Badiehneshin, A., Noory, H. and Vazifedoust, M. (2015). Improving Crop Yield Estimation through SWAP Model Using Satellite Data. Iranian Journal of Soil and Water Research. 45 (4), 379-388.
Balaghi, R., Tychon, B., Eerens, H. and Jlibene, M. (2008). Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geo-information, 10, 438–452.
Bastiaanssen, W. G. and Steduto, P. (2016). The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize. Science of the Total Environment. 575, 1-17. http://dx.doi.org/10.1016/j.scitotenv.2016.09.032.
Bastiaanssen, W.G.M. and Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture Ecosystems & Environment, 94, 321–340.
Farahza, M. N., Nazari, B., Akbari, M. R., Naeini, M. S. and Liaghat, A. (2020). Assessing the physical and economic water productivity of annual crops in Moghan Plain and analyzing the relationship between physical and economic water productivity. Journal of Irrigation and Water Engineering, 11 (42), 166-179. (In Farsi)
Ferencz, C., Bognar, P., Lichtenberger, J., Hamar, D., Tarscai, G., et al. (2004) Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25. 4113–4149.
Foster, T. Brozovic, N., Butler, A. P., Neale, C. M. U. Raes, D., Steduto, P. and Hsiao, T. C. (2017). AquaCrop- OS: An open source version of FAO's crop water productivity model. Agricultural Water Management, 181, 18-22.
Funk, C. and Budde, M.E. (2009). Phonologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sensing of Environment, 113, 115–125.
Gemechu, M.G., Huluka, T.A., Steenbergen, F.V., Wakjira, Y.C., Chevalking, S. and Bastiaanssen, S.W. (2020). Analysis of spatial-temporal variability of water productivity in Ethiopian sugar estates: using open access remote sensing source. Annals of GIS. https://doi.org/10.1080/19475683.2020.1812716.
Huang, J.F., Wang, F.M. and Wang, X.Z. (2010). Hyper-spectral experiment for paddy rice remote sensing; Huang JQ, Chen JY, editors. Hangzhou: Zhejiang University Press. 315 p. (in Chinese with English abstract).
Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.
Jalali Koutenaei, N. 2009. Basic and criteria survey, design and execution of land consolidation in the paddy fields. Haraz Extension and Technology Development Center. 212pp.
Kamali, L., Kaviani, A., Nazari, B. and Liaghat, A. M. (2018). Wheat yield estimate by satellite imageris Landsat 8 (Case study: Moghan Plain). Iranian Journal of Soil and Water Research. 49 (5), 1031-1042.
Karthikeyan, L., Chawla, I. and Mishra, A.K. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. Journal of Hydrology, 586, 1-22.
Kastens, J.H., Kastens, T.L., Kastens, D.L.A., Price, K.P., Martinko, E.A., et al. (2005). Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sensing of Environment, 99, 341–356.
Loveimi, N., Akram, N., Bagheri, N. and Hajiahmad, A. (2021). Evaluation of several spectral indices for estimation of Canola yield using Sentinel- 2 sensor image. Journal of Agricultural Machinery, 11 (2), 447- 464. (In Farsi)
Mahmoud Soltani, S. and Abbasian, A. (2021). Simultaneous appliacation of rice husk biochare and zinc sulfate fertilizer on yield, yield components of rice (Oryza sativa L.) Hashemi cultivar and some soil chemical properties. Iranian Journal of Soil and Water Research. 52 (3), 707-719.
Manjunath, K.R., Potdar, M.B. and Purohit, N.L. (2002). Large area operational wheat yield model development and validation based on spectral and meteorological data. International Journal of Remote Sensing, 23, 3023–3038.
Maselli, F. and Rembold, F. (2001). Analysis of GAC NDVI data for cropland identification and yield forecasting in Mediterranean African countries. Photogrammetric Engineering and Remote Sensing, 67, 593–602.
Maselli, F., Romanelli, S., Bottai, L. and Maracchi, G. (2000). Processing of GAC NDVI data for yield forecasting in the Sahelian region. International Journal of Remote Sensing, 21, 3509–3523.
Mcbratney, A., Whelan, B., Ancev, T. and Bouma, J. (2005). Future directions of precision agriculture. Journal of Precision Agriculture, 6 (1), 7-23.
Meier, U. 2001. Growth stages of mono-and dicotyledonous plants, BBCH Monograph. Federal Biological Research Center for Agriculture and Forestry.
Mkhabela, M.S., Bullock, P., Raj, S., Wang, S. and Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151, 385–393.
Mkhabela, M.S. and Mashinini, N.N. 2005. Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAAs- AVHRR. Agricultural and Forest Meteorology, 129, 1–9.
Mika, J., Kerenyi, J., Rimoczi-Paal, A., Merza, A., Szinell, C., et al. (2002). On correlation of maize and wheat yield with NDVI: Example of Hungary (1985-1998) In: Fellous JL, LeMarshall JF, Choudhury BJ, Menenti M, Paxton LJ et al., editors. Earth’s Atmosphere, Ocean and Surface Studies, 2399–2404.
Mo, X., Liu, S., Lin, Z., Xu, Y., Xiang, Y. and McVicar, T.R. (2005). Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling, 183, 301-322.
Mosleh M.K., Hassan Q.K. and Chowdhury E.H. (2015). Application of Remote Sensing in Mapping Rice Area and Forecasting Its Production. Sensors, 15, 769-791.
Nazari, B., Liaghat, A., Akbari, M. R. and Keshavarz, M. (2018). Irrigation water management in Iran: Implications for water use efficiency improvement. Agricultural Water Management, 208, 7-18.
Noureldin, N.A., Aboelghar, M.A., Saudy, H.S. and Ali, A.M. (2013). Rice yield forecasting models using satellite imagery in Egypt. The Egyptian Journal of Remote Sensing and Space Sciences, 16, 125-131.
Nuarsa, I.W., Nishio, F. and Hongo, C. (2012). Rice yield estimation using Landsat ETM+ data and field observation. Journal of Agricultural Science, 4 (3), 45- 56.
Prasad, A.K., Singh, R.P., Tare, V. and Kafatos, M. (2007). Use of vegetation index and meteorological parameters for the prediction of crop yield in India. International Journal of Remote Sensing, 28, 5207–5235.
Ren, J.Q., Chen, Z.X., Zhou, Q.B. and Tang, H.J. (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geo-information, 10, 403–413.
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. Journal of Plant Production, 20 (4), 45- 63. (In Farsi)
Tian, F., Zhang, Y. and Saihong, L. (2020). Spatial-temporal dynamics of cropland ecosystem water-use efficiency and the responses to agricultural water management in the Shiyang River Basin, northwestern China. Agricultural Water Management, 237, 1-12.
Virnodkar, S.S., Pachghare, V.K., Paril, V.C. and Jha, S.K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision agriculture. https://doi.org/10.1007/s11119-020-09711-9.
Wannebo, A. and Rosenzweig, C. (2003). Remote sensing of US cornbelt areas sensitive to the El Ni@o-Southern Oscillation. International Journal of Remote Sensing, 24 (10), 2055-2067.
Wang, R.C, Huang, J.F. (2002). Rice yield estimation using remote sensing data. Beijing. China Agriculture Press, 287 p. (in Chinese with English abstract).
Weissteiner, C. and Ku¨hbauch, W. (2005). Regional Yield Forecasts of Malting Barley (Hordeum vulgare L.) by NOAA-AVHRR Remote Sensing Data and Ancillary Data. Journal of Agronomy and Crop Science, 191, 308–320.
Wendroth, O., Reuter, H.I. and Kersebaum, K.C. (2003). Predicting yield of barley across a landscape: a state-space modeling approach. Journal of Hydrology, 272, 250–263.
Zandsalimi, Z., Sima, S. and Mousivand, A.J. (2021). Evaluating the Performance of Global Land Cover Maps in Agricultural Land Delineation (Case Study: Lake Urmia Basin). Iranian Journal of Soil and Water Research. 52 (3), 795-810.
Zhang, F., Wu, B.F. and Luo, Z.M. (2004). Winter wheat yield predicting for America using remote sensing data. Journal of Remote Sensing, 8, 611–617. (In Chinese with English abstract).