Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Liangzhi You
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
International Food Policy Research Institute (IFPRI), Washington, DC,
20005-3915, USA
International Institute for Applied Systems Analysis, ESM, Laxenburg,
2361, Austria
Qiangyi Yu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Yanbing Wei
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Di Chen
Institute of Environment and Sustainable Development in Agriculture,
Chinese Academy of Agricultural Sciences, Beijing, 100081, China
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Bing Xue
School of Engineering and Computer Science, Victoria University of
Wellington, Wellington, 6140, New Zealand
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6,019
1,728
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7,917
201
260
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PDF: 1,728
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Total: 7,917
BibTeX: 201
EndNote: 260
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Cumulative views and downloads
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Total article views: 6,981 (including HTML, PDF, and XML)
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5,539
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120
6,981
154
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PDF: 1,322
XML: 120
Total: 6,981
BibTeX: 154
EndNote: 214
Views and downloads (calculated since 28 Aug 2020)
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Total article views: 936 (including HTML, PDF, and XML)
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480
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936
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Viewed (geographical distribution)
Total article views: 7,917 (including HTML, PDF, and XML)
Thereof 7,181 with geography defined
and 736 with unknown origin.
Total article views: 6,981 (including HTML, PDF, and XML)
Thereof 6,334 with geography defined
and 647 with unknown origin.
Total article views: 936 (including HTML, PDF, and XML)
Thereof 847 with geography defined
and 89 with unknown origin.
Global cropland distribution is critical for agricultural monitoring and food security. 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 cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
Global cropland distribution is critical for agricultural monitoring and food security. We...
