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 …

To deal with the global and regional issues including food security, climate change, land
degradation, biodiversity loss, water resource management, and ecosystem health, detailed …

Abstract Machine learning algorithms find frequent application in spatial prediction of biotic
and abiotic environmental variables. However, the characteristics of spatial data, especially …

This paper describes the technical development and accuracy assessment of the most
recent and improved version of the SoilGrids system at 250m resolution (June 2016 update) …

We present new spatio-temporal geostatistical modelling and interpolation capabilities of the
R package gstat. Various spatio-temporal covariance models have been implemented, such …

Importance of target-oriented validation strategies for spatio-temporal prediction models is
illustrated using two case studies:(1) modelling of air temperature (T air) in Antarctica, and …

With the growing recognition that effective action on climate change will require a
combination of emissions reductions and carbon sequestration, protecting, enhancing and …

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 …

SOC is the most important indicator of soil fertility and monitoring its space-time changes is a
prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we …

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 …