ارزیابی تعمیم‌پذیری مدل جنگل تصادفی برای پیش‌بینی کلاس‌های خاک در سطح زیرگروه

2.Breiman, L. 2001. Random forests. Machine Learning. 45: 1. 5-32.

3.Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A., and Edwards, T.C. 2015. Machine earning for predicting soil classes in three semi-arid landscapes. Geoderma. 239-240: 1. 68-83.

4.Bui, E.N. 2004. Soil survey as a knowledge system. Geoderma. 120: 1-2. 17-26.

5.Carré, F., and Girard, M.C. 2002. Quantitative mapping of soil types based on regression kriging of taxonomic distances with landform and land cover attributes. Geoderma. 110: 3-4. 241-263.

6.Coll, C., Galve, J.M., Sanchez, J.M., and Caselles, V. 2010. Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing.
48: 1. 547-555.

 7.Congalton, R. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. 37: 1. 35-46.

8.Debella-Gilo, M., and Etzelmüller, B. 2009. Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS. Examples from Vestfold County, Norway. Catena. 77: 1. 8-18.

9.Gallant, J.C., and Austin, J.M. 2015. Derivation of terrain covariates for digital soil mapping in Australia. Soil Research. 53: 1. 895-90.

10.Grimm, R., Behrens, T., Marker, M., and Elsenbeer, H. 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island-Digital soil mapping using random forests analysis. Geoderma. 146: 1-2. 102-113.

11.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: 1-2. 180-190.

12.Guo, P.T., Li, M.F., Luo, W., Tang, Q.F., Liu, Z.W., and Lin, Z.M. 2015. Digital mapping of soil organic matter for rubber plantation at regional scale: an application of Random Forest plus residual kriging approach. Geoderma. 237-238: 1. 49-59.

13.Heung, B.C., Bulmer, C.E., and Schmitdt, M.G. 2014. Predictive soil parent material mapping at a regional-scale: A random forest approach. Geoderma. 214-215: 1. 141-154.

14.Ho, H.C., Knudby, A., Sirovyak, P., Xu, Y., Hodul, M., and Henderson, S.B. 2014. Mapping maximum urban air temperature on hot summer days. Remote Sensing of Environment. 154: 1. 38-45.

15.Jenny H. 1941. Factors of Soil Formation, a System of Quantitative Pedology. McGraw-Hill, New York, 281p.

16.Lagacherie, P. 2002. Cartographie de la diversité des sols viticoles de versant par imagerie à haute résolution: contribution à la connaissance des terroirs, Montpellier, France.

17.Lagacherie, P., Legros, J.P., and Burrough, P.A. 1995. A soil survey procedure using the knowledge on soil pattern of a previously mapped reference area. Geoderma. 65: 3-4. 283-301.

18.Mahler, P.J. 1970. Manual of Multipurpose Land Classification. Report no. 212. Soil and Water Research Institute, Tehran. Iran.

19.Mallavan, B.P., Minasny, B., and McBratney, A.B. 2010. Homosoil: a methodology for quantitative extrapolation of soil information across the globe.
P 137-149. In: J.L. Boettinger (ed.) Digital Soil Mapping: Bridging Research, Environmental Application, and Operation. Springer, London.

20.Malone, B.P., Sanjeev, K.J., Minasny, B., and McBratney, A.B. 2016. Comparing regression-based digital soil mapping and multiple-point geostatistics for the spatial extrapolation of soil data. Geoderma. 262: 1. 243-253.

21.McBratney, A.B., Mendonça Santos, M.L., and Minasny, B. 2003. On digital soil mapping. Geoderma. 117: 1-2. 3-52.

22.Mehnatkesh, A., Ayoubi, S., Jalalian, A., and Sahrawat, K.L. 2013. Relationships between soil depth and terrain attributes in a semi-arid hilly region in western Iran. J. Moun. Sci. 10: 1. 163-172.

23.Minasny, B., and McBratney, A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of  ancillary information. Computer and Geoscience. 32: 9. 1378-1388.

24.Minasny, B., and McBratney, A.B. 2007. Spatial prediction of soil properties using EBLUP with the Matern covariance function. Geoderma. 140: 1. 324-336.

25.Moore, I.D., Gessler, P., Nielsen, G., and Peterson, G. 1993. Soil attribute prediction using terrain analysis. Soil Sci. Soc. Amer. J. 57: 2. 443-452.

26.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: 1. 97-106.

27.RStudio. 2015. RStudio: Integrated Development Environment for R, Boston, MA. Available at http://www.
r-studio.com. (Visited 20 November 2018).

28.Saga Development Team. 2011. System for Automated Geoscientific Analyses (SAGA). Available at http://saga-gis. org/en/index.html (visited 12 August 2012).

29.Schoeneberger, P.J., Wysocki, D.A., Benham, E.C., and Broderson, W.D. 2012. Field book for describing and sampling soils, version 3.0. USDA Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE.

30.Sim, J., and Wright, C.C. 2005. The kappa statistic in reliability studies: use, interpretation and sample size requirements. Physical Therapy. 85: 3. 257-268.

31.Soil and Water Research Institute. 1999. Semi detailed soil survey of Saadat Shahr, Sivand, Seydan and Arsenjan. Soil and Water Research Institute of Iran, Ministryof Agricultures, Tehran, Iran. (In Persian)

32.Soil Survey Staff. 2014. Keys to soil taxonomy, 12^th edition. USDA Natural Resources Conservation Service.

33.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: 1. 67-77.

34.Thompson, J.A., Pena-Yewtukhiq, E.M., and Grove, J.H. 2006. Soil-landscape modeling across a physiographic
region: topographic patterns and model transportability. Geoderma. 133: 1-2. 57-70.

35.United State Department of Agriculture. Soil Conservation Service. 1993. Soil survey manual. Soil Survey. Div.
Staff. US. Department of Agriculture. Handbook. 18. Washington, DC.

36.Zhu, A.X., Hudson, B., Burt, J., Lubich, K., and Simonson, D. 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Sci. Soc. Amer. J. 65: 5. 1463-1472.

37.Zhu, A.X., Liu, J., Du, F., Zhang, S.J., Qin, C.Z., Burt, J., Behrens, T., and Scholten, T. 2015. Predictive soil mapping with limited sample data. Europ. J. Soil Sci. 66: 1. 535-547.