Comparison of two synergy approaches for hybrid cropland mapping
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...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2019
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/146615 |
| _version_ | 1855527064900206592 |
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| author | Chen, Di Lu, Miao Zhou, Qingbo Xiao, Jingfeng Ru, Yating Wei, Yanbing Wu, Wenbin |
| author_browse | Chen, Di Lu, Miao Ru, Yating Wei, Yanbing Wu, Wenbin Xiao, Jingfeng Zhou, Qingbo |
| author_facet | Chen, Di Lu, Miao Zhou, Qingbo Xiao, Jingfeng Ru, Yating Wei, Yanbing Wu, Wenbin |
| author_sort | Chen, Di |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace146615 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1466152025-12-08T10:29:22Z Comparison of two synergy approaches for hybrid cropland mapping Chen, Di Lu, Miao Zhou, Qingbo Xiao, Jingfeng Ru, Yating Wei, Yanbing Wu, Wenbin spatial data data fusion land-use mapping regression analysis remote sensing satellite observation cartography farmland satellite imagery cultivated land synergism 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. 2019-01-30 2024-06-21T09:07:46Z 2024-06-21T09:07:46Z Journal Article https://hdl.handle.net/10568/146615 en Open Access MDPI 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 |
| spellingShingle | spatial data data fusion land-use mapping regression analysis remote sensing satellite observation cartography farmland satellite imagery cultivated land synergism Chen, Di Lu, Miao Zhou, Qingbo Xiao, Jingfeng Ru, Yating Wei, Yanbing Wu, Wenbin Comparison of two synergy approaches for hybrid cropland mapping |
| title | Comparison of two synergy approaches for hybrid cropland mapping |
| title_full | Comparison of two synergy approaches for hybrid cropland mapping |
| title_fullStr | Comparison of two synergy approaches for hybrid cropland mapping |
| title_full_unstemmed | Comparison of two synergy approaches for hybrid cropland mapping |
| title_short | Comparison of two synergy approaches for hybrid cropland mapping |
| title_sort | comparison of two synergy approaches for hybrid cropland mapping |
| topic | spatial data data fusion land-use mapping regression analysis remote sensing satellite observation cartography farmland satellite imagery cultivated land synergism |
| url | https://hdl.handle.net/10568/146615 |
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