Identification and Determination of Wheat Cultivated Farms Using Vegetation Index Reflectance Changes and Spatial Analysis in Western of Iran

Document Type : Research Paper


1 Faculty of climatology, Faculty of Humanities, Zanjan University, Zanjan, Iran

2 Faculty of Meteorology, Faculty of Humanities, Zanjan University, Zanjan, Iran

3 Department of Meteorology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran


   Mapping and spatial analysis of wheat fields are very important in studying macroeconomic and social issues, including agricultural management. Highly variable crop pattern maps and its preparation using terrestrial data are associated with many problems. The purpose of this study is to implement a practical method for extracting wheat fields by using changes in vegetation index and spatial analysis of wheat fields in western of Iran. Investigation of the changes curve of vegetation index of wheat typic farms showed that the highest amount of reflection index of wheat farms is in June and early July and after harvest the reflectance index decreases extremely. In this regard, Sentile sensor data was processed in the Earth Engine system and the 12-month vegetation index of 1398 was extracted as a data set. By introducing training data to the data set created by the support vector machine classification method, the land use of the study area was obtained in five classes. By applying altitude filter and removing the extracted fields above 3,000 meters, the distribution map of wheat fields was verified with the remaining 48 ground data. The total accuracy and the kappa coefficient were obtained 0.86 and 0.79, respectively. Since in the proposed method, more training data are given to the algorithm, the overall accuracy of the classification is increased. The spatial pattern of wheat fields with the mean function of the nearest neighbor and P_value <0.05 indicating the cluster dispersion of the fields and the Caripley function indicating the non-random scattering of wheat fields up to distances of 21,000 meters. The results of this research and its output maps can be used to obtain information for agricultural planning as well as the allocation and spatial distribution of resources and facilities.


Abrifam, M. (2001). The Synoptic Analysis of Entranced Air Masses to the West of Iran (2004-2005), Supervisor: Gholamreza Barati, Master of Science in Climatology. Razi University of Kermanshah.
Adamowski, J., and Chan, H. F .(2011) A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(4): 28-40.
Ahmadpour, Z., Ghanbari, Q., and Karami, Q. (2014). Political organization of space.Tehran: Geographical Organization Publications Armed Forces. Third edition. 113. (In Farsi)
Alipour, F., Aq Khani Mohammad, H., Pourfard, A., Mohammadi, H., and Sepehr, A. (2016). Separation of the area and estimation of agricultural crops with satellite images. Agricultural machines,4: 244-254. (In Farsi)
Al-Gaadi, K.A., Hassaballa, A.A., Tola, E., Kayad, A.G., Madugundu, R., Alblewi, B., and Assiri, F. (2016). Prediction of potato crop yield using Precision agriculture techniques. PLoS One, 11,9:1-16.
Alijani, B., (2012). Synoptic climatology. Tehran: Samt Publications. (In Farsi)
Alijani, B., (2015). Spatial Analysis. Journal of Spatial Analysis of Environmental Hazards, 2(3): 1-14. (In Farsi)
Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L. and Royo, C. (2000). ining durum wheat yield. Agronomic Jurnal, 92(1): 83-91.
Alizadeh, P., Kamkar, B., Shatai, SH., and Kazemi. H. (2018). Estimation of changes in wheat and soybean cultivation using satellite image classification in the west of Golestan province. Applied agricultural research, 31(3): 41-61. (In Persian)
Ashourloo, M., Alimohammadi, A., Rezaian, P. and Ashourloo, D. (2006). Separation of wheat from other products on satellite images. Environmental Sciences, 4(2): 101-116. (In Farsi)
Caren, D., David, M., and C. R. Volker. (2001). Phonological difference in tasseled cap indices Improves deciduous forest classification. Remotesensing of Environment, 80: 460-472.
Chen, Y., Zhang, Z., and Tao, F. (2018). Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data. European Journal of Agronomy, 101: 163-173.
Collins, W. (1978). Remote sensing of crop type and maturity. Photogrammetric Eng. and Remote Senseing, 44, 43-55.
FAO. 2017. FAOSTAT Database. Available online at:
Foody, G. M., Mathur, A., Sanchez-Hernandez, C., and Boyd, D. (2006). Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104(1): 1-14.
Ganji, M. H. (2003). Climatic faults of Iran. Bulletin of the National Center for Climatology, 3(1): 41. (In Farsi)
Gualtieri, J. A., Chettri, S. R., Cromp, R. F. and Johnson, L. F. (1999). Support vector machine classifiers as applied to AVIRIS data.
Harvey, D. (1996). Explanation in Geography. London: Arnold.
Hatfield, J., Prueger, J. (2010). Value of using different vegetative Indices to quantify agricultural crop characteristics at different growth stages under varying management Practices, Remote Sensing, 2: 562–578.
Hong, X., CAO Wei-Xing1, C., and YANG Lin-Zhang, Y. (2007). Predicting Grain Yield and Protein Content in Winter Wheat at Different N Supply Levels Using Canopy Reflectance Spectra. Pedosphere, 17(5): 646–653.
Kaswani, I., Norsaliza, U. and Hasmadi, I. (2010). Analysis of spectral vegetation indices related to soil-line for mapping mangrove forests using satellite imagery. J. Rem. Sens, 1(1): 25-31.
Khodakarami, L. and Sefyaninan, A. (2012). Application of multi-time remote sensing in determining the area under cultivation. Soil and Water Sciences, 16(59): 215-231. (In Farsi)
Mojarad, F., and Masoompour, J. (2013). Estimation of maximum probable precipitation by synoptic method in Kermanshah province. Geographical studies of arid regions, 13: 1-14.
Pattanaik, F., and Mohanty, S. (2017). Changes in Cropping Pattern in Odisha Agriculture in Neo-Liberal Period. Journal of Rural Development, 36(1): 121-154.
Richards, J. (2013). Remote Sensing Digital Image Analysis.  Springer Berlin Heidelberg   DOI:
Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.M., Schlemmer, M.R. and Major, D.J. (2001). Use of remote-sensing imagery to estimate corn grain yield. Agronomic Jurnalm 93: 583-589.
Wang, L., Zhoub, X., Zhua, X., Donga, Z., Guo, W. (2016). Estimation of biomass in wheat using random forest. Regression algorithm and remote sensing data, t h e c r o p j o u r n a l, 4: 2 1 2 – 2 1 9.
Wardlow, B.D., Egbert, S.L., Kastens, J.H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ, 108 (3): 290–310.
Wit, D., Duveiller, A., and G., Defourny, G. (2012). Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations. Agricalture for Meteorological, 164: 39–52.
Zadehdifard, N. (2002). Preparation of land use map using satellite data in Baft drainage basin. MSc Disseration, Faculty of Agriculture, Isfahan University of Technology, Iran. (In Farsi)
Ziaeian Firoozabadi, P. Sayad Bidhendi, L. and Eskandari Nodeh, M. (2009). Rice cultivation in Sari city using RADARSAT satellite images. Natural Geography, 41(68): 45-58. (In Farsi)