Using model averaging to combine soil property rasters from legacy soil maps and from point data

By Brendan P. Malone, Budiman Minasny, Nathan P. Odgers, Alex B. McBratney. 2014

139 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/4c393f36a5bad07021c1c561a8e7a538b9764385

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Alex B. McBratney
2. Budiman Minasny
3. C. Brungard
4. THOMAS C. EDWARDS JR.
5. J. Boettinger
6. Michael C. Duniway
7. S. Wills
8. Attila Nemes

In Environmental Engineering

1. Budiman Minasny
2. Alex B. McBratney
3. C. Brungard
4. S. Wills
5. THOMAS C. EDWARDS JR.
6. J. Boettinger
7. Michael C. Duniway
8. Sabine Grunwald

In Soil Science

1. Alex B. McBratney
2. Budiman Minasny
3. C. Brungard
4. S. Wills
5. THOMAS C. EDWARDS JR.
6. J. Boettinger
7. Michael C. Duniway
8. Tomislav Hengl

Citations in the corpus

• Machine learning for predicting soil classes in three semi-arid landscapes (2015, methodology): “If multiple models are applied for a digital soil 442 mapping project and accuracy metrics are approximately equivalent between models, then model 443 averaging (Malone et al., 2014) may be appropriate.”
• Digital soil mapping: A brief history and some lessons (2016, background): “In addition the disaggregated maps and scorpankriging derived maps can be combined to produce better estimates (Malone et al., 2014).”