Predictive soil mapping: a review

By Peter Scull, Janet Franklin, Oliver A. Chadwick, D. Mcarthur. 2003

510 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/8800b625ed20acff99d9e3a12c3eed3b9fed9fa0

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In Environmental Engineering

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Citations in the corpus

Soil carbon debt of 12,000 years of human land use (2017, background): “This is the foundation of the current state of the art in predictive soil mapping (27, 48),”
Spatial prediction of USDA‐ great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types (2012, background): “Received 29 May 2011; revised version accepted 8 December 2011 Unfortunately, traditional methods do not yield quantifiable soil-landscape information that describes actual soil variation robustly (Scull et al., 2003).”
Relationships in soil distribution as revealed by a global soil database (2009, background): “Even with all the technical advances in recent years there appears to have been little movement away from conventional mapping approaches by government agencies in the USA (Scull et al., 2003) and in Australia, at least at the state government level.”
On digital soil mapping (2003, background): “An independent review of digital soil mapping under the name of 'predictive' soil mapping has appeared (Scull et al., 2003a,b).”
Digital soil mapping: A brief history and some lessons (2016, methodology): “models (Franklin, 1995; McKenzie and Ryan, 1999; Scull et al., 2003; Legros, 2006; Kempen et al., 2010).”
Selecting appropriate machine learning methods for digital soil mapping (2020, background): “Advancement of statistical approaches and geospatial technologies occurring in the late 20 th and early 21 st century spurred many new tools for digital soil mapping (DSM) [1], [2], [3].”
Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping (2016, unknown): “The coupling is accomplished by regression and classification approaches resulting in continuous or discrete maps of soil properties ( McBratney et al., 2003; Scull et al., 2003; McMillan , 2008).”