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Comparison of two synergy approaches for hybrid cropland mapping

By Di Chen and Yating Ru


Cropland 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.IFPRI3; ISI; DCA; 1 Fostering Climate-Resilient and Sustainable Food SupplyEPTDP

Topics: land use mapping; satellite observation; remote sensing; satellite imagery; farmland; cultivated land; spatial data, data fusion; cropland mapping; synergy map; geographically weighted regression; modified fuzzy agreement scoring
Publisher: 'MDPI AG'
Year: 2019
DOI identifier: 10.3390/rs11030213
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