Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context

By Clovis Grinand, Dominique Arrouays, B. Laroche, Manuel P. Martin. 2008

196 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/be8b2f5b0cd150c72d403f731e8bfd499d00d6ec

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Citations in the corpus

Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils (2012, methodology): “In addition, we used different window sizes (8×8, 15×15) to analyze the effect of scales and spatial context (Grinand et al., 2008; Smith et al., 2006).”
Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach (2011, background): “…2009) and soil landscape Please cite this article as: Razakamanarivo, R.H., et al., Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach, Geoderma (2011), doi:10.1016/j.geoderma.2011.03.006 prediction (Grinand et al., 2008).”
Multi-scale digital terrain analysis and feature selection for digital soil mapping (2010, methodology): “A multi-scale digital terrain analysis approach based on multiple local average filters (Behrens et al., 2005; Grinand et al., 2008) is proposed, which produces separate hierarchical spatial components (McBratney et al.”
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): “The predictive soil distribution models can be generated on the basis of the relationships between soil and relief classes and geomorphological units (Grinand et al., 2008).”
Multi-criteria characterization of recent digital soil mapping and modeling approaches (2009, methodology): “S and O factors were utilized by West et al. (2008); and S and R factors by Santra and Das (2008)), while other studies (e.g. Jasques et al., 2008; Grinand et al., 2008) incorporated a more comprehensive set of SCORPAN factors.”
Machine learning for predicting soil classes in three semi-arid landscapes (2015, background): “The first approach attempts to find and extract soil class-landscape relationships from existing 83 digitized soil polygon maps when the exact locations (GPS coordinates) of soil pedon observations are 84 unknown (Behrens et al., 2005; Grinand et al., 2008; Subburayalu and Slater, 2013).”
Sampling for validation of digital soil maps (2011, background): “© 2011 Alterra, Wageningen University and Research Centre Journal compilation © 2011 British Society of Soil Science, European Journal of Soil Science, 62, 394–407 A recent example on validation of a soil map by data-splitting is given by Grinand et al. (2008).”
Digital soil mapping: A brief history and some lessons (2016, background): “Rules can be learned from the soil map, and subsequently these learned rules can be used to spatially extrapolate the mapping areas (Grinand et al., 2008), update or refine existing maps (Collard et al.”
Estimating forest soil bulk density using boosted regression modelling (2010, background): “The biochemistry of tropical soils is obviously different from temperate soils (Trumbore, 1993; Grisi et al., 1998; Six et al., 2002).”
Predictive soil parent material mapping at a regional-scale: a Random Forest approach. (2014, methodology): “Decision trees have been used extensively to map soil classes (Bui and Moran, 2001, 2003; Bui et al., 1999; Grinand et al., 2008; Moran and Bui, 2002; Scull et al., 2005); soil properties such as pH (Henderson et al.”