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Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/50308
DC FieldValueLanguage
dc.contributor.authorHanif, Fatimait
dc.contributor.authorKanae, Shinjiroit
dc.contributor.authorFarooq, Rashidit
dc.contributor.authorIqbal, M. Rashidit
dc.contributor.authorPetroselli, Andreait
dc.date.accessioned2023-11-13T06:27:46Z-
dc.date.available2023-11-13T06:27:46Z-
dc.date.issued2023it
dc.identifier.issn2072-4292it
dc.identifier.urihttp://hdl.handle.net/2067/50308-
dc.format.mediumELETTRONICOit
dc.language.isoengit
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleImpact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulationsit
dc.typearticle*
dc.identifier.doihttps://doi.org/10.3390/rs15225338it
dc.identifier.urlhttps://www.mdpi.com/2072-4292/15/22/5338it
dc.relation.journalREMOTE SENSINGit
dc.relation.numberofpages17it
dc.relation.article5338it
dc.relation.volume15it
dc.relation.issue22it
dc.description.numberofauthors5it
dc.description.internationalsìit
dc.contributor.countryPAKit
dc.type.refereeREF_1it
dc.type.miur262*
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.grantfulltextrestricted-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.journal.journalissn2072-4292-
crisitem.journal.anceE199973-
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