Comparison of two synergy approaches for hybrid cropland mapping

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cg.authorship.typesCGIAR single centreen
cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.donorNational Natural Science Foundation of Chinaen
cg.contributor.donorNational Key Research and Development Program, Chinaen
cg.contributor.donorFundamental Research Funds for Central Non-profit Scientific Institutionen
cg.contributor.donorChinese Academy of Agricultural Sciencesen
cg.creator.identifierYating Ru: 0000-0001-9071-0687
cg.identifier.doihttps://doi.org/10.3390/rs11030213en
cg.identifier.projectIFPRI - Environment and Production Technology Division
cg.identifier.publicationRankB
cg.isijournalISI Journalen
cg.issn2072-4292en
cg.issue3en
cg.journalRemote Sensingen
cg.reviewStatusPeer Reviewen
cg.volume11en
dc.contributor.authorChen, Dien
dc.contributor.authorLu, Miaoen
dc.contributor.authorZhou, Qingboen
dc.contributor.authorXiao, Jingfengen
dc.contributor.authorRu, Yatingen
dc.contributor.authorWei, Yanbingen
dc.contributor.authorWu, Wenbinen
dc.date.accessioned2024-06-21T09:07:46Zen
dc.date.available2024-06-21T09:07:46Zen
dc.identifier.urihttps://hdl.handle.net/10568/146615
dc.titleComparison of two synergy approaches for hybrid cropland mappingen
dcterms.abstractCropland maps at regional or global scales typically have large uncertainty and are also inconsistent with each other. The substantial uncertainty in these cropland maps limits their use in research and management efforts. Many synergy approaches have been developed to generate hybrid cropland maps with higher accuracy from existing cropland maps. However, few studies have compared the advantages, disadvantages, and regional suitability of these approaches. To close this knowledge gap, this study aims to compare two representative synergy methods of cropland mapping: Geographically weighted regression (GWR) and modified fuzzy agreement scoring (MFAS). We assessed how the sample size, quality of input satellite-based maps, and various landscapes influence the accuracy of the synergy maps based on these two methods.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationChen, Di; Lu, Miao; Zhou, Qingbo; Xiao, Jingfeng; Ru, Yating; Wei, Yanbing; and Wu, Wenbin. 2019. Comparison of two synergy approaches for hybrid cropland mapping. Remote Sensing 11(3): 213. https://doi.org/10.3390/rs11030213en
dcterms.issued2019-01-30
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherMDPI AGen
dcterms.replaceshttps://ebrary.ifpri.org/digital/collection/p15738coll5/id/6573en
dcterms.subjectspatial dataen
dcterms.subjectdata fusionen
dcterms.subjectland-use mappingen
dcterms.subjectregression analysisen
dcterms.subjectremote sensingen
dcterms.subjectsatellite observationen
dcterms.subjectcartographyen
dcterms.subjectfarmlanden
dcterms.subjectsatellite imageryen
dcterms.subjectcultivated landen
dcterms.subjectsynergismen
dcterms.typeJournal Article

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