Comparison of two synergy approaches for hybrid cropland mapping

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dc.creatorChen, Di
dc.creatorLu, Miao
dc.creatorZhou, Qingbo
dc.creatorXiao, Jingfeng
dc.creatorRu, Yating
dc.creatorWei, Yanbing
dc.creatorWu, Wenbin
dc.date2019-01-30
dc.date2024-06-21T09:07:46Z
dc.date2024-06-21T09:07:46Z
dc.date.accessioned2026-06-27T15:18:59Z
dc.descriptionCropland 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.
dc.identifierhttps://hdl.handle.net/10568/146615
dc.identifier.urihttp://hdl.handle.net/123456789/100926
dc.languageen
dc.publisherMDPI
dc.rightsOpen Access
dc.sourceChen, 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/rs11030213
dc.subjectspatial data
dc.subjectdata fusion
dc.subjectland-use mapping
dc.subjectregression analysis
dc.subjectremote sensing
dc.subjectsatellite observation
dc.subjectcartography
dc.subjectfarmland
dc.subjectsatellite imagery
dc.subjectcultivated land
dc.subjectsynergism
dc.titleComparison of two synergy approaches for hybrid cropland mapping
dc.typeJournal Article

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)