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The goal of this research was to investigate the feasibility of utilizing remotely sensed aerial images in agricultural crop management. Green canopy has very specific reflectance characteristics distinguishing it from other materials such as soil and dry vegetative matter. Reflectance values in red (R) and near infrared (NIR) spectral bands have been widely used for calculating Normalized Difference Vegetation Index (NDVI). Many researchers have related NDVI values to plant vigor, water stress, Leaf Area Index (LAI), or yield. However, vegetation indices such as NDVI are often sensitive to background reflectance characteristics. The relationship between the processing tomato yield and Soil Adjusted Vegetation Index (SAVI) based on the R and NIR reflectance has been investigated in this study. Eight three-band (R, NIR and green) aerial images were obtained at approximately two-week intervals during the 2000 processing tomato growing season. These images were analyzed to obtain SAVI values, which were in turn related to LAI using regression techniques. The LAI values were numerically integrated over the whole growing season to obtain Cumulative Leaf Area Index Days (CLAID) and obtain CLAID variability map. The CLAID variability map showed similar pattern as the yield map obtained in the same field.