Discrimination of winter crops using temporal spectral profiles

The possibility of discriminating three winter crops namely wheat (Triticum aestivum linn, emend Thell), Chickpea (Cicer arietinum L.) and mustard (Brasica compestris), on the basis of their temporal spectral profile, has been studied. The profile for each crop has a characteristic overall form showing different profile duration, amplitude, rise and fall time. The profile can be used to discriminate spectrally the three Indian winter crops which are grown in the same season.     

Assessment of water-stress effects on crops

The present paper describes the results of a ground experiment conducted on major crops of India during December 1980 to April 1981 and October 1981 to April 1982. The crops selected were wheat, chickpea and mustard. The experiment was jointly conducted between the Indian Space Research Organization (ISRO) and the Indian Agricultural Research Institute. Canopy spectral reflectance and physiological variables were measured over the complete crop cycle. The results of the 2 year experiment led to the following conclusions: (i) spectral parameters can be used for detecting water and nutrient stress in crops, (ii) spectral parameters are highly correlated to physiological variables and final production, (iii) correlation is further improved by including the effect of weather parameters, (iv) stress degree day is highly correlated to grain yield and (v) a yield model based on photosynthesis calculation worked well for the wheat experiment.     

Cotton Production Estimation using IRS-IB and Meteorological Data

An approach to estimate cotton production at district level by estimating cotton acreage from IRS-IB multispectral data and predicting yield from meteorological data has been described. The meteorological data (fortnightly rainfall and climatic potential evapotranspiration) have been used to estimate fortnightly actual evapotranspiration (AET) using simplified soil moisture budgeting technique. Fortnightly AET values have been stepwise regressed with yield to get the final model which is used for yield prediction. Cotton acreage, yield and production have been estimated for the year 1992-93.