In Iran, Yield forecasting is important for determining import–export policies, government aid for farmers, and allocation of subsidies for regional agricultural programs. Crop models have been used for monitoring crop growth and predicting yield. This research was curried in the lands under cultivation of dry-land Wheat in Malayer region in order to create an experimental regression model between the amount of yield or product and vegetation index. Measuring the coordinates of 150 points of the wheat sample with maximum amount of accuracy by GPS when the dry-land wheat of the region was ripen completely. The layers of gLAI and NDVI were crossed together in the context of ILWIS software in order to extract the amounts of NDVI corresponding with gLAI. The approach of determining LAI by establishing a relationship between NDVI and LAI is widely used due to its simplicity and ease of computation. In this case study a single date images, as demonstrated in this study, still provides good information to predict middle of season yield as long as it is within time when there is maximum vegetation between panicle initiation and heading stage. This research showed that NDVI has a good correlation with LAI and there is a good correlation between NDVI and yield but using NDVI as end-of- season yield estimator gives unsatisfactory results because of the problems of choosing the best time of the image to use, vegetation indices calculated from images taken at panicle initiation and heading stages have high correlation with yield too. Although simulation error was increased due to sLAI was used instead of gLAI (n = 30 & n = 120) is 0.36 and 0.55 %, respectively, but this amount equals less than one percent. Moreover, it is evident that there would not be errors when calculating in the farming planning in the region.
