Alizadeh Dehkordi, P., Nehbandani, A.R, Hassanpour-bourkheili, S., and Kamkar, B., 2020. Yield gap analysis using remote sensing and modeling approaches: Wheat in the Northwest of Iran. International Journal of Plant Production 1-10. https://doi.org/10.1007/s42106-020-00095-4
Azhirabi, R., Kamkar, B., and Abdi, O., 2019. Comparison of geostatistical interpolation models (kriging) to estimate soil salinity and wheat yield (A case study: Army field of Aq Qala. Crop Production 12(1): 1-16. (In Persian with English Summary) https://doi.org/10.22069/EJCP.2019.6955.1495
Badsar, M., 2014. Yield gap estimation in wheat fields using GIS, RS and SSM model (A case study: Qaresso basin, Gorgan distinct). M.Sc. Thesis. Gorgan University of Agricultural Sciences and Natural, Iran. 95 p. (In Persian)
Balasundram, S.K., Memarian, H., and Khosla, R., 2013. Estimating oil palm yields using vegetation indices derived from Quickbird. Life Science Journal 10(4): 851-860
Bannari, A., Staenz, K., Haboudane, D., and Khurshid, K., 2006. Sensitivity analysis of chlorophyll indices to soil optical properties using ground-reflectance data. In: 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 120-123). https://doi.org/10.1109/IGARSS.2006.36.
Baskent, E.Z., and Keles, S., 2005. Spatial forest planning: A review. Ecological Modeling 188(2-4): 145-173. https://doi.org/10.1016/j.ecolmodel.2005.01.059
Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F., and Koropaka, A.E., 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58: 1501-1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
Dente, L., Satalino, G., Mattia, F., and Rinaldi, M., 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment 112(4): 1395-1407. https://doi.org/10.1016/j.rse.2007.05.023
Elvidge, C.D., and Chen, Z., 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment 54(1): 38-48. https://doi.org/10.1016/0034-4257(95)00132-K
FAO. 2018. Food and Agricultural Organization of the United Nations (sited in: http://www,fao.org/index_en.htm/, 1/1/2020.
Fatemi, B., and Rezaei, Y., 2006. Basic of Remote Sensing. Azade publication. 257 p. (In Persian)
Ghasemi, M., 2011. Investigating Weed biodiversity in wheat fields of Gorgan County. M.Sc. Thesis. Gorgan University of Agricultural Sciences and Natural Resources, Iran. 114 p. (In Persian with English Summary)
Goudarzi, M., Farahpour, M., and Mosavi, A.R., 2006. Land cover and rangeland classification map using Land sat satellite image (TM) (Case study) Namrood watershed. Rangeland and Desert Research 13(3): 265-277. (In Persian with English Summary)
Gutierrez, M., Norton, R., Thorp, K.R., and Wang, G., 2012. Association of spectral reflectance indices with plant growth and lint yield in upland cotton. Crop Science 52(2): 849-857. https://doi.org/10.2135/cropsci2011.04.0222
Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Kazemi, H., Tahmasebi Sarvestani, Z., Kamkar, B., Shataei, S., and Sadeghi, S., 2012. Evaluation of geostatistical methods for estimating and zoning of macronutrients in agricultural lands of Golestan province. Water Soil Science. 22(1): 201-218. (In Persian with English Summary)
Khosravi, R., Hemami, M.R. and Malekian, M., 2014. Comparison of geostatistical methods to determine the best bioclimatic data interpolation method for modeling species distribution in Central Iran. Iranian Journal of Applied Ecology 3(8): 55-68. (In Persian with English Summary) 20.1001.1.24763128.1393.3.8.5.5
Kim, Y., Jackson, T., Bindlish, R., Hong, S., Jung, G., and Lee, K., 2013. Retrieval of wheat growth parameters with radar vegetation indices. IEEE Geoscience and Remote Sensing Letters 11(4): 808-812. https://doi.org/10.1109/LGRS.2013.2279255
Kim, Y., and Van Zyl, J., 2009. A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing 47: 2519-2527. https://doi.org/10.1109/TGRS.2009.2014944
Koppe, W., Gnyp, M., Hutt, C., Yao, Y., Miao, Y., Chen, X., and Bareth, G., 2013. Rice monitoring with multitemporal and dualpolarimetric TerraSAR-X data.International Journal of Applied Earth Observation and Geoinformation, 21: 568-576. https://doi.org/10.1016/j.jag.2012.07.016
Liu, Z., Zhou, W., Shen, J., He, P., Lei, Q., and Liang, G., 2014. A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China. Geoderma 235: 39-47. https://doi.org/10.1016/j.geoderma.2014.06.027
Lobell, D.B., 2013. The use of satellite data for crop yield gap analysis. Field Crops Research 143: 56-64. https://doi.org/10.1016/j.fcr.2012.08.008
Ma, J.W., Nguyen, C.H., Lee, K., and Heo, J., 2019. Regional-scale rice-yield estimation using stacked auto-encoder with climatic and MODIS data: A case study of South Korea. International Journal of Remote Sensing 40(1): 51-71. https://doi.org/10.1080/01431161.2018.1488291
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(2): 301-322. https://doi.org/10.1016/j.ecolmodel.2004.07.032
Mohammadi Ahmad Mahmoudi, E., Kamkar, B., and Abdi, O., 2015. Comparison of geostatistical- and remote sensing data-based methods in wheat yield prediction in some of growing stages (A case study: Nemooneh filed, Golestan province). Crop Production 8(2): 51-76. (In Persian with English Summary). 20.1001.1.2008739.1394.8.2.3.5
Nassiri Mahallati, M., Koocheki, A.R., and Jahani, M., 2016. Estimating within field variability of wheat yield using spatial variables: An approach to precision agriculture. Journal of Agroecology 8(3): 329-345. (In Persian with English Summary) https://doi.org/10.22067/jag.v8i3.34502
Pourhadian, H., Kamkar, B., Soltani, A., and Mokhtarpour, H., 2019. Evaluation of forage maize yield gap using an integrated crop simulation model-satellite imagery method (Case study: Four watershed basins in Golestan Province). Archives of Agronomy and Soil Science 65(2): 253-268. https://doi.org/10.1080/03650340.2018.1493579
Rahmat, S.R., Firdaus, R.R., Shaharudin, S.M., and Ling, L.Y., 2019. Leading key players and support system in Malaysian paddy production chain. Cogent Food and Agriculture 5(1): 1708682. https://doi.org/10.1080/23311932.2019.1708682
Raziei, T., 2017. Köppen-Geiger climate classification of Iran and investigation of its changes during 20th century. Earth and Space Physics 43: 419-439. (In Persian with English Summary). https://doi.org/10.22059/JESPHYS.2017.58916
Ren, H., Zhou, G., and Zhang, F., 2018. Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. Remote Sensing of Environment 209: 439-445. https://doi.org/10.3390/s21062115
Rezaei Hossein Abad, A.R., 2013. An Investigation on the relationship between soil nutrients and wheat yield using Geographic Information Systems (GIS). M.Sc. Thesis, Gorgan University of Agricultural Sciences and Natural Resources, Iran. 71 p. (In Persian with English Summary)
Sanaienejad, S.H., Shah Tahmasbi, A.R., Sadr Abadi Haghighi, R., and Kelarestani, K.A., 2008. Study of spectral reflection on wheat fields in Mashhad using MODIS data. Journal of Water and Soil Science 12(45):11-19. (In Persian with English Summary) 20.1001.1.24763594.1387.12.45.2.9
SAS Institute., 2015. Base SAS 9.4 procedures guide. SAS Institute. www.sas.com
Shi, H., and Xingguo, M., 2011. Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecological Modeling 222(14): 2530-2541. https://doi.org/10.1016/j.ecolmodel.2010.11.011
Simoes, M.D.S., Rocha, J.V., and Lamparelli, R.A.C., 2005. Spectral variables, growth analysis and yield of sugarcane. Scientia Agricola 62(3): 199-207. https://doi.org/10.1590/S0103-90162005000300001
Siyal, A.A., Dempewolf, J., and Becker-Reshef, I., 2015. Rice yield estimation using Landsat ETM+ Data. Journal of Applied Remote Sensing 9: 1-16. https://doi.org/10.1117/1.JRS.9.095986
Tesfahunegn, G.B., Tamene, L., and Vlek, P.L.G., 2011. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil Tillage Research 117: 124–139. https://doi.org/10.1016/j.still.2011.09.005
Utset, A., Lopez, T., and Diaz, M., 2000. A comparison of soil maps, kriging and a combined method for spatially prediction bulk density and field capacity of Ferralsols in the Havana-Matanaz Plain. Geoderma 96(3): 199-213. https://doi.org/10.1016/S0016-7061(99)00055-5
Wang, J., Dai, Q., Shang, J., Jin, X., Sun, Q., Zhou, G., and Dai, Q., 2019. Field-scale rice yield estimation using Sentinel-1A Synthetic Aperture Radar (SAR) data in coastal saline region of Jiangsu province, China. Remote Sensing 11(19): 2274. https://doi.org/10.3390/rs11192274
Webster, R., and Oliver, M., 2001. Geostatistics for Environmental Scientists. John Wiley & Sons, Ltd, Chichester, 271 p.
Yaghouti, H., Pazira, E., Amiri, E., and Masihabadi, M.H., 2019. The feasibility of using vegetation indices and soil texture to predict rice yield. Polish Journal of Environmental Studies 28(4). DOI: https://doi.org/10.15244/pjoes/81088
Zarco-Tejada, P.J., Ustin, S.L., and Whiting, M.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal 97(3): 641-653. https://doi.org/10.2134/agronj2003.0257
Zhu, Y., Yao, X., Tian, Y., Liu, X., and Cao, W., 2008. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation 10(1): 1-10. https://doi.org/10.1016/j.jag.2007.02.006
Zolekar, R.B., and Bhagat, V.S., 2015. Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Computers and Electronics in Agriculture 118: 300-321. https://doi.org/10.1016/j.compag.2015.09.016