1.Abdinam, A. 2004. An investigation on preparing of the soil salinity map using correlation
method between imagery and soil salinity data in the Qazvin plain. J. Pazhouhesh and
Sazandegi. 64: 33-38. (In Persian)
2.Adhikari, K., Minasny, B., Greve, B.G., and Greve, M.H. 2014. Constructing a soil class map of
Denmark based on the FAO legend using digital techniques. Geoderma. 214 -215: 101-113.
3.Azhirabi, R., Kamkar, B., and Abdi, O. 2015. Comparison of different indices adopted from
Landsat images to map soil salinity in the army field of Gorgan. J. Soil Manage. Sust. Prod.
5: 1. 173-176. (In Persian)
4.Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. 1984. Classification and
Regression Trees. Chapman & Hall, New York, 355p.
5.Brus, D.J., Kempen, B., and Heuvlink, G.B.M. 2011. Sampling for validation of digital soil
maps. Eur. J. Soil Sci. 62: 394-407.
6.Dai, P.F., Qigang, Z., Zhiqiang, L.V., Xuemei, W., and Gangcai, W.L. 2014. Spatial
prediction of soil organic matter content integrating artificial neural network and ordinary
kriging in Tibetan Plateau. Ecol. Ind. 45: 184-194.
7.Farifte, J., Farshad, A., and George, R.J. 2005. Assessing salt - affected soils using remote
sensing, solute modeling, and geophysics. Geoderma. 130: 191-206.
8.Grinand, C., Arrouays, D., Laroche, B., and Martin, M.P. 2008. Extrapolating regional soil
landscapes from an existing soil map: sampling intensity, validation procedures, and
integration of spatial context. Geoderma. 143: 180-190.
9.Hengel, T., Rossiter, D.G., and Stein, A. 2003. Soil sampling strategies for spatial prediction
by correlation with auxiliary maps. Geoderma. 120: 75-93.
10.Hengl, T., Toomanian, N., Reuter, H., and Malakouti, M.J. 2007. Methods to interpolate soil
categorical variables from profile observations: Lessons from Iran. Geoderma. 140: 417-427.
11.Heung, B., Bulmer, C.E., and Schmidt, M.G. 2014. Predictive soil parent material mapping
at a regional-scale: a random forest approach. Geoderma. 214-215: 141-154.
12.Jafari, A., Khademi, H., Finke, P., Wauw, J.V.D., and Ayoubi, S. 2014. Spatial prediction of
soil great groups by boosted regression trees using a limited point dataset in an arid region,
southeastern Iran. Geoderma. 232-234: 148-163.
13.Kempen, B., Brus, D.J., Heuvelink, G.B.M., and Stoorvogel, J.J. 2009. Updating the
1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach.
Geoderma. 151: 311-326.
14.Kheir, R.B., Greve M.H., Abdallah, C., and Dalgaard, T. 2010. Spatial soil zinc content
distribution from terrain parameters: A GIS-based decision-tree model in Lebanon. Environ.
Pollut. 158: 520-528.
15.Marcel, G.S., Feike, J.L., Martinus, T., and Van Genuchten, H. 1998. Neural Network
Analysis for Hierarchical Prediction of Soil Hydraulic Properties. Soil Sci Soc. Am. J.
62: 847-855.
16.McBratney, A.B., Odeh, I.O.A., Bishop, T.F.A., Dunbar, M.S., and Shatar, T.M. 2000.
An overview of pedometric techniques for use in soil survey. Geoderma. 97: 293-327.
17.McBratney, A.B., Santos, M.L.M., and Minasny, B. 2003. On digital soil mapping.
Geoderma. 117: 3-52.
18.Metternicht, G., and Zinck, J.A. 2004. Remote sensing of soil salinity: Potentials and
constraints. Remote Sens Environ. 64: 33-38.
19.Minasny, B., and McBratney, A. 2002. The method for fitting neural network parametric
pedotransfer functions. Soil Sci. Soc. Am. J. 66: 2. 352-361.
20.Minasny, B., and McBratney, A.B. 2006. A conditioned Latin hypercube method for
sampling in the presence of ancillary information. Comput. Geosci. 32: 1378-1388.
21.Nosrati, H., and Eftekhari, M. 2014. A new approach for variable selection using fuzzy logic.
Computational Intelligence in Electrical Engineering. 4: 71-83. (In Persian)
22.Pahlavan-Rad, M.R., Toomanian, N., Khormali, F., Brungard, C.W., Komaki, C.B., and
Bogaert. P. 2014. Updating soil survey maps using random forest and conditioned Latin
hypercube sampling in the loess derived soils of northern Iran. Geoderma. 232-234: 97-106.
23.Piccini, C., Marchetti, A., and Francaviglia. R. 2014. Estimation of soil organic matter by
geostatistical methods: use of auxiliary information in agricultural and environ-mental
assessment. Ecol. Ind. 36: 301-314.
24.Sparks, D.L., Page, A.L., Helmke, P.A., Leoppert, R.H., Soltanpour, P.N., Tabatabai, M.A.,
Johnston, G.T., and Summer, M.E. 1996. Methods of Soil Analysis. Soil. Sci. Soc. Am. J.
Madison, Wisconsin.
25.Tajgardan, T., Aubi, Sh., Shatai, Sh., and Khormali, F. 2009. Mapping soil surface salinity
using remote sensing data of ETM+. (Case study: North of Agh Ghala, Golestan province). J.
Soil Water Cons. 88: 9-15. (In Persian)
26.Taghizadeh-Mehrjardi, R., Nabiollahi, K., Minasny, B., and Triantafilis, J. 2015. Comparing
data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh
region, Iran. Geoderma. 253-254: 67-77.
27.Taghizadeh-Mehrjardi, R. 2016. Modern concepts in Soil Science (PEDOMETRICS).
Ardakan Univ. Press, 311p.
28.Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., and Malone, B.P. 2014. Digital
mapping of soil salinity in Ardakan region, central Iran. Geoderma. 213: 15-28.
29.Taghizadeh-Mehrjardi, R., Nabiollahi, K., and Kerry, R. 2016. Digital mapping of soil
organic carbon at multiple depths using different data mining techniques in Baneh region,
Iran. Geoderma. 253-254: 67-77.
30.Taghizadeh-Mehrjardi, R., Sarmadian, F., Savaghebi, G.H., Omid, M., Tomanian, N., Rosta,
M.J., and Rahimian, M.H. 2013. The comparison of efficiency of neuro-fuzzy, genetic
algoritm, neural network an multivariate regression models to prediction of soil salinity
(Case study: Ardakan). J. Natur. Resour. 66: 2. 207-222. (In Persian)
31.Vasques, G.M., Dematte, J.A.M., Viscarra Rossel, R.A., Ramirez-Lopez, L., and Terra, F.S.
2014. Soil classification using visible/near-infrared diffuse reflectance spectra from multiple
depths. Geoderma. 223-225: 73-78.
32.Veronesi, F., Corstanje, R., and Mayr, T. 2014. Landscape scale estimation of soil carbon
stock using 3D modeling. Sci. Total Environ. 487: 578-586.