Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79117
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorZhu, X-
dc.creatorCai, F-
dc.creatorTian, J-
dc.creatorWilliams, TKA-
dc.date.accessioned2018-10-30T03:01:29Z-
dc.date.available2018-10-30T03:01:29Z-
dc.identifier.urihttp://hdl.handle.net/10397/79117-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Zhu, X., Cai, F., Tian, J., & Williams, T. K. A. (2018). Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10(4), 527 is available at https://doi.org/10.3390/rs10040527en_US
dc.subjectData blendingen_US
dc.subjectSatellite imagesen_US
dc.subjectSpatial resolutionen_US
dc.subjectSpatiotemporal data fusionen_US
dc.subjectTemporal resolutionen_US
dc.titleSpatiotemporal fusion of multisource remote sensing data : literature survey, taxonomy, principles, applications, and future directionsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume10-
dc.identifier.issue4-
dc.identifier.doi10.3390/rs10040527-
dcterms.abstractSatellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, 2018, v. 10, no. 4, 527-
dcterms.isPartOfRemote sensing-
dcterms.issued2018-
dc.identifier.scopus2-s2.0-85045984906-
dc.identifier.eissn2072-4292-
dc.identifier.artn527-
dc.identifier.rosgroupid2017004418-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201810 bcma-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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