A synergy cropland of China by fusing multiple existing maps and statistics
Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/146209 |
| _version_ | 1855525465797689344 |
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| author | Lu, Miao Wu, Wenbin You, Liangzhi Chen, Di Zhang, Li Yang, Peng Tang, Huajun |
| author_browse | Chen, Di Lu, Miao Tang, Huajun Wu, Wenbin Yang, Peng You, Liangzhi Zhang, Li |
| author_facet | Lu, Miao Wu, Wenbin You, Liangzhi Chen, Di Zhang, Li Yang, Peng Tang, Huajun |
| author_sort | Lu, Miao |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ considerably from statistics. In this study, we propose a hierarchical optimization synergy approach (HOSA) to develop a hybrid cropland map of China, circa 2010, by fusing five existing cropland products, i.e., GlobeLand30, Climate Change Initiative Land Cover (CCI-LC), GlobCover 2009, MODIS Collection 5 (MODIS C5), and MODIS Cropland, and sub-national statistics of cropland area. HOSA simplifies the widely used method of score assignment into two steps, including determination of optimal agreement level and identification of the best product combination. The accuracy assessment indicates that the synergy map has higher accuracy of spatial locations and better consistency with statistics than the five existing datasets individually. This suggests that the synergy approach can improve the accuracy of cropland mapping and enhance consistency with statistics. |
| format | Journal Article |
| id | CGSpace146209 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1462092025-02-19T13:42:22Z A synergy cropland of China by fusing multiple existing maps and statistics Lu, Miao Wu, Wenbin You, Liangzhi Chen, Di Zhang, Li Yang, Peng Tang, Huajun data fusion land-use mapping remote sensing cartography farmland land cover mapping synergism statistics Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ considerably from statistics. In this study, we propose a hierarchical optimization synergy approach (HOSA) to develop a hybrid cropland map of China, circa 2010, by fusing five existing cropland products, i.e., GlobeLand30, Climate Change Initiative Land Cover (CCI-LC), GlobCover 2009, MODIS Collection 5 (MODIS C5), and MODIS Cropland, and sub-national statistics of cropland area. HOSA simplifies the widely used method of score assignment into two steps, including determination of optimal agreement level and identification of the best product combination. The accuracy assessment indicates that the synergy map has higher accuracy of spatial locations and better consistency with statistics than the five existing datasets individually. This suggests that the synergy approach can improve the accuracy of cropland mapping and enhance consistency with statistics. 2017 2024-06-21T09:06:11Z 2024-06-21T09:06:11Z Journal Article https://hdl.handle.net/10568/146209 en Open Access MDPI Lu, Miao; Wu, Wenbin; You, Liangzhi; Chen, Di; Zhang, Li; Yang, Peng; and Tang, Huajun. 2017. A synergy cropland of China by fusing multiple existing maps and statistics. Sensors 17(7): 1613. https://doi.org/10.3390/s17071613 |
| spellingShingle | data fusion land-use mapping remote sensing cartography farmland land cover mapping synergism statistics Lu, Miao Wu, Wenbin You, Liangzhi Chen, Di Zhang, Li Yang, Peng Tang, Huajun A synergy cropland of China by fusing multiple existing maps and statistics |
| title | A synergy cropland of China by fusing multiple existing maps and statistics |
| title_full | A synergy cropland of China by fusing multiple existing maps and statistics |
| title_fullStr | A synergy cropland of China by fusing multiple existing maps and statistics |
| title_full_unstemmed | A synergy cropland of China by fusing multiple existing maps and statistics |
| title_short | A synergy cropland of China by fusing multiple existing maps and statistics |
| title_sort | synergy cropland of china by fusing multiple existing maps and statistics |
| topic | data fusion land-use mapping remote sensing cartography farmland land cover mapping synergism statistics |
| url | https://hdl.handle.net/10568/146209 |
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