Modeling wheat yield estimation base upon spectral data and field measurement, Case study: Razan plain, IRAN

Wheat is one of the most important strategic products, including cereals and food security of the country. Thus, estimation the yield of wheat in the country is necessary and important . LAI is One of the key indicators of development biophysics canopy. Leaf area index, leaf area per unit ground area is defined, therefore, by definition, LAI ground models are used to determine the ecological processes. Remote sensing estimates of LAI based largely on empirical relationships between LAI and spectral response was observed when the ground is measured by the sensor depends. NDVI is a good indicator, as a measure of the efficiency of the plant is used. But in areas where coverage is one hundred percent coverage offers more accurate estimation of SAVI index. The aim of this study was to evaluate the ability of crops to provide satellite data to evaluate LAI measured and calculated from the images, measure the performance of each of the fields selected in the relationship between leaf area index and grain yield and yield models based on the spectral data of Landsat ETM+. The results show that the spectral data (satellite) in the growing season and the harvest is the most it can be used to estimate the performance to an acceptable accuracy. Also, due to the fact that the data on the yield estimate of 30 × 30 pixels, variation in the yield estimates are presented. The results show that the actual performance and the performance of the model, the estimated coefficient is 0.74, which indicates an acceptable accuracy of the model is to estimate crop yield.