Spatial modelling with Euclidean distance fields and machine learning-Reference-Cited by-同舟云学术

Spatial modelling with Euclidean distance fields and machine learning

Published:2018-07-12 Issue:5 Volume:69 Page:757-770
ISSN:1351-0754
Container-title:European Journal of Soil Science
language:en
Short-container-title:Eur J Soil Sci

Author:

Behrens T.¹^ORCID,Schmidt K.¹,Viscarra Rossel R. A.²^ORCID,Gries P.¹^ORCID,Scholten T.¹,MacMillan R. A.³

Affiliation:

1. Department of Geosciences, Soil Science and Geomorphology; University of Tübingen; Rümelinstraße 19-23, 72074, Tübingen Germany

2. CSIRO Land and Water; GPO Box 1700, Canberra ACT 2601 Australia

3. LandMapper Environmental Solutions Inc.; 7415 118 A Street NW, Edmonton Alberta Canada

Funder

Deutsche Forschungsgemeinschaft

Publisher

Wiley

Subject

Soil Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/ejss.12687/fullpdf

Reference46 articles.

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2. Digital soil mapping using artificial neural networks;Behrens;Journal of Plant Nutrition and Soil Science,2005

3. The ConMap approach for terrain based digital soil mapping;Behrens;European Journal of Soil Science,2010

4. Hyper-scale digital soil mapping and soil formation analysis;Behrens;Geoderma,2014

5. Multiscale contextual spatial modelling with the Gaussian scale space;Behrens;Geoderma,2018

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