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RELATION OF CROP CANOPY VARIABLES TO THE 
MULTISPECTRAL REFLECTANCE OF SMALL GRAINS 
J. S. Ahlrichs, M. E. Bauer, M. M. Hixson, 
C. S. T. Daughtry, and D. W. Crecelius 
Purdue University 
Laboratory for Applications of Remoté Sensing 
West Lafayette, Indiana U.S.A. 
Abstract 
Reflectance spectra over the wavelength range 0.4-2.5 um were 
ired during each of the major development stages of spring wheat 
canopies. Treatments in the experiment included planting date, 
nitrogen fertilization, cultivar, and soil moisture. Agronomic 
characterization of the wheat canopies included measurements of 
maturity stage, plant height, fresh and dry biomass, leaf area index, 
and percent soil cover. Analysis of variance, correlation, and 
regression analyses were used to relate the agronomic variables to 
reflectance. 
acqu 
hips were found between reflectance and percent 
Strong relations 
water content. A 
soil cover, leaf area index, biomass, and plant 
middle infrared wavelength band, 2.08-2.35 um, was most important in 
explaining variation in fresh and dry biomass and plant water content, 
while a near infrared band, 0.76-0.90 yum, explained the most variation 
in percent soil cover and leaf area index. The relationship of canopy 
variables to reflectance, however, is influenced by the maturity stage 
of the crop and decreases as the crop begins to ripen. The canopy 
variables could be accurately predicted using measurements from three 
to five wavelength bands. The reflective wavelength bands proposed 
for the thematic mapper sensor were more strongly related to and better 
predictors of the canopy variables than the Landsat MSS bands. 
Introduction 
Crop identification and area estimation promises to be one of the 
major applications of remote sensing and the Large Area Crop Inventory 
Experiment (LACIE) has pushed the technology to near operational use 
for wheat (MacDonald et al., 1978). Remote sensing also offers great 
potential for obtaining accurate and timely information about the 
condition and yield of crops (Bauer, 1975). 
To fully realize the potential of remote sensing for crop identi- 
fication, condition assessment, and yield prediction it is important 
to understand and quantify the relation of agronomic characteristics