نقشه‌برداری رقومی خاک با استفاده از تکنیک‌های یادگیری ماشین

Adhikari, K., Minasny, B., Greve, M.B. & Greve, M.H. (2014). Constructing a soil class map of Denmark based on the FAO legend using digital techniques. Geoderma, 214, 101–113.

Aksoy, E., Panagos, P. & Montanarella, L. (2012). Spatial prediction of soil organic carbon of Crete using geostatistics. Pp. 149–159. In: Minasny B, et al. (eds.) Digital soil assessments and beyond. CRC Press, London.

Alijani, Z. & Sarmadian, F. (2014). The role of topography in changing of soil carbonate content. Indian Journal of Science and Research, 6, 263–271.

Arrouays, D., McKenzie, N., Hempel, J., de Forges, A.R. & McBratney, A.B. (2014). Global soil map: basis of the global spatial soil information system. CRC press.

Bagheri Bodaghabadi, M., Martinez-Casasnovas, J.A., Salehi, M.H., Mohammadi, J., Esfandiarpoor Borujeni, I., Toomanian, N. & Gandomkar, A. (2015). Digital soil mapping using artificial neural networks and terrain-related attributes. Pedosphere, 25, 580–591.

Boettinger, J.L., Ramsey, R.D., Bodily, J.M., Cole, N.J., Kienast Brown, S., Nield, S.J., Saunders, A.M. & Stum, A.K. (2008). Landsat spectral data for digital soil mapping. Pp. 193–203. In: Hartemink A.E. et al. (eds). Digital soil mapping with limited data. Springer, Australia.

Byrt, T., Bishop, J. & Carling, J.B. (1993). Bias, prevalence and kappa. Journal of Clinical Epidemiology, 46, 423–429.

Camera, C., Zomeni, Z., Noller, J.S., Zissimos, A.M., Christoforou, I.C. & Bruggeman, A. (2017). A high-resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization. Geoderma, 285, 35–49.

Carré, F., McBratney, A.B., Mayr, T. & Montanarella, L. (2007). Digital soil assessments: Beyond DSM. Geoderma, 142(1-2), 69–79.

Congalton, R. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

Congalton, R.G. and Green, K. (1998). Assessing the accuracy of remotely sensed data: principles and practices. CRC Press.

Friedman, J.H. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29, 1189–1232.

Heung, B., Bulmer, C.E. & Schmidt M.G. (2014). Predictive soil parent material mapping at a regional-scale: a random forest approach. Geoderma, 214, 141–154.

Jafari, A., Ayoubi, S., Khademi, H., Finke, P.A. & Toomanian, N. (2013). Selection of a taxonomic level for soil mapping using diversity and map purity indices: A case study from an Iranian arid region. Geomorphology, 201, 86–97.

Khaledian, Y & Miller, BA, (2020). Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 81, 401–418.

Lal, R., Mohtar, R.H., Assi, A.T., Ray, R., Baybil, H. & Jahn, M. (2017). Soil as a basic nexus tool: soils at the center of the food–energy–water nexus. Current Sustainable/Renewable Energy Reports, 4(3), 117–129.

Lie, M., Glaser, B. & Huwe, B. (2012). Uncertainty in the spatial prediction of soil texture: comparison of regression tree and Random Forest models. Geoderma, 170, 70–79.

Lumley, T. (2009). Regression subset selection. http://CRAN.com.

MacMillan, R.A., Jones, R.K. & McNabb, D.H. (2004). Defining a hierarchy of spatial entities for environmental analysis and modeling using digital elevation models (DEMs). Computers, Environment and Urban Systems, 28(3), 175–200.

McBratney, A.B., Santos, M.M.  & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3–52.

Minasny, B. & McBratney, A.B. (2007). Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma, 140, 324–336.

Minasny, B. & McBratney, A.B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301–311.

Mosleh, Z., Salehi, M.H., Jafari, A. Esfandiarpoor Borujeni, I. & Mehnatkesh, A. (2016). The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental Monitoring Assessment, 31, 188–195.

Padarian, J., Minasny, B. & McBratney, A.B. (2019). Using deep learning for digital soil mapping. Soil Discussion, 5(1), 79–89.

Padarian, J., Perez-Quezada, J. & Seguel, S. (2012). Modeling the distribution of organic carbon in the soils of Chile. Pp. 329–333. In: Minasny B, et al. (eds.) Digital soil assessments and beyond. CRC Press, London, UK. 

Pahlavanrad, M.R., Toomanian, N., Khormali, F., Brungard, C.W., Komaki, S.B. & 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, 97–106.

Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J.P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104.

Taghizadeh Mehrjardi, R., Nabiollahi, K., Minasny, B. & 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.

Wadoux, A.M.C. (2019). Using deep learning for multivariate mapping of soil with quantified uncertainty. Geoderma, 351, 59–70.

Zhu, A.X., Band L., Vertessy, R. & Dutton, B. (1997). Derivation of soil properties using a soil land inference model (SoLIM). Soil Science Society of America Journal, 61(2), 523–533.