Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8670
DC FieldValueLanguage
dc.contributor.authorZhang, Jingxiong-
dc.contributor.authorKyriakidis, Phaedon-
dc.contributor.authorKelly, Richard-
dc.date.accessioned2016-07-13T11:44:19Z-
dc.date.available2016-07-13T11:44:19Z-
dc.date.issued2009-09-
dc.identifier.citationInternational Journal of Remote Sensing, 2009, vol. 30, no. 20, pp. 5441-5451en_US
dc.identifier.issn13665901-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8670-
dc.description.abstractInformation on snow cover extent and mass is important for characterization of hydrological systems at different spatial and temporal scales, and for effective water resources management. This paper explores geostatistics for conflation of ground-measured and passive microwave remotely sensed snow data, here referred to as primary and secondary data, respectively. A modification to conventional cokriging is proposed, which first estimates differenced local means between sparsely distributed primary data and densely sampled secondary data by cokriging, followed by a best linear estimation of the primary variable based on the primary data and bias-corrected secondary data, with variogram models revised in the light of corrections made to the original secondary data. An experiment was carried out with snow depth (SD) data derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument and the World Meteorological Organization (WMO) SD measurement, confirming the effectiveness of the proposed methodology.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Remote Sensingen_US
dc.rights© Taylor & Francisen_US
dc.subjectHydrological systemsen_US
dc.subjectEffective water resources managementen_US
dc.subjectGround-measureden_US
dc.subjectMicrowave remotely sensed snow dataen_US
dc.titleGeostatistical approaches to conflation of continental snow dataen_US
dc.typeArticleen_US
dc.collaborationWuhan Universityen_US
dc.collaborationUniversity of Californiaen_US
dc.collaborationUniversity of Waterlooen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryUnited Statesen_US
dc.countryChinaen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1080/01431160903130960en_US
dc.dept.handle123456789/54en
dc.relation.issue20en_US
dc.relation.volume30en_US
cut.common.academicyear2009-2010en_US
dc.identifier.spage5441en_US
dc.identifier.epage5451en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1366-5901-
crisitem.journal.publisherTaylor & Francis-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4222-8567-
crisitem.author.parentorgFaculty of Engineering and Technology-
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