The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …

Pedology focuses on understanding soil genesis in the field and includes soil classification
and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and …

Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to
identify relationships between soil properties and multiple covariates that can be detected …

Abstract Knowledge about distribution of soil properties over the landscape is required for a
variety of land management applications and resources, modeling, and monitoring …

Land suitability assessment is essential for increasing production and planning a
sustainable agricultural system, but such information is commonly scarce in the semi-arid …

Abstract Knowledge about the spatial variability of soil aggregate stability indices, soil
organic carbon (SOC) in various aggregate sizes, and aggregation across the landscape is …

Slope gradient and land use change are known to influence soil quality and the assessment
of soil quality is important in determining sustainable land-use and soil-management …

The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe,
Cu, Mn) in western Iran, using environmental covariates and applying two machine learning …

This study was designed to evaluate soil quality (SQ) in deforested and intensively cultured
lands in Mazandaran Province, Iran. For this purpose, three soil quality indices (SQIs …

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 …