Modelling soil variation: past, present, and future

By Gerard B. M. Heuvelink, Richard Webster. 2001

412 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/301ea774926c1c38ffc0180467f5ebc83885e598

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In Soil Science

Citations in the corpus

Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils (2012, positive): “continuous nature of soil (Heuvelink and Webster, 2001; Webster and Becket, 1968).”
On digital soil mapping (2003, methodology): “Hewitt (1993) pleaded for the need for explicitly stated, but not necessarily quantitative, models for soil survey.”
SoilGrids1km — Global Soil Information Based on Automated Mapping (2014, background): “Low crossvalidation percentages are common in soil mapping [38,55], these numbers were not unexpected.”
Predictive soil mapping: a review (2003, methodology): “…are not included in the table because they have been comprehensively surveyed elsewhere (Odeh et al., 1994; Burrough et al., 1997; McBratney et al., 2000; Heuvelink and Webster, 2001) and because the objectives and assumptions of geostatistical methods differ slightly from other PSM research.”
A generic framework for spatial prediction of soil variables based on regression-kriging (2004, methodology): “A spatial prediction technique, which jointly employs correlation with auxiliary maps and spatial correlation is universal kriging (UK), originally described by Matheron (1969). Many authors (Deutsch and Journel, 1992; Wackernagel, 1998), however, agree that the term Universal kriging should be rese”
A Practical Guide to Geostatistical Mapping (2009, background): “Even the soil variables such as pH, nutrients, water-saturation levels and water content, can vary over a few years, within a single season or even over a few days (Heuvelink and Webster, 2001).”
Spatial prediction of categorical variables: the Bayesian maximum entropy approach (2002, background): “…is of paramount importance for mapping purposes, e.g., land use suitability (Bierkens and Burrough, 1993), land cover (de Bruin, 2000) or soil mapping (Heuvelink and Webster, 2001), that are typically characterized by their discrete nature and the necessity of integrating various data sources.”
Optimization of sample patterns for universal kriging of environmental variables (2007, background): “Geostatisticians and pedometricians have concentrated most on the second stage, by developing and applying various types of kriging algorithms (Goovaerts, 1997; Heuvelink and Webster, 2001).”
Prediction of soil properties using fuzzy membership values (2010, methodology): “Geostatistical methods have also been investigated to take into account the spatial autocorrelation of observed values in field samples (McBratney and Webster, 1986; Odeh et al., 1992, 1994; Odeh and Chittleborough, 1992; McBratney et al., 2000; Heuvelink and Webster, 2001; Hengl et al., 2004).”
Interpolation in Time Series : An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment (2017, methodology): “Transfer function-noise (TFN) modeling is another interpolation method initially developed by [33], e.g., with application in [78].”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, background): “Conventionally, spatial prediction of soil has been embedded in the geostatistical framework (Heuvelink and Webster, 2001) in which a sample of a soil property is modelled as a sum of a linear combination of environmental covariates and a spatially autocorrelated (stochastic) residual, and…”