Comparison of two synergy approaches for hybrid cropland mapping

Chen, 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

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.