Dk. Das et al., ASSESSING GROWTH AND YIELD OF WHEAT USING REMOTELY-SENSED CANOPY TEMPERATURE AND SPECTRAL INDEXES, International journal of remote sensing, 14(17), 1993, pp. 3081-3092
Prediction models were developed for wheat to assess crop growth in te
rms of leaf area index, dry matter production and grain yield from rem
otely-sensed temperature and spectral indices. The cumulative stress d
egree days (SDD) for the period of flowering to grain formation stage
showed significantly higher correlation with dry matter (r= -0.940) an
d grain yield (r= -0.939) whereas that, for the period grain formation
to harvest stage, showed significantly higher correlation (r= -0.967)
for crop water use. Significant and positive correlations between dry
matter, leaf area and grain yield with infrared/red, normalised diffe
rence (ND), transformed vegetation index and greenness index were atta
ined with the latter providing the highest degree of predictability. S
pectral indices measured between flowering to milking stages gave the
best prediction indicating the suitability of this period for crop gro
wth assessment by this technique. Inter-stage sensitivity analysis by
using multiple regression approach also revealed that greenness and tr
ansformed vegetation indices could provide better prediction of dry ma
tter and grain yield. From the values of regression coefficients the j
ointing to beginning of milk formation period of the crop was found to
be the most sensitive stage influencing the yield of crop.