Estimation of wheat yield by satellite imageries Landsat 8

Document Type : Research Paper

Authors

1 Graduated M.Sc. of Irrigation and Drainage, Faculty of Engineering and technology, Imam Khomeini International University, Qazvin, Iran

2 Assistant Professor, Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran

3 Professor, Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Abstract

Remote sensing can be used to provide agricultural land use map and to estimate crop cultivated area and crop yield. The purpose of this study was to estimate wheat yield by satellite imageries in Moghan Agro-Industry which is one of the important centers of agricultural production in Iran. Wheat is one of the strategical crops. Field data were collected from Moghan Agro-Industry during 2013 to 2015. Landsat 8 images which were coincided to field observation were collected to determine eight vegetation indices. Landsat-8 with high spatial resolution and ease facility images has presented acceptable results. The obtained results showed that NDVI parameter  with R2=0.71 and RMSE, MAE and MBE  equal to 797, 637 and 87 kg per hectare, respectively, has a higher precision for crop yield prediction as compared to other vegetation indexes. Thus, wheat yield could be predicted by Landsat 8 imageries before harvesting time in Moghan plain. This can help Moghan Agro-Industry, water resources, food providers, agricultural insurance, and transportation managers to manage their decisions before time is going.

Keywords

Main Subjects


Aboelghar, M., Arafat ,S., Abo Yousef ,M., El-Shirbeny, M., Naeem, S., Massoud, A., Saleh, N. (2011). Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta. The Egyptian Journal of Remote Sensing and Space Sciences (2011) 14, 81–89.
Alavi-panah, S.K., Matinfar, H.R., and Rafiei emam, A. (2008). Application of Information Technology in Earth Sciences. Tehran University press. 472 p. (in farsi)
Asrar, G.M., Fuchs, E.T., and Kanemas.(1984). Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agr. J. 76: 300-306.
Balaghi,R., Tychon, B., Eerens, H., Jlibene, M., (2008).Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation 10 (2008) 438–452.
Bao, Y., Gao, W., and Gao, Z. (2009). Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions. Earth Sci. 3(1):118–128.
Darvish zadeh, R., Motakan, A.A., and Eskandari, N. (2011). Evaluation of ALOS-AVNIR-2 spectral indices for prediction of rice biomass. Journal. Geograph. Land. 14:61-73. (in farsi).
Huang, L., Yang, Q., Liang, D., Dong, Y., Xu, X., and Huang, W. (2012). The Estimation of Winter Wheat Yield Base on MODIS Remote Sensing Data. International Federation for Information Processing. pp. 496–503.
Mohammadi, E,. Kamkar, B,.and Abdi, O. (2015). Comparison of geostatistical- and remote sensing data-based methods in wheat yield predication in some of growing stages (A case study: Nemooneh filed, Golestan province).Electronic journal of crop production.Vol. 8(2) Page 51-76
Ren,J., Chen, Z., Zhou, Q., Tang, H.(2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China.International Journal of Applied Earth Observation and Geoinformation 10 (2008) 403–41.
Sanaeinejad, H., Shah Tahmasebi, A.R., Sadr Abadi Haghighi, R., and Kelarestai, K. (2008). A study of spectral reflection on wheat fields in Mashhad using MODIS data. J.f Sci. Technol. Agri. Nat. Res. 45:11-19. (in farsi).
Sánchez, N., González, R., Prado, J., Martínez-Fernández, J., and Pérez-Gutiérrez, C. (2006). Estimating vegetation parameters of cereals using an Aster 1A Image. Commission VII, WGVII/1, Spain.
Sawasawa, H.L.A, (2003). Crop yield estimation: integration RS, GIS and management factors. ITC, International Institute for Geo-information science and earth observation enschede, The Netherlands.
Singh, R.(2003). Crop Yield Estimation And Forecastion Using remote Sensing Indian. Agricultural statistics reseearch. institute, new delhi-11012.
Weiss, J.L., Gutzler, D.S., Allred Coonrod, J.E., and Dahm, C.N. (2004). Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico. USA. J. Arid Environ. 58: 248–271.
Fatemi, B., and Rezaei, Y. (2006). Basic of Remote Sensing. Azade publication. 257 p. (in farsi)
Franch, B., Vermote, E.F., Becker-Reshef, I.,Claverie, M., Huang, J., Zhang, J., Justice, C., Sobrino, J.A. (2015). Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China usingMODIS data and NCAR Growing Degree Day information. Remote Sensing of Environment 161 (2015) 131–148.
Hu, Sh., Mo, X. (2001). Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecological Modelling 222 (2011).PP 2530– 2541
Jiang, J., Suozhong, C., Shunxian, C.A.O., Hongan, W.U., Li, Z., and Hailong, Z. (2005). Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China. Journal. Geograph. Sci. 15: 247-254.
Kanooni,A. (2005). Evaluation of Furrow Irrigation Efficiency under Different Management in Moghan Region.Journal of Agricultural Engineering Research.8(2).17-32(in farsi)
Kazemi Poshtmasari, H., Tahmasebi Sarvestani, Z., Kamkar, B., Shataei, Sh., and sadeghi, S. (2012). Evaluation of geostatistical methods for estimating and zoning of macronutrients in agricultural lands of Golestan province. Water Soil Sci. 22(1): 201-218. (in farsi)
Liu, R., Chen, J.M., Liua, F., Deng, R., and Sunk. D. (2007). Application of a new leaf area index algorithm to China’s land mass using MODIS data for carbon cycle Research. J. Environ. Management. 85: 649–658.