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

Document Type : Research Paper

Authors

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

Abstract

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 http://earthexplorer.usgs.gov. 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.

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  1. Ahmadi, J., Khatibi, M., Amirshekari, H., and Amini Dehagi, M. 2011. Evaluation of the effective morpho-physiological indices on the yield of spring wheat (Triticum aestivum L.) using multivariate statistical methods. Agron. J. 4: 55-66. (In Persian).
  2. Alavipanah, K. 2006. Thermal remote sensing and its application in earth sciences. Tehran University Press. 524p. (In Persian).
  3. 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.
  4. Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., and Royo, C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agro. J. 92: 1. 83-91.
  5. Asrar, G., Fuchs, M., Kanemasu, E., and Hatfield, J. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76: 300-306.
  6. Bannari, A., Morin, D., Bonn, F., and Huete, A. 1995. A review of vegetation indices. Remote Sens. Reviews. 13: 95- 120.
  7. Baret, F., and Guyot, G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 35: 161-173.
  8. Bausch, W.C., Halvorson, A.D., and Cipra, J. 2008. Quick bird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots. Biosyst Eng. IOI. 306-315.
  9. Campbell, J.B. 2011. Introduction to Remote Sensing. Guilford Press. 667p.
  10. Gitelson, A.A. 1996. Remote estimation of leaf area index and green leaf biomass in maize canopies. J. Plant Physiol. 161: 165-173.
  11. Goel, N.S., and Qin, W. 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulation. Remote Sens. Reviews, 10: 309- 347.
  12. Hoffmann, C., and Blomberg, M. 2004. Estimation of leaf area index of Beta vulgaris L. based on optical remote sensing data. J. Agro. Crop Sci. 190: 197-204.
  13. Huete, A.R. 1988. A soil adjusted vegetation index (SAVI). Remote Sens. Environ. 25: 295-309.
  14. Huete, A.R., Liu, H., Batchily, K., and Leeuwen, W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59: 3. 440-451.
  15. Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yan, G., and Zhang, X. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101: 366-378.
  16. Jiang, Z., Huete, A.R., Didan, K., and Miura, T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ.112: 3833-3845.
  17. Jimen´ez-Mu˜noz, J.C., Sobrino, J.A., Plaza, A., Guanter, L., Moreno, J., and Martinez, P. 2009. Comparison between fractional vegetation cover retrievals from vegetation indices and mixture analysis: case study of PROBA/CHRIS data over an Agricultural area. Sensors. 9: 768-793.
  18. Jordan, C.F. 1969. Derivation of leaf area index from quality of light on forest floor. Ecol. J. 50: 663-666.
  19. Kamali, G.A., Momenzadeh, H., and Vazifeh Doust, M. 2011. Study of wheat yield production over Esfahan province during periods of dry and wet years using MODIS satellite data. J. Agro. 2: 181-190. (In Persian).
  20. Li-Hong, X., Wei-Xing, C., and Lin-Zhang, L. 2007. Predicting grain yield and protein content in winter wheat at different supply levels using canopy reflectance spectra. Published by Elsevier Limited Science Press. 17: 5. 646-653.
  21. Lopresti, M.F., Di Bella, C.M., and Degioanni, A.J. 2015. Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina. Inform. Process. Agri. 2: 73-84.         
  22. Mohammadi Ahmad Mahmoudi, E., Kamkar, B., and Abdi, O. 2015. Comparison land- statistic methods and the use of remote sensing data to predict wheat yield in some stage of growth (Case study: field lands of Golestan province). J. Crop Prod. 8: 2. 51-76. (In Persian).
  23. Mosleh Ghahfarokhi, Z. 2016. Soil digital mapping, land suitability and optimization cultivation model for major products plains of Shahrekord. PhD thesis pedology, Faculty of Agriculture, University of Shahrekord, Iran. (In Persian)
  24. Pinter, P., Jackson, R., Idso, S., and Reginato, R. 1981. Multidate spectral reflectance as predictors of yield in water stressed wheat and barley. Int. J. Remote Sens. 2: 1. 43-48.  
  25. Raun, W.R., Solie, J.B., Stone, M.L., Lukina, E.V., Thomason, W.E., and Schepers, J.S. 2001. In season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93: 131-138.
  26. Rondeaux, G., Steven, M. and Baret, F. 1996. Optimization of soil- adjusted vegetation indices. Remote Sens. Environ. 55: 98-107.
  27. Roujean, J.L., and Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 51: 375-384.
  28. Rouse, J.W. 1974. Monitoring the vernal advancement of retrogration of natural vegetation. NASA/GSFS Type III, Final Report, Greenbelt, MD.
  29. Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N., and Ghaemi, M. 2014. Wheat yield estimation using landsat images and field observation: A case study in Mashhad. J. Plant Prod. 20: 4. 45-63. (In Persian).
  30. Serrano, L., Filella, I., and Penuelas, J. 2000. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci. 40: 3. 723-731.  
  31. 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. Agron. J. 93: 583-589.
  32. Siyal, A.A., Dempewolf, J., and Becker-Reshef, I. 2015. Rice yield estimation using Landsat ETM+ Data. J. Appl. Remote Sens. 9: 1-16.
  33. Solaimani, K., Shokrian, F., Tamartash, R., and Banihashemi, M. 2011. Landsat ETM+ based assessment of vegetation indices in highland environment. J. Adv. Res. Dev. 2: 1. 5-13.
  34. Tucker, C.J., Holben, B.N., Elgin, Jr, J. H., and McMurtrey, J.E. 1981. Remote sensing of total dry -matter accumulation in winter wheat. Remote Sens. Environ. 11: 171-189.
  35. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127-150.
  36. Tucker, C.J., Holben, B.N., Elgin Jr, J.H., and McMurtrey III, J.E. 1980. Relationship of spectral data to grain yield variation. Photogramm. Photogramm. Eng. Rem S. 46: 657-666.
  37. Webster, R. and Oliver, M.A. 1990. Statistical methods in soil and land resource survey. Spatial Information Systems, Oxford University Press, Oxford.
  38. Wiegand, C.L., Richardson, A.J., Escobar, D.E. and Gerbermann, A.H. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 35: 105-119.
  39. Zahirnia, A.R. and Matinfar, H.R. 2016. Evaluate the yield of irrigated wheat fields on the basis of data obtained from Landsat 8 in the southwestern province of Khuzestan. First National Conference on Remote Sensing and GIS in the earth sciences. Atmospheric and Oceanic Sciences Research Center-in. College of Agriculture, Shiraz University. (In Persian).
  40. Zanter, K. 2015. Landsat8 (L8) data users handbook. Department of the Interior U.S. Geological
  41. Zeinvand, M., Matinfar, H.R. and Zahirnia, A.R. 2016. Alfalfa yield estimation based on data obtained from Landsat 8, in plain Jaydar Poldokhtar of Lorestan province. First National Conference on Remote Sensing and GIS in the earth sciences. Atmospheric and Oceanic Science Research Center-in. College of Agriculture, Shiraz University. (In Persian).