Abbaszadeh Tehrani, N., Beheshtifar, M. & Morabbi, M. (2011). Estimation of cropping area in Qazvin province with IRS-LISS III multi-timed images application. Environmental Researches, 2(3), 87-96.
Akbari, M. (2013). Estimation of cultivar density using satellite data. Journal of water research in agriculture, 27(1), 77-88.
Allen, R.G., Tasumi, M., Trezza, R., Waters, R., Bastiaanssen, W.G.M., 2002. Surface Energy Balance Algorithms for Land, Advance Training and Users Manual–Idaho Implementation, Version 1.
Allen, R., Morse, A. & Tasumi, M. (2003). Application of SEBAL for western US water rights regulation and planning. ICID workshop on remote sensing of ET for large regions.
Ashourloo, D., Matkan, A., Bagheri, B. & Shahri, M. (2012). Extracting Wheat Biomass Using Satellite Data and Geographically Weighted Regression. Agronomy Journal (Pajouhesh & Sazandegi), 104, 121–128.
Bastiaanssen, W.G.M., Menenti. M., Feddes, R.A. & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL): 1) Formulation. Journal of Hydrology, 212 (213):213-229.
Carneiro, F. M., Furlani, C. E. A., Zerbato, C., de Menezes, P. C., da Silva Gírio, L. A. & de Oliveira, M. F. (2019). Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors: Precision Agriculture, 1-29.
Hsu, C.w., Chang, C. & Lin, C. (2003). A practical guide to support vector classification.
Khodakarami, L. & Soffianian, A. (2012). Application of Multi Temporal Remote Sensing for Precision Farming. J. Sci. & Technol. Agric. & Natur. Resour. Water and Soil Sciences, 16(59), 215–231. (in Persian)
Mirzaei Mossivand, A., Ghorbani, A. & Keivan Behjou, F. (2018). Land use/cover change detection using Landsat and IRS imagery: A case study, Khalkhal County. Geographic Space, 17(60), 101–116. (In Persian)
Mountrakis, G., Im, J. & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259.
Najafi, A., Azizi Ghalati, S. & Mokhtari, M.H. (2017). Assessment Kernel Support Vector Machines in Classification of Landuses (Case Study: Basin of Cheshmeh kileh-Chalkrod). Journal of Watershed Management Research, 8(15), 92–101.
Riahi, V., Zeaiean Firouzabadi, P., Azizpour, F. & Darouei, P. (2019). Identification and investigation of the area under cultivation in Lenjanat using Landsat 8 satellite images. Researches in Geographical Sciences, 19(52), 147–169.
Schultz, G. A. & Engman, E. T. (2012). Remote sensing in hydrology and water management. Springer Science & Business Media.
Soheylifar, Z., Mirlatifi, S.M., Naseri, A.A. & Assari, M. (2012). Estimating Actual Evapotranspiration of Sugarcane by Remote Sensing. (A Case Study: Mirza Kochakkhan SugarcaneAgro-Industry Company Farms). Water and Soil Science, 23(1), 151-163. (in Persian)
Tasumi, M., Trezza, R., Allen, R. G. & Wright, J. L. (2003). US Validation tests on the SEBAL model for evapotranspiration via satellite. In 2003 ICID Workshop on Remote Sensing of ET for Large Regions, Vol. 17.
Torahi, A., Firoozy Nejad, M. & Abdolkhani, A. (2017). Assessment support vector machine (SVM) algorithm and maximum likelihood to providing land use map of Riparian forest using OLI (Case Study: Maroon River – Behban. Iranian Remote Sensing & GIS. 9(1), 49–62.
Zhou, X., Zheng, H., Xu, X., He, J., Ge, X., Yao, X., Cheng, T., Zhu, Y., Cao, W. & Tian, Y. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246-255.