Estimation of irrigated wheat yield (Triticum aestivum L.) using data of remote sensing data (Case study in Shahrekord County)

Document Type : Research Paper


1 Student/Sharekord university

2 Pedology Department, Faculty of Agriculture, Shahrekord University, Shahrekord, IRAN.

3 Associate Professor of Agronomy, Agronomy Department, Faculty of Agriculture, Shahrekord University, Shahrekord, IRAN


Background and objectives: In order to improve food security as one of the most crucial needs of the society, prediction and estimation of wheat yield should be considered by decision makers in the country. On the other hand, agricultural production is always at risk of climate and international markets changes, however, this risk is never completely eliminated, but we can estimate the yield before the harvest season to minimize them. Nowadays, one of the methods of yield estimating is using satellite- imagery. Remote sensing data allow estimating crop yield based on vegetation indices. The present study aimed to find a fast way with acceptable accuracy for predicting wheat yield in the field in Shahrekord County, Chaharmahal and Bakhtiari province, Iran by using Landsat 8 data.
Materials and Methods: In order to fulfill the goal three sets of Landsat 8 imagery data dated on June 4th, 20th and July 6th 2016 were downloaded from The acquisition dates were corresponded with milk, dough and ripening stages of wheat growth cycle. Concurrently three wheat cultivated farms were selected in the Shahrekord County, based upon surface area, homogeneity in wheat cultivated farms and satisfaction of landlords with sampling. Coordinates of samples were recorded using GPS device (Garmin etrex). In the whole, the yield of 60 plots (0.25 m2) from selected fields were used for yield prediction. The density of plants per plots was considered and the yield in each plot was estimated. Nine introduced vegetation indices by literature were considered and the correlation coefficients between indices' valued and estimated yields were calculated. The models were evaluated using the coefficient of determination (R2) and standard error of estimation (SEE), reduced variance (RV) and mean estimation error (MEE). Image processing and statistical analysis were carried out using ILWIS 3.3 and Sigma Plot 10.0 software, respectively.
Results: The results showed that among the imagery data, the highest correlation coefficients existed between wheat yield and vegetation indices developed by images dated on 20th June 2016 corresponded to wheat dough stage. The coefficients of determinations (R2) of models with vegetation indices NDVI, NRVI, OSAVI, RVI, SAVI, RDVI, DVI, EVI and GNDVI were 0.86, 0.86, 0.86, 0.86, 0.86, 0.83, 0.81, 0.80, 0.78, respectively. The results indicated that the most appropriate models were Polynomial and quadratic. The results also showed the potential of satellite images for yield prediction at pre-harvest stage with an accuracy above 80 percents.
Conclusion: According to this study appropriate utilizing of satellite images and field observation at dough stage aided yield estimation of wheat in semi-arid regions. The most appropriate indices for yield estimation were NDVI, NRVI and OSAVI. Among the statistical models, polynomial and quadratic models with the highest coefficients of determination were introduced as the best models. Based upon the results Landsat 8 data and field samples could be used for wheat yield forecasting which help decision makers managing the market for this strategic crop. The results should be tested in other similar climates.


Main Subjects

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