Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19216
DC FieldValueLanguage
dc.contributor.authorKim, Yeseul-
dc.contributor.authorKyriakidis, Phaedon-
dc.contributor.authorPark, No Wook-
dc.date.accessioned2020-10-20T10:03:24Z-
dc.date.available2020-10-20T10:03:24Z-
dc.date.issued2020-05-01-
dc.identifier.citationRemote Sensing, 2020, vol. 12, iss. 10, article no. 1553en_US
dc.identifier.issn2072-4292-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19216-
dc.description.abstractDense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relationERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environmenten_US
dc.relation.ispartofRemote Sensingen_US
dc.rights© by the authorsen_US
dc.subjectDeconvolutionen_US
dc.subjectResolutionen_US
dc.subjectSpatiotemporal data fusionen_US
dc.subjectTemporal informationen_US
dc.titleA cross-resolution, spatiotemporal geostatistical fusion model for combining satellite image time-series of different spatial and temporal resolutionsen_US
dc.typeArticleen_US
dc.collaborationInha Universityen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationERATOSTHENES Centre of Excellenceen_US
dc.collaborationGeospatial Analytics Laben_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsOpen Accessen_US
dc.countrySouth Koreaen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/rs12101553en_US
dc.identifier.scopus2-s2.0-85085564512en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85085564512en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.issue10en_US
dc.relation.volume12en_US
cut.common.academicyear2019-2020en_US
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.journal.journalissn2072-4292-
crisitem.journal.publisherMDPI-
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-
crisitem.project.funderEuropean Commission-
crisitem.project.grantnoH2020-WIDESPREAD-2018-01 / WIDESPREAD-01-2018-2019 Teaming Phase 2-
crisitem.project.fundingProgramH2020 Spreading Excellence, Widening Participation, Science with and for Society-
crisitem.project.openAireinfo:eu-repo/grantAgreeent/EC/H2020/857510-
Appears in Collections:Publications under the auspices of the EXCELSIOR H2020 Teaming Project/ERATOSTHENES Centre of Excellence
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