Apan, A., Held, A., Phinn, S., & Markley, J. (2004). Detecting sugarcane “orange rust” disease using EO-1 Hyperion Apan, A., Held, A., Phinn, S., & Markley, J. (2004). Detecting sugarcane “orange rust” disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing. https://doi.org/10.1080/01431160310001618031
Bastidas Obando, E. and Carbonell Gonzalez, J. (2007). Evaluating the Applicability of MODIS Data for Forecasting Sugarcane Yields in Colombia. In International Society of Sugar Cane Technologists (ISSCT). Durban.
Bégué, A., Lebourgeois, V., Bappel, E., Todoroff, P., Pellegrino, A., Baillarin, F., & Siegmund, B. (2010). Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI. International Journal of Remote Sensing. https://doi.org/10.1080/01431160903349057
Benvenuti, F. and Weill, M. (2010). Relationship between Multi-Spectral Data and Sugarcane Crop Yield. In Proceedings of the 19th World Congress of Soil Science and Soil Solutions for a Changing World (pp. 33–36). Brisbane, 1-6 August 2010.
Bruc, C. M., & Hilbert, D. W. (2004). PRE-PROCESSING METHODOLOGY FOR APPLICATION TO LANDSAT TM/ETM+ IMAGERY OF THE WET TROPICS. Cairns, QLD, Australia 4870.
Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(88)90019-3
Do Bendini, H. N., Sanches, I. D., Körting, T. S., Fonseca, L. M. G., Luiz, A. J. B., & 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 (Vol. 41, pp. 845–850). https://doi.org/10.5194/isprsarchives-XLI-B8-845-2016
Duveiller, G., López-Lozano, R., & Baruth, B. (2013). Enhanced processing of 1-km spatial resolution fAPAR time series for sugarcane yield forecasting and monitoring. Remote Sensing. https://doi.org/10.3390/rs5031091
El Hajj, M., Bégué, A., Guillaume, S., & Martiné, J. F. (2009). Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices - The case of sugarcane harvest on Reunion Island. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2009.04.009
Essari, M., & Mirlatifi, S. (1393). 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. Tarbiat Modares University.(In Farsi)
FAOSTAT. (2017). Sugarcane stat Of United Nation. Retrieved December 15, 1BC, from http://www.fao.org/faostat/en/#data
Gitelson, A. A., & Merzlyak, M. N. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research. https://doi.org/10.1016/S0273-1177(97)01133-2
Hall, F. G., Strebel, D. E., Nickeson, J. E., & Goetz, S. J. (1991). Radiometric rectification: Toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(91)90062-B
Holben, B., & Fraser, R. S. (1984). Red and near-infrared sensor response to off-nadiir viewing. International Journal of Remote Sensing. https://doi.org/10.1080/01431168408948795
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. https://doi.org/10.1016/S0034-4257(02)00096-2
Huete, A., Justice, C., & Liu, H. (1994). Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(94)90018-3
Iannini, L., Molijn, R., Tabak, A., Mousivand, A., & Hanssen, R. (2015). Sugarcane Identification Through Time-Series of Landsat and ERS/ENVISAT Data. Simposio Brasileiro de Sensoriamento Remoto, Natal, Brasil.
Julien, Y., & Sobrino, J. A. (2010). Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2009.11.001
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
Lee-Lovick, G. and Kirchner, L. (1990). The Application of Remotely Sensed (Landsat TM) Data to Monitor the Growth and Predict Yields in Sugarcane. Australian Society of Sugar Cane Technology, 65–72.
Lisboa, I. P., Damian, J. M., Cherubin, M. R., Barros, P. P. da S., Fiorio, P. R., Cerri, C. C., & 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. https://doi.org/10.3390/agronomy8090196
Lobell, D. B., Asner, G. P., Ortiz-Monasterio, J. I., & Benning, T. L. (2003). Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties. Agriculture, Ecosystems and Environment. https://doi.org/10.1016/S0167-8809(02)00021-X
Morel, J., Bégué, A., Todoroff, P., Martiné, J. F., Lebourgeois, V., & 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. https://doi.org/10.1016/j.eja.2014.08.004
Mulianga, B., Bégué, A., Simoes, M., & Todoroff, P. (2013). Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI. Remote Sensing. https://doi.org/10.3390/rs5052184
Mutanga, S., Schoor, C. Van, Olorunju, P. L., Gonah, T., & Ramoelo, A. (2013). Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery. Advances in Remote Sensing. https://doi.org/10.4236/ars.2013.23029
Rahman, M. M., & 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. https://doi.org/10.4236/ars.2016.52008
Robson, A., Abbott, C., Lamb, D., & Bramley, R. O. B. (2012). Developing sugar cane yield prediction algorithms from satellite imagery. Proceedings of the Australian Society of Sugar Cane Technologists.
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). Retrieved from https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740022592.pdf
sadeghzade hemayati, S., Hamdi, H., fathollahzade taleghani, D., & Amili, H. (2012). National documentary on sugarcane strategic research. Agricultural Research and Education Institute of Sugar Crushing Company. (In Farsi)
Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N., & Ghaemi, M. (2014). Wheat yield estimation using landsat images and field observation: A case study in Mashhad. Plant Production, 20(4), 45–63. (In Farsi)
Zakidizaji, H., Monjezi, N., & Sheikhdavoodi, M. J. (2018). Investigating Effective Factors on Sugarcane Production Performance to Increase the Production of Sugarcane Using Data Mining. Iranian Journal Of Biosystem Engineering, 49(3), 501–511. https://doi.org/https://ijbse.ut.ac.ir/article_68269.html. (In Farsi)