Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms

• Published in: Geoderma Regional Vol. 31; p. e00584
• Main Authors: Kaya, Fuat, Başayiğit, Levent, Keshavarzi, Ali, Francaviglia, Rosa
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022
• Subjects: agroecology; decision support systems; digital elevation models; Digital soil mapping; Environmental covariates; Fluvisols; lakes; Landsat; Machine learning; prediction; remote sensing; soil management; soil texture; Soil texture class; support vector machines; texture; topography; topsoil; uncertainty; Vertisols; Environmental covariates; Vertisols; Fluvisols; Digital soil mapping; Soil texture class; Machine learning

Soil texture classes (STCs) influence the physical, chemical and biological properties of the soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling. The purpose of this study was to assess the capabilities of environmental covariates derived from Landsat 8 OLI Sci...