Soils are essential for supporting food production and providing ecosystem services but are
under pressure due to population growth, higher food demand, and land use competition …

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 …

Geocomputation with R is for people who want to analyze, visualize and model geographic
data with open source software. It is based on R, a statistical programming language that …

Abstract Machine learning techniques are widely employed to generate digital soil maps.
The map accuracy is partly determined by the number and spatial locations of the …

For the better part of the 20th century pedologists mapped soil by drawing boundaries
between different classes of soil which they identified from survey on foot or by vehicle …

In this chapter we review the statistical theory for soil mapping. We focus on models
considered most suitable for practical implementation and use with soil profile data and …

Using machine learning and earth observation data to capture real-world variability in
spatial predictive mapping depends on sample size, design, and spatial extent …

Many national and international initiatives rely on spatially explicit information on soil
organic carbon (SOC) stock change at multiple scales to support policies aiming at land …

We performed a systematic mapping of validation methods used in digital soil mapping
(DSM), in order to gain an overview of current practices and make recommendations for …

Intensified human activities can augment soil organic carbon (SOC) losses from the world's
croplands, making SOC a highly dynamic parameter both in space and time. Sentinel-2 …