Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture
Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture
About this item
Full title
Author / Creator
Maino, Andrea , Alberi, Matteo , Anceschi, Emiliano , Chiarelli, Enrico , Cicala, Luca , Colonna, Tommaso , De Cesare, Mario , Guastaldi, Enrico , Lopane, Nicola , Mantovani, Fabio , Marcialis, Maurizio , Martini, Nicola , Montuschi, Michele , Piccioli, Silvia , Raptis, Kassandra Giulia Cristina , Russo, Antonio , Semenza, Filippo and Strati, Virginia
Publisher
Basel: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Basel: MDPI AG
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More information
Scope and Contents
Contents
Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km2 agricultura...
Alternative Titles
Full title
Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture
Authors, Artists and Contributors
Author / Creator
Alberi, Matteo
Anceschi, Emiliano
Chiarelli, Enrico
Cicala, Luca
Colonna, Tommaso
De Cesare, Mario
Guastaldi, Enrico
Lopane, Nicola
Mantovani, Fabio
Marcialis, Maurizio
Martini, Nicola
Montuschi, Michele
Piccioli, Silvia
Raptis, Kassandra Giulia Cristina
Russo, Antonio
Semenza, Filippo
Strati, Virginia
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_6cc1994c954d4b29aac2531480b5cbca
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_6cc1994c954d4b29aac2531480b5cbca
Other Identifiers
ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs14153814
