- Al-doski, J., Mansorl Sh.B., & Shafri, H.Z.M. (2013). Image classification in remote sensing. Journal of Environment and Earth Science, 3(10), 141-47. https://doi.org/10.1088/1755-1315/540/1/012022
- Amirantekabi, S., Javan, F., & Hasani Moghaddam, H. (2017). Detection of land use changes and its influencing factors using Artificial Neural Network (Case study: Talesh County). Journal of GIS & RS Application in Planning, 8(3), 1-11. (In Persian with English abstract)
- Amiri, F. (2023). A review of remote sensing vegetation indices in the land cover assessment. Water and Soil Management and Modeling, 3(2), 297-318. (In Persian with English abstract). https://doi.org/10.22098/MMWS.2023.12207.1212
- Amiri, F., & Nateghi, S. (2023). Lands cover classification of Bushehr Province using landsat-8 and MODIS images. Water and Soil Management and Modelling, 3(2), 143-156. (In Persian with English abstract). https://doi.org/10.22098/mmws.2022.11372.1124
- Beg, A.A.F., & Reddy, Y.S. (2010). Estimation of urban heat island using Landsat ETM+ imagery at Chennai city – a case study. International Journal of Earth Sciences and Engineering, 3(3), 332-340.
- Banaei, M.H. (1998). Soil Moisture and Temperature Regime Map of Iran. Soil and Water Research Institute, Ministry of Agriculture, Iran.
- Dehghani, T., Ahmadpari, H., & Amini, A. (2022). Assessment of land use changes using multispectral satellite images and artificial neural network. Water and Soil Management and Modeling, 3(2), 18-35. (In Persian with English abstract). https://doi.org/10.22098/mmws.2022.11279.1114
- Diek, S., Fornallaz, F., Schaepman, M.E., & De Jong, R. (2017). Barest pixel composite for agricultural areas using landsat time series. Remote Sensing, 9(12), 1245. https://doi.org/10.3390/rs9121245
- Esfandeh, S., Danehkar, A., Salmanmahiny, A., Sadeghi, S.M.M., & Marcu, M.V. (2021). Climate change risk of urban growth and land use/land cover conversion: An in-depth review of the recent research in Iran. Sustainability, 14(1), 338. https://doi.org/10.3390/su14010338
- Ghoodjani, A. (2016). Advanced statistical methods and applications. Jameh Negar Publication. (In Persian)
- Gitelson, A.A., Merzlyak, M.N., & Chivkunova, O.B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45.
- Hasani Moghaddam, H., Adli Atiq, R., Gholami, J., Abasi Ghadim, A., & Zeaiean Firouz Abadi, P. (2018). Performance analysis of support vector machine, neural network and maximum likelihood in land use/cover mapping and GIS (A Case Study: Namin County).1-13. In 2nd International Conference on New Horizons in the Engineering Science, 9 August 2018. Yildiz Technical University, Istanbul, Turkey.
- Hosseini, S.B., Saremi, A., Noori Gheydari, M.H., Sedghi, H., & Firoozfar, A.R. (2020).Land use classification and determining the pattern of changes for 2014-2017, using OLI sensor’s data. Journal of Water and Soil, 34(1), 55-71. (In Persian with English abstract). https://doi.org/10.22067/jsw.v34i2.74878
- Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
- Huete, A.R., Liu, H.Q., Batchily, K., & van Leeuwen, W.J.D. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing Environment, 59, 440-451.
- (2021). Country Climate Analysis. In: Islamic Republic of Iran Meteorological Organization, Sarab center. Data sheet.
- Jamalabad, M.S., & Abkar, A.A. (2004). Forest canopy density monitoring, using satellite images.1-6. In XXth Proceedings of the International Society for Photogrammetry and Remote Sensing (ISPRS), 12-23 July 2004. ISPRS Congress: Istanbul, Turkey.
- Jensen, J. (2005). Introductory Digital Image Processing. Prentice Hall.
- Koroleva, P.V., Rukhovich, D.I., Rukhovich, A.D., Rukhovich, D.D., Kulyanitsa, A.L., Trubnikov, A.V., Kalinina, N.V., & Simakova, M.S. (2018). Characterization of soil types and subtypes in n-dimensional space of multitemporal (empirical) soil line. Eurasian Soil Science, 51(9), 1021-33. https://doi.org/10.1134/S1064229318090065
- Li, H., Wang, C., Zhong, C., Su, A., Xiong, C., Wang, J., & Liu, J. (2017). Mapping urban bare land automatically from landsat imagery with a simple index. Remote Sensing, 9, 700. https://doi.org/10.3390/rs9030249
- Nguyen, C.T., Chidthaisong, A., Diem, P.K., & Huo, L. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in Southeast Asia using landsat 8. Land, 10(3), 231. https://doi.org/10.3390/land10030231
- Niazi, Y., Ekhtesasi, M.R., Maleki Nejad, H., Morshedi, J., & Hoseyni, S.Z. (2011). Comparison between two classification methods of maximum likelihood and artificial neural network for providing land use maps, Case study: Ilam dam area. Geoghraphy and Development, 8(20), 119-132. (In Persian with English abstract). https://doi.org/10.22111/gdij.2010.633
- Nouri, A., Omidvar, J., Modaresi, F., Davari, K., Nouri, S., & Asadi, A. (2024). Estimating the changes in the agricultural lands using satellite images, Case study: Fariman dam downstream basin. Journal of Water and Soil, 37(6), 829-840. (In Persian with English abstract). https://doi.org/10.22067/jsw.2023.83163.1301
- Ojaghi, S., Ebadi, H., & Ahmadi, F. (2015). Using artificial neural network for classification of high resolution remotely sensed images and assessment of its performance compared with statistical methods. American Journal of Engineering, Technology and Society, 2(1), 1-8.
- Piyoosh, A.K., & Ghosh, S.K. (2018). Development of a modified bare soil and urban index for landsat 8 satellite data. Geocarto International, 33(4), 423-442. https://doi.org/10.1080/10106049.2016.1273401
- Qian, X., & Zhang, L. (2022). An integration method to improve the quality of global land cover. Advances in Space Research, 69(3), 1427-38. https://doi.org/10.1016/j.asr.2021.11.002
- Radoux, J., Chomé, G., Jacques, D.C., Waldner, F., Bellemans, N., Matton, N., & Defourny, P. (2016). Sentinel-2’s potential for sub-pixel landscape feature detection. Remote Sensing, 8(6), 488. https://doi.org/10.3390/rs8060488
- Rasul, A., Balzter, H., Ibrahim, G.R.F., Hameed, H.M., Wheeler, J., Adamu, B., Ibrahim, S., & Najmaddin, P.M. (2018). Applying built-up and bare-soil indices from landsat 8 to cities in dry climates. Land, 7, 81. https://doi.org/10.3390/land7030081
- Rouse, J.J., Haas, R.H., Deering, D., Schell, J., & Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA Special Publication 351, 309.
- Rujoiu-Mare, M.R., & Mihai, B.A. (2016). Mapping land cover using remote sensing data and GIS techniques: A case study of Prahova Subcarpathians. Procedia Environmental Sciences, 32, 244-55. https://doi.org/10.1016/j.proenv.2016.03.029
- Saraskanrood, S., Khodabandelo, B., Naseri, A., & Moradi, A. (2019). Extracting land use map based on a comparison between pixel-based and object-oriented classification methods, Case study: Zanjan city. Scientific Research Quarterly of Geographical Data, 28(110), 195-208. (In Persian with English abstract). https://doi.org/10.22131/sepehr.2019.36623
- Sinha, S., Sharma, L.K., & Nathawat, M.S. (2015). Improved land-use/land cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217-33. http://dx.doi.org/10.1016/j.ejrs.2015.09.005
- Yadav, P., Kapoor, M., & Sarma, K. (2012). Land use land cover mapping, change detection and conflict analysis of Nagzira-Navegaon corridor, central India using geospatial technology. International Journal of Remote Sensing and GIS, 1(2), 90-98.
- Yao, Y., Yan, X., Luo, P., Liang, Y., Ren, Sh., Hu, Y., Han, J., & Guan, Q. (2022). Classifying land-use patterns by integrating time-series electricity data and high-spatial resolution remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 106(102664), 1-11. https://doi.org/10.1016/j.jag.2021.102664
- Yousefi, S., Tazeh, M., Mirzaee, S., Moradi, H., & Tavangar, S. (2011). Comparison of different classification algorithms in satellite imagery to produce land. Journal of Applied RS & GIS Techniques in Natural Resource Science, 2(2), 15-24. (In Persian with English abstract)
- Zinck, J.A., Metternicht, G., Bocco, G., & Del Valle, H.F. (2016). Geopedology (An Integration of Geomorphology and Pedology for Soil and Landscape Studies). Springer Cham.
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