A cultivated planet in 2010 - Part 1: The global synergy cropland map
Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Copernicus GmbH
2020
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| Acceso en línea: | https://hdl.handle.net/10568/110739 |
| _version_ | 1855518688691617792 |
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| author | Lu, Miao Wu, Wenbin You, Liangzhi See, Linda Fritz, Steffen Yu, Qiangyi Wei, Yanbing Chen, Di Yang, Peng Xue, Bing |
| author_browse | Chen, Di Fritz, Steffen Lu, Miao See, Linda Wei, Yanbing Wu, Wenbin Xue, Bing Yang, Peng You, Liangzhi Yu, Qiangyi |
| author_facet | Lu, Miao Wu, Wenbin You, Liangzhi See, Linda Fritz, Steffen Yu, Qiangyi Wei, Yanbing Chen, Di Yang, Peng Xue, Bing |
| author_sort | Lu, Miao |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of the cropland area, which is independent of training samples. First, cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets. Secondly, statistics are allocated adaptively to the pixels with higher agreement scores until the cumulative cropland area is close to the statistics. The multilevel allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a 500 m spatial resolution for circa 2010. The accuracy assessments show that the synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics. The synergy cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map has been used as an essential input to the Spatial Production Allocation Model (SPAM) for producing the global dataset of agricultural production for circa 2010, which is described in the second part of the two-paper series. |
| format | Journal Article |
| id | CGSpace110739 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Copernicus GmbH |
| publisherStr | Copernicus GmbH |
| record_format | dspace |
| spelling | CGSpace1107392025-02-24T06:48:47Z A cultivated planet in 2010 - Part 1: The global synergy cropland map Lu, Miao Wu, Wenbin You, Liangzhi See, Linda Fritz, Steffen Yu, Qiangyi Wei, Yanbing Chen, Di Yang, Peng Xue, Bing spatial data models agricultural production crops capacity development farmland satellite imagery Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of the cropland area, which is independent of training samples. First, cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets. Secondly, statistics are allocated adaptively to the pixels with higher agreement scores until the cumulative cropland area is close to the statistics. The multilevel allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a 500 m spatial resolution for circa 2010. The accuracy assessments show that the synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics. The synergy cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map has been used as an essential input to the Spatial Production Allocation Model (SPAM) for producing the global dataset of agricultural production for circa 2010, which is described in the second part of the two-paper series. 2020-08-28 2021-01-07T06:11:18Z 2021-01-07T06:11:18Z Journal Article https://hdl.handle.net/10568/110739 en https://doi.org/10.5194/essd-12-3545-2020 Open Access Copernicus GmbH Lu, Miao; Wu, Wenbin; You, Liangzhi; See, Linda; Fritz, Steffen; Yu, Qiangyi; Wei, Yanbing; Chen, Di; Yang, Peng; Xue, Bing. 2020. A cultivated planet in 2010 - Part 1: The global synergy cropland map. Earth System Science Data. 12(3):1913-1928. https://doi.org/10.5194/essd-12-1913-2020 |
| spellingShingle | spatial data models agricultural production crops capacity development farmland satellite imagery Lu, Miao Wu, Wenbin You, Liangzhi See, Linda Fritz, Steffen Yu, Qiangyi Wei, Yanbing Chen, Di Yang, Peng Xue, Bing A cultivated planet in 2010 - Part 1: The global synergy cropland map |
| title | A cultivated planet in 2010 - Part 1: The global synergy cropland map |
| title_full | A cultivated planet in 2010 - Part 1: The global synergy cropland map |
| title_fullStr | A cultivated planet in 2010 - Part 1: The global synergy cropland map |
| title_full_unstemmed | A cultivated planet in 2010 - Part 1: The global synergy cropland map |
| title_short | A cultivated planet in 2010 - Part 1: The global synergy cropland map |
| title_sort | cultivated planet in 2010 part 1 the global synergy cropland map |
| topic | spatial data models agricultural production crops capacity development farmland satellite imagery |
| url | https://hdl.handle.net/10568/110739 |
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