A non-linear regression form for vegetation index-crop yield relation incorporating acquisition date normalization

A non-linear form relating vegetation indices (VI) to crop grain yields which normalizes for differences in acquisition date is suggested. It is based on the assumption that deviations in VI near the peak VI follow a quadratic behaviour. This form gave a higher R(2) value than a simple VI-yield linear model on a multi-year, multi-location data set of IRS (Indian Remote Sensing Satellite-1A) LISS-I(Linear Imaging Self Scanner-I) derived near-infrared (NIR)/red radiance ratios and wheat grain yields in a study site in Madhya Pradesh (India). As the suggested model includes time of peak as a variable, it allows integration of results from other sources, such as, weather-based crop phenology model or high repetivity spectral data into the VI-yield relation.