Prediction of lead in agricultural soils : an integrated approach using machine learning, terrain attributes and reflectance spectra

dc.contributor.author	Kebonye, Ndiye Michael
dc.date.accessioned	2026-08-10T15:19:35Z
dc.date.available	2026-08-10T15:19:35Z
dc.date.issued	2025
dc.identifier.issn	1002-0160
dc.identifier.uri	http://hdl.handle.net/10900/182140
dc.language.iso	en	de_DE
dc.publisher	Beijing : Science Press	de_DE
dc.relation.uri	http://dx.doi.org/10.1016/j.pedsph.2024.01.002	de_DE
dc.subject.ddc	550	de_DE
dc.title	Prediction of lead in agricultural soils : an integrated approach using machine learning, terrain attributes and reflectance spectra	de_DE
dc.type	Article	de_DE
utue.quellen.id	20250630000000_01142
utue.publikation.seiten	325-337	de_DE
utue.personen.roh	Agyeman, Prince Chapman
utue.personen.roh	John, Kingsley
utue.personen.roh	Kebonye, Ndiye Michael
utue.personen.roh	Khosravi, Vahid
utue.personen.roh	Boruvka, Lubos
utue.personen.roh	Vasat, Radim
utue.personen.roh	Boateng, Charles Mario
dcterms.isPartOf.ZSTitelID	Pedosphere	de_DE
dcterms.isPartOf.ZS-Issue	2	de_DE
dcterms.isPartOf.ZS-Volume	35	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät	de_DE