SPECTRO-METEOROLOGICAL MODELING OF SORGHUM YIELD USING SINGLE DATE IRS LISS-I AND RAINFALL DISTRIBUTION DATA

The yield of grain Sorghum cultivated in dry-land regions in India flu ctuates widely in relation to its critical growth phases depending on the weather conditions. Vegetation indices derived form remote sensing data acquired at the time of maximum vegetative growth are indicative of crop growth and vigour and consequent potential grain yields. In t his paper we investigate rabi (winter) sorghum yields using Indian Rem ote Sensing Satellite's Linear Imaging and Self Scanning-I (IRS LISS-I ) sensor data and monthly rainfall distribution data of the recent fou r seasons for the 37 tehsils (sub-units of districts) that constitute the three principal sorghum producing districts of the central Maharas htra state (India). The multiple linear regression yield models with b oth the spectral and spectro-meteorological parameters have been devel oped for tehsil, as well as the district yields, by identifying critic al parameters with model estimation errors of about 22 per cent on teh sil yields and about 5 per cent on district yields. The yields are fou nd to be correlated significantly with monsoon rainfall about 1 to 2 m onths before sowing. This study brings out the problems of yield model ling of the semi-arid tropical crop in a small region using remote sen sing data only, and shows that the vegetation indices deduced from rem ote sensing data are found to be good indicators of the yield on large spatial scales, as the effects of varying rainfall on yields largely cancel out.