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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5,828
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7,670
189
248
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PDF: 1,678
XML: 164
Total: 7,670
BibTeX: 189
EndNote: 248
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Cumulative views and downloads
(calculated since 11 Feb 2020)
Total article views: 6,754 (including HTML, PDF, and XML)
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5,359
1,281
114
6,754
143
202
HTML: 5,359
PDF: 1,281
XML: 114
Total: 6,754
BibTeX: 143
EndNote: 202
Views and downloads (calculated since 28 Aug 2020)
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469
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916
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Viewed (geographical distribution)
Total article views: 7,670 (including HTML, PDF, and XML)
Thereof 6,913 with geography defined
and 757 with unknown origin.
Total article views: 6,754 (including HTML, PDF, and XML)
Thereof 6,088 with geography defined
and 666 with unknown origin.
Total article views: 916 (including HTML, PDF, and XML)
Thereof 825 with geography defined
and 91 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...
