Evaluation of Vegetation Indices for Sugarcane Yield Modeling with Emphasis on Growth Pattern Based on Satellite Imagery: (Case Study: Khouzestan Imam Khomeini Agro Industry)

Document Type : Research Paper


1 Ph.D. Student of Agricultural Mechanization, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran

3 .Professor, Department of Science and Soil Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran

4 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran


The aim of this study is to determine the growth pattern and to investigate the vegetation indices power for sugarcane yield modelling at field scale in Imam Khomeini Agro-industry. For this purpose, the vegetation indices extracted from Landsat7 satellite images were investigated using time series analysis. Overall, 306 Landsat7 satellite images from March 2004 to February 2017 were used. All of the images were converted to surface reflectance via FLAASH algorithm. The average values of 13 vegetation indices related to the study region extracted from satellite images and converted to seven days' time-series via interpolation. In order to eliminate the noise, all series were reconstructed using the Savitzky-Golay algorithm. Thus, 13 different time series of vegetation indices were made for 523 sugarcane fields. Then the growth pattern was drawn via averaging NDVI time series and it was divided into three growth periods. Then the accumulative values of vegetation indices related to the first and second periods of growth stage were extracted since 2004 to 2017. Therefore, 3286 samples were prepared overall, of which 2628 samples were used for modelling and 658 samples for evaluation. The samples extracted from time series were evaluated by simple linear regression model against the average observed yields. The result showed that the accumulative vegetation index of GNDVI for the first growth period with R2=0.47, RMSE=11.70 ton/ha and the accumulative vegetation index of NDI for the second growth period with R2=0.56, RMSE=10.62 ton/ha are a better indeces for sugarcane yield estimation as compared to the other vegetation indices. Also, the sum of GNDVI and NDI indeces for summation of first and second growth periods had a better result (R2=0.65, RMSE=9.47 ton/ha) than that's where one index at one period was used. Finally, the sugarcane yield of 658 samples was estimated for evaluation and the R2 and RMSE of the best model was obtained to be 0.58 and 10.99 ton/ha, respectively. The results of this study confirm the suitability of the GNDVI and NDI indeces for monitoring sugarcane growth during the first and second growth stages.


Main Subjects

Apan, A., Held, A., Phinn, S., and Markley, J. (2004). Detecting sugarcane “orange rust” disease using EO-1 Hyperion hyperspectral imagery.International Journal of Remote Sensing 25(2): 489-498
Bégué, A., Lebourgeois, V., Bappel, E., Todoroff, P., Pellegrino, A., Baillarin, F., and Siegmund, B. (2010). Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI. International Journal of Remote Sensing. 31 (20), 5391-5407.
Cai, Z., Jönsson, P., Jin, H., and Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sensing.9, 1271
Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment. 17(3), 301-306. doi.org/10.1016/0034-4257(85)90102-6
Do Bendini, H. N., Sanches, I. D., Körting, T. S., Fonseca, L. M. G., Luiz, A. J. B., and Formaggio, A. R. (2016). Using Landsat 8 image time series for crop mapping in a region of Cerrado, Brazil. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41, 845–850. doi.org/10.5194/isprsarchives-XLI-B8-845-2016
Elhag, A., and Abdelhadi, A. (2018). Monitering And Yield Estimation of Sugarcane Using Remote Sensing and GIS. American Journal of Engineering Research (AJER), 7(1), 170–179
Essari, M., and Mirlatifi, S. (2004). Exploring the use of TERRA satellite,MODIS sensor,CSWB model imagery To estimate the production of cane sugar. Case study of sugarcane cultivation and production of Mirzakochek Khan. Ph. D. dissertation, University of Tarbiat Modares.(In Farsi)
FAOSTAT (2017). Sugarcane stat Of United Nation. Retrieved December 15 from http://www.fao.org/faostat/en/#data
Gitelson, A. A., Kaufman, Y. J., and Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment. 58(3), 289-298 ,doi.org/10.1016/S0034-4257(96)00072-7
Gitelson, A. A., and Merzlyak, M. N. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research. 22(5), 689-692, doi.org/10.1016/S0273-1177(97)01133-2
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83(1-2), 195-213, doi.org/10.1016/S0034-4257(02)00096-2
Huete, A., Justice, C., and Liu, H. (1994). Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment. 49(3), 224-234, doi.org/10.1016/0034-4257(94)90018-3
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 25(3), 295-309, doi.org/10.1016/0034-4257(88)90106-X
IKAI. District (2018). Iman Khomeini Agro Industy.  Retrieved September 18, 2018, from http://www.ik-sugarcane.ir/
Jiang, Z., Huete, A., Didan, K., and Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment. 112(10), 3833-3845,
Kaufman, Y. J., and Tanr, D. (1992). Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 261–270.
Kauth, R. J., and Thomas, G. S. (1976). The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Laboratory for Applications of Remote Sensing. doi.org/10.1529/biophysj.106.083931
Landsat-SLC-off. (2018). Landsat7 SLC off. Retrieved November 18, 2018, from https://landsat.usgs.gov/landsat-7
Landsat7-BQA. (2018). Landsat Collection 1 Level-1 Quality Assessment Band. Retrieved November 18, 2018, from https://landsat.usgs.gov/collectionqualityband
Landsat7-L1TP. (2018). Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1 Data Products. Retrieved November 18, 2018, from https://lta.cr.usgs.gov/LETMP
Lisboa, I. P., Damian, J. M., Cherubin, M. R., Barros, P. P. da S., Fiorio, P. R., Cerri, C. C., and Cerri, C. E. P. (2018). Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal. Agronomy. doi.org/10.3390/agronomy8090196
Mcnairn, H., and Protz, R. (1993). Mapping corn residue cover on agricultural fields in oxford county, ontario, using thematic mapper. Canadian Journal of Remote Sensing. 19(2), 152–159.
Mobasheri, M. R., Chahardoli, M., and Farajzadeh, M. (2010). Introducing PASAVI and PANDVI methods for sugarcane physiological date estimation, using ASTER images. Journal of Agricultural Science and Technology. Journal agriculture science technology. 12, 309-320
Morel, J., Bégué, A., Todoroff, P., Martiné, J. F., Lebourgeois, V., and Petit, M. (2014). Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. European Journal of Agronomy. 61, 60-68, doi.org/10.1016/j.eja.2014.08.004
Muir, J. S., Robson, A. J., and Rahman, M. M. (2018). ‘Sugar from space’: Using satellite imagery to predict cane yield and variability. In 40th Annual Conference Australian Society of Sugar Cane Technologists, ASSCT 2018.
Mutanga, S., Schoor, C. Van, Olorunju, P. L., Gonah, T., and Ramoelo, A. (2013). Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery. Advances in Remote Sensing. 2(3), 269-275, doi.org/10.4236/ars.2013.23029
Rahman, M. M., and J. Robson, A. (2016). A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region. Advances in Remote Sensing. 5(2), 93-102, doi.org/10.4236/ars.2016.52008
Robson, A., Abbott, C., Lamb, D., and Bramley, R. O. B. (2012). Developing sugar cane yield prediction algorithms from satellite imagery. Proceedings of the Australian Society of Sugar Cane Technologists. 34(11).
Rondaux, G., Steven, M., and Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment. 55(2), 95-107, doi.org/10.1016/0034-4257(95)00186-7.
Rouse, J.W, Haas, R.H., Scheel, J.A., and Deering, D. W. (1974). ’Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium (pp. 48–62).
Vicente, L. E., Gomes, D., Victoria, D. de C., Koga-Vicente, A., and Iwashita, F. (2013). Evaluation of annual sugarcane monitoring using MODIS/EVI temporal series and spectral mixture analysis approach. In International Geoscience and Remote Sensing Symposium. Retrieved from https://www.researchgate.net/publication/264458361
Zhang, R. H., Rao, N. X., and Liao, K. N. (1996). Approach for a vegetation index resistant to atmospheric effect. Acta Botanica Sinica, 38(1), 53–62.