ASSESSING GROWTH AND YIELD OF WHEAT USING REMOTELY-SENSED CANOPY TEMPERATURE AND SPECTRAL INDEXES

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.