Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran

• Published in: Geoderma Vol. 338; pp. 445 - 452
• Main Authors: Zeraatpisheh, Mojtaba, Ayoubi, Shamsollah, Jafari, Azam, Tajik, Samaneh, Finke, Peter
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.03.2019
• Subjects: artificial intelligence; calcium carbonate; clay fraction; computer-aided mapping; digital elevation models; Environmental covariates; Iran; Landsat; landscapes; Modeling accuracy; monitoring; nonlinear models; prediction; regression analysis; remote sensing; semiarid zones; Soil characteristics; soil organic carbon; soil sampling; soil surveys; Spatial prediction; thematic maps; topography; topsoil; vegetation index; Iran; Modeling accuracy; Environmental covariates; Spatial prediction; Soil characteristics

Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices. The main aim of this research was to conduct a spatially prediction of the top soil properties such as soil organic carbon (...