Abstract Machine-learning is the automated process of uncovering patterns in large datasets
using computer-based statistical models, where a fitted model may then be used for …

Peatlands offer a series of ecosystem services including carbon storage, biomass
production, and climate regulation. Climate change and rapid land use change are …

For decades scientists have produced maps of biological, ecological and environmental
variables. These studies commonly evaluate the map accuracy through cross-validation with …

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 …

Spatial prediction of soil organic matter is a global challenge and of particular importance for
regions with intensive land use and where availability of soil data is limited. This study …

The increase in digital soil mapping around the world means that appropriate and efficient
sampling strategies are needed for validation. Data used for calibrating a digital soil …

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 …

The purpose of the study is to map the spatial variation of major soil properties in
Bukkarayasamudrum mandal of Anantapur district, India using Random Forest model. The …

In the context of major global environmental challenges such as food security, climate
change, fresh water scarcity and biodiversity loss, the protection and the sustainable …

The efficiency of different digital and conventional soil mapping approaches to produce
categorical maps of soil types is determined by cost, sample size, accuracy and the selected …