ارزیابی شاخص‌های سبزینگی در مدل‌سازی عملکرد نیشکر با تأکید بر الگوی رشد بر اساس پردازش تصاویر ماهواره‌ای مطالعه موردی: خوزستان کشت و صنعت امام خمینی (ره)

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.