Estimation of Wheat Yield on a Farm in Najaf/Iraq, using Principal Component Analysis of Multi Temporal Satellite Images Pairs

Abstract

Abstract The present work aims to estimate the yield of wheat crop for a particular wheat farm in Najaf Governorate, southwestern Iraq. Seven multispectral bands of the Landsat satellite (8 and 9) were used for two different time periods. The satellite image for the 1st time (Landsat 8) represents the high vegetative growth of the wheat crop, and the satellite image for the second time (Landsat 9) represents the farm after harvest. Changes in land use were detected based on Principal Component Analysis (PCA) technology wherein PCA images were used to calculate wheat production for a specific area in Najaf scene in Iraq. RGB color model was adopted as an unsupervised method of scene classification as this model was used to determine the number of classes in a scene. Maximum-likelihood method was applied as supervised classification with the images generated by applying the principal component analysis technique for the 1st time and for the 2nd time separately considering the number of classes derived from the RGB color model. The data was read within the borders of the region of interest (a wheat farm) for which the yield will be calculated by creating a mask in which the outer borders of the study area are defined. The results showed that the rate of wheat production for the study area amounted to 97.85 tons, with an error rate that did not exceed (1.55%) when using PC images.