Proximally sensed digital data library to predict topsoil clay across multiple sugarcane fields of Australia: Applicability of local and universal support vector machine-Reference-Cited by-同舟云学术

Proximally sensed digital data library to predict topsoil clay across multiple sugarcane fields of Australia: Applicability of local and universal support vector machine

Published:2021-01 Issue: Volume:196 Page:104934
ISSN:0341-8162
Container-title:CATENA
language:en
Short-container-title:CATENA

Author:

Arshad Maryem,Zhao Dongxue,Zare Ehsan,Sefton Michael,Triantafilis John^ORCID

Funder

Sugar Research Australia

Publisher

Elsevier BV

Subject

Earth-Surface Processes

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3. Soil texture classification using multi class support vector machine;Barman;Inform. Process. Agric.,2019

4. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel;Ben-Dor;Int. J. Remote Sens.,2002

5. The use of electromagnetic measurements of apparent soil electrical conductivity to predict the boulder clay depth;Brus;Geoderma,1992

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