We determined the accuracy and reliability of yielding models by using the values of two differently obtained indexes – the leaf area index (LAI) obtained through direct surface measurements, and the normalized difference vegetation index (NDVI) obtained through spatial remote sensing of crops. The study based on the drip-irrigated sweet corn yielding data obtained in the field experiment held in the semi-arid climate on dark-chestnut soil in the South of Ukraine. Suitability of the LAI and NDVI for simulation of sweet corn yields was estimated by the regression analysis of the yielding data by correlation (R) and determination (R2) coefficients. Besides, mathematical models for the crop yields estimation based on the regression analysis were developed. It was determined that the LAI is more suitable index for the crop yield prediction: the R2 value was 0.92 and 0.94 against 0.85 for the NDVI-based models. Besides, it was determined that it is better to use the LAI values obtained at the stage of flowering, when R2 averaged to 0.94, and the NDVI-based models does not depend on the crop’s stage (the R2 was 0.85 both for the flowering and ripening stages of the plant development). The combined NDVI-LAI model showed that there is no necessity in complication of the LAI-based model through introduction of the remotely sensed index because of insignificant improvement in performance (R2 was 0.94 and 0.92).