Digital mapping of soil organic carbon using ensemble learning model in Mollisols of Hyrcanian forests, northern Iran

• Published in: Geoderma Regional Vol. 20; p. e00256
• Main Authors: Tajik, Samaneh, Ayoubi, Shamsollah, Zeraatpisheh, Mojtaba
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020
• Subjects: computer-aided mapping; cost effectiveness; deciduous forests; Environmental covariates; Feature selection; forest ecosystems; forest soils; Iran; least squares; linear models; Machine learning; Mollisols; prediction; remote sensing; soil organic carbon; Soil properties; soil sampling; soil surveys; Spatial modeling; support vector machines; topography; vegetation index; Iran; Environmental covariates; Feature selection; Mollisols; Soil properties; Machine learning; Spatial modeling

This study was conducted to evaluate the efficacy of the ensemble machine learning model to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous forest ecosystem in northern Iran. To do this, a total of 153 soil samples by applying regular systematic sampling grid...