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 map** (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …

For many decades, kriging and deterministic interpolation techniques, such as inverse
distance weighting and nearest neighbour interpolation, have been the most popular spatial …

Geostatistics and machine learning have been extensively applied for modelling and
predicting the spatial distribution of continuous soil variables. In addition to providing …

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for
supporting national and international policies aimed at achieving land degradation neutrality …

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

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils
are a finite resource that is under threat, mainly due to human pressure. There is an urgent …

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 …

Spatial soil information is essential for informed decision-making in a wide range of fields.
Digital soil map** (DSM) using machine learning algorithms has become a popular …

Site-specific estimation of lime requirement requires high-resolution maps of soil organic
carbon (SOC), clay and pH. These maps can be generated with digital soil map** models …