventory Using High Spectral 
Resolution IRS-P3 MOS-B Spectrometer Data 
R.P. SINGH, V.K. DADHWAL and R.R. NAVAI,GUND 
Relnote Sensing Applications Area 
Space Applications Centre 
Ahmedabad-380053 
ABSTRACT 
Modular Optoelectronie Scanner (MOS-B) spectrometer data over parts of Northern 
India was evaluated for wheat crop monitoring involving (a) sub pixel wheat tractional area 
estimation using spectral unmixmg approach and (b) growth assessment b3 red edge shift at 
different phenological stages. Red hitt of 10 nm was observed between crown root initiation 
stage to flowering stage. Wheat fraction estimates using linear spectral unmixing on Feb. 13. 
1999 acquisition of MOS-B data bad high correlatiol7 {0.82) with estimates from Wide Field 
Sensor (WiFSI data acquired on same date by IRS-P3 platfonn. It was observed that live 
bands 14.5.8.12.13 MOS-B bands) are saffieient for signature separability of major land cover 
classes viz. wheat, urban, wasteland, and water based on purely spectral separability, criterion 
using I'ransformed Divergence (T.D.) approach. Higher number of bands aturated the T.D. 
values. [n contrast, perfbrmanee of sub pixel fi'actional area estimation using unmixing 
decreased drastically tbr eight bands (4.5.6,728.9. 12,13 MOS-B bands l chosen from optimal 
band selection calteria in comparison to lull set of 13 bands. The relative deviation between 
area estimated from Wits and MOS-B increased from 1.72 percent when all thirteen bands 
were used in unmixing to 26. I0 percent for the above ight bands. 
Introduction 
Development and operationalization of 
Remote Sensing based techniques using space 
borne sensors for monitoring crop growth over 
large areas for crop production forecasting has 
been one of the thrust areas of research in India 
(Navalgtmd el al. 1991). Large area crop 
production forecasting requires accurate area 
estimates as well as appropriate indicators of 
crop vigour and growth. High spectral 
resolution data makes it possible to derive 
(Recd. 14 Sept. '98: in final form 24 Jan. 2000) 
168 R.P. Singh et al. 
sensitive spectral parameters like red edge and 
red edge shift (Collins, 1978, Demetriades-Shah 
et al. 1988) which are related to chlorophyll 
content (Collins, 1978, Horler et al. 1983, 
Gitelson et al. 1996), leaf nitrogen content, leaf 
area index and leaf angle distribution (Guyot et 
al. 1992). Most of the works on use of high 
spectral resolution on crop studies are either 
ground based or carried out by airborne sensors 
which precludes its direct applicability for 
regular monitoring for large areas. Satellites for 
global monitoring that employs narrow 
bandwidth sensors are generally optimised for 
coarser spatial resolution. High spectral 
resolution MOS-B sensor onboard IRS-P3 
platform provides sensitive measures for 
estimating crop biophysical parameters but is 
less effective for crop inventory in Indian 
condition due to inadequate spatial resolution. 
Within the scope of above limitations for crop 
monitoring, there lies a need for exploring 
newer techniques addressing both the uses from 
such type of data. 
This study discusses the feasibility of 
using MOS-B spectrometer data for crop growth 
monitoring as well as area estimation. High 
spectral information in the red - near infrared 
(NIR) transition domain was used for crop 
growth monitoring using fitted red edge 
parameters. Use of multi-date MOS-B data for 
wheat monitoring over the large homogeneous 
wheat belt of Northern India during 1996-97 
rabi season to observe red shift, a sensitive 
parameter of crops biophysical properties and 
wheat area estimation using linear spectral 
unximing is reported. 
Material and Methods 
Data Used and Study area 
MOS-B data was acquired on five dates 
i.e. Dec. 27, 1996, Jan. 15, 1997, Feb. 13, 1997, 
March 4, 1997, and March 9, 1997 which 
covered crop phenology from crown root 
initiation stage to flowering stage of crop over 
large homogeneous wheat ract in Punjab, India. 
Synchronous WiFS data of Feb. 13, 1997 
(Plate 1) was also acquired for identification of 
endmembers as well as validation for linear 
spectral unmixing analysis. 
Methodology 
Data of all the five dates from Dec. 27 
1996 to March 9, 1997 were registered using 
nearest neighbour esampling with root mean 
square error less than 0.5 pixel. Signatures for 
different land cover classes from MOS-B were 
generated for sites verified with WiFS data. 
Thirteen spectral bands (Table 1) were used to 
overcome the limitation of coarse spatial 
resolution of data using spectral unmixing 
technique for subpixel wheat proportion 
estimation. Estimated wheat proportions were 
Table 1: Spectral bands of MOS-B sensor 
Channel  No. Wavelenght  (nm) 
1 408+5 
2 443+5 
3 485-4- 5
4 5205+5 
5 570+5 
6 615+5 
7 650• 
8 685+5 
9 750+5 
I 0 870• 
11 1010• 
12 815• 
13 945• 
Plate 1. False colour composite of MOS-B data (Bands 1 I:R, 6:G, 3:B) and Wifs data showing wheat dominated 
region of Punjab, India. 
Plate 2. False colour composite of MOS-B data (Bands I 1 :R, 6:G, 3 :B) and fraction images of different land cover 
classes howing proportions ranging from 0 to 100 percent. 
Wheat Crop Inventory Using High Spectral Resolution... 169 
correlated with the wheat area estimated from 
WiFS data acquired on same date. Comparison 
of unmixing was also studied with two sets of 
band combinations i.e. eight bands of MOS-B 
(4,5,6,7,8,9,12,13,) chosen from optimal band 
selection criteria and all 13 bands of MOS-B 
data. Various steps followed in analysis include 
(a) computation of radiance using calibration 
coefficients supplied in header file (b) 
computation of apparent reflectance using solar 
irradiance at top of atmosphere and solar zenith 
angle (c) atmospheric correction by removal of 
Rayleigh path radiance, (d) inverted Gaussian 
fitting, (e) linear spectral unnixing and (f) 
selection of optimum spectral bands. 
Procedure 
MOS-B data was converted into radiance 
and subsequently converted into apparent 
reflectance. Solar extraterrestrial irradiance and 
Solar Zenith angle were used to calculate 
apparent reflectance. This apparent reflectance 
is modulation of pure target reflectance and 
reflectance due to atmospheric ontributions. 
An attempt was made to remove atmospheric 
effect due to Rayleigh contributions (eqn. [2] 
using procedure of Doerffer (1992). 
Reflectance p (X) is 
p ()~) : 7t L ()~) d2/Eo()~) COS 0 . . . . . . . .  [1] 
where L((Z.) is radiance observed at satellite 
sensor, Eo(Z) is extraterrestrial solar irradiance, 
ezis solar zenith angle. And d is mean distance 
between sun and earth in Astronomical units. 
Reyleigh path radiance L,. is 
L, = Eo t,. COo, Pr (F)/4 x COS 0 . . . . . . . .  [2] 
Where t,.is Rayleigh optical depth, Oo, is single 
scattering albedo, p,. (F) is scattering phase 
function and 0v is view zenith angle. 
Red Edge Using Inverted Gaussian Model 
Changes in chlorophyll, carotenoid 
concentration and leaf area index manifest as 
subtle spectral change in the visible and infrared 
portion of reflectance curve. The slope and 
position of the red edge which is related to 
phenological and health status was modelled 
using inverted Gaussian model (Bonham-Carter, 
1987) which is: 
R(A,) = (R s - R o) Exp -Ix-~~176 ....... (3) 
where Ro is the reflectance at the absorption 
maximum at ~.o. Rs is the reflectance at shoulder 
above 780 nm and cy is the Gaussian shape 
parameter (sigma) which determines the red 
edge value. Red edge inflection point is given 
by )%+G at which the slope is maximum. 
Spectral Unmixing 
Unlike high spatial resolution data which 
have high proportion of pure pixels, a large 
proportion of coarse spatial resolution data are 
spectrally mixed. In linear spectral unmixing 
analysis, it is assumed that signatures of a subset 
number of surface elements can reproduce the 
observed spectra when mixed together in 
various proportions. This subset may be referred 
to as endmembers, components, or factors; they 
in fact may be mixtures themselves (Gong et al. 
1991). 
Hence, given multi spectral information, it
is possible to model each pixel spectrum as a 
linear combination of a finite set of components: 
n 
ri = ~[](aij .x j) + ei ........ (4) 
j=l 
Where ri = mean spectral reflectance for the ith 
spectral band of the pixel containing one or 
more components; aij = spectral reflectance of 
170 R.P. Singh et al. 
the j th component in the pixel for the ith 
spectral band; x j = Proportion value of the jth 
component in the pixek ei = error term for the 
ith spectral band; j = 1, 2, . ....... n is number of 
components assumed in unmixing and 
i = 1,2,. ....... m is the number of spectral bands 
considered in the analysis. A constraint is 
applied, since the proportion values must be 
non-negative and the sum of proportions for any 
pixel must be one. 
xj  > or - 0 and Z xj = l , j  =1,2 ...... n. 
Proportion of each component was 
obtained by using singular value decomposition 
method with constrained least squares 
(Shimabukuro and Smith 1991). In order to 
have deterministic solution, the number of 
components hould not exceed the number of 
spectral bands, i.e., n < or = m. Once image 
components (end-members) are identified, the 
entire image can be unmixed, pixel-by-pixel. 
Spectral unmixing was carried out by first 
coding every pixel/scan line of MOS-B image 
with unique number. Image to image 
transformation equation was developed to 
transfer MOS code at WiFS resolution tbr 
locating the corresponding pixels on WiFS 
image for a given pixel of MOS image. It also 
helped in transferring the endmembers 
locations, selected from WiFS data for use in 
unmixing MOS data. Pure pixels of wheat, 
urban, wasteland and water classes were taken 
as endmembers. Accuracy assessment of 
fractional area was done at 70 random locations 
of 3x3 pixels from MOS image and estimated 
wheat proportion from corresponding WiFS 
pixels. Analysis was carried out using "unmix" 
module of a commercial software package. 
Selection of optimal spectral bands 
In order to determine the optimum bands 
for class discrimination from a given set of 
bands, some criteria must be established. The 
criteria used was based on average transformed 
divergence (T.D.). This was calculated from the 
class sample means and the class covariance 
matrices. The average transformed divergence is
based on the exponential of the divergence 
(Mahalanobis distance), and reduces the 
dominance of classes, which have the highest 
divergence. A branch and bound method 
developed by Narendra and Fukunaga (1977) 
was used for feature subset selection. 
Results and Discussion 
Inverted gaussian model (Bonham-Carter, 
1987) was fitted for MOS-B derived 
reflectances between 650 and 870 nm to 
estimate inflection wavelength and its 
subsequent change with crop stages i.e. red 
shift. Red shift of 10 nm observed from crown 
root initiation stage (703.8 rim) to peak 
vegetative stage (714.2 rim). An attempt was 
also made to analyze the error component and 
sensitivities of red edge detection by redncing 
the number of unknown parameters by fixing 
wavelength of the absorption maximum ()vo). 
Wavelength X,o was kept constant from 665 to 
685 nm at the interval of 5 nm. It was observed 
that maximum red shift variation occurred at Lo 
within 665 to 670 nm during crop growth cycle 
(Singh et al. 1998). 
All thirteen narrow MOS-B spectral bands 
were used for subpixel wheat proportion 
estimation using spectral unximing and 
validation of wheat proportion was also carried 
out with corresponding WiFS pixels. Sub pixel 
wheat proportion fi'om MOS-B data, when all 
the thirteen bands were used in unmixing, 
showed high correlation (0.82) with proportion 
enumerated from WiFS pixels (Fig 1). 
Fractional images of diffe,'ent landcovers viz. 
wheat, urban, wasteland and water are shown in 
Plate (2). Relative deviation between both 
estimates was 1.72 per cent. As is discussed ill 
the methodology, proportion of each component 
Wheat Crop Inventory Using High Spectral Resolution... 171 
SPECTRAL UNMIXING FROM MOS-B DATA 
120 
@ 
4= 
9 9 00  y 9 9 
I 
0 L=F'A Am m ~ m ~ , AA  ~ ~ ! I I 
0 20 40  60  80  100 
Wheat fraction(%) from MOS-B 
I 
120 
Fig. 1 Comparison of wheat proportions estimated from spectral unmixing of MOS-B data to estimated 
proportions from WiFS data 
in a pixel was obtained using constrained least 
square method. In order to have deterministic 
solution, requirement of bands are such that it 
should either equal to (worst case) or more than 
the components (number of classes) considered. 
Accuracy of estimated proportion of different 
components depends on the number of bands 
considered visa vis number of component used 
in analysis. To study the influence of number of 
bands on sub pixel fraction estimation, 
unmixing result was compared at two sets of 
band combination i.e., all thirteen bands as 
descr ibed above and eight bands 
(4,5,6,7,8,9,12,13) of MOS-B data. There are 
many eight-band combinations that can be 
selected out of full thirteen bands of MOS-B 
data. Optimum band combination procedure was 
used to arrive at the above eight-band 
combination. Details on the optimum band 
combination are discussed in the next 
paragraph. It was found that correlation between 
subpixel wheat proportion estimated froln 
MOS-B data and enumerated WiFS pixels area 
reduced to 0.72 when only above eight bands 
were used in unmixing. Relative deviation 
observed between WiFS and MOS enumeration 
for this combination of bands increased to 26. I 
per cent. This decrease in performance was also 
172 R.P. Singh et al. 
visually verified when false colour composite of 
WiFS was compared with Pseudo colour 
composite generated by giving red colour to 
wheat fractions and green and blue cotours to 
non wheat fractions. 
Selection of optimal bands becomes 
crucial for feature space reduction in analysis or 
deciding the relative importance of spectral 
bands when the large number of bands is to be 
analysed. Although the choice of bands is 
associated with scope of applications and the 
level of classes considered. In the present work, 
very broad classes, viz., wheat, urban, wasteland 
and water bodies which were used in unmixing 
were taken into consideration. The idea was to 
get the best subset of bands (eight) for 
comparison of unmixing analysis. Average 
transformed ivergence (T.D.) criterion was 
used for the determination of best subset of 
bands optimum for discrimination of above 
classes. 
It was observed that five bands 
(4,5,8,12,13 out of all MOS-B bands) are 
sufficient for signature separability (T.D. 1.979) 
of above described land cover classes (Table 2). 
These bands with central location at 520, 570, 
685, 815 and 945 nm cover green, red and NIR 
spectral region. With higher number of bands, 
T.D. values get saturated for these classes. On 
the contrary, performance of unmixing approach 
in sub pixel fractional area estimation decreased 
when the results of analysis carried out using all 
the 13 bands of MOS-B data when compared 
with the results of analysis using eight 
(4,5,6,7,8,9,12,13) MOS-B bands chosen from 
optimal band selection criteria. It can be 
inferred that high separability of the components 
in the signature domain for a given number of 
bands, need not be a sufficient condition of 
efficient unmixing. 
Conclusion 
Feasibility study was carried out for 
assessment of wheat crop over the parts of 
northern India using IRS-P3 MOS-B data. It 
showed encouraging results for its use in crop 
monitoring in homogeneous conditions at 
regional scale. High spectral resolution MOS-B 
spectrometer data provided an opportunity for 
the assessment of vigour of wheat crop using 
red edge technique from space platform. 
Attempt was made to overcome the limitation of 
coarse spatial resolution by taking advantage of 
large number of bands (13) of narrow spectral 
resolution for spectral unmixing. Wheat area 
estimated from MOS-B data using unmixing 
approach showed good correlation (0.82) when 
it was compared with the corresponding area 
estimated using WiFS data. Though MOS-B 
spectrometer is originally designed for oceanic 
applications, still provided an opprtunity to 
Table 2: Selection of optimal bands for land covers discrimination 
No. of optimal 
bands 
2 
3 
4 
6 
A verage 1".19. 
1.811 
MOS-B Bands and its central wavelength in 
(rim) 
5(570), 12(815) 
1.936 5(570), 8(685), 12(815) 
1.959 5(570), 8(685), 12(815), 13(945) 
1.979 4(520), 5(570), 8(685), 12(815), 13(945) 
1.980 4(520), 5(570), 7(650), 8(685), 12(815), 13(945) 
Wheat Crop Inventory Using High Spectral Resolution... 173 
effectively handle such type of  high spectral 
resolution data for agricultural applications. 
Techniques used can further be utilized in future 
for the analysis of data from MODIS and 
MERIS sensors. 
Acknowledgments 
We are thankful to Shri J.S. Parihar Group 
Director, Agricultural Resources Group for his 
keen interest and continuous encouragement in 
carrying out this work. We are also grateful to 
Dr. Markand Oza for his critical comments and 
help in Image Processing. 
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Wheat crop inventory using high spectral resolution IRS-P3 MOS-B spectrometer data

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Wheat crop inventory using high spectral resolution IRS-P3 MOS-B spectrometer data

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