Articles | Volume 13, issue 10
https://doi.org/10.5194/essd-13-4799-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4799-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Oct 2021
Data description paper |
| 21 Oct 2021
| 21 Oct 2021
GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery
Miao Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
Bingfang Wu
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Hongwei Zeng
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Guojin He
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Chong Liu
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, 510275, PR China
Shiqi Tao
Graduate School of Geography, Clark University, Worcester, MA 01610, USA
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Frederick S. Pardee Center for the Study of Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA 02215, USA
Mohsen Nabil
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Division of Agriculture Applications, Soils, and Marine (AASMD), National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, New Nozha, Alf Maskan, 1564, Egypt
Fuyou Tian
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
José Bofana
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Center for Agricultural and Sustainable Development Research (CIADS), Faculty of Agricultural Sciences, Catholic University of Mozambique, Cuamba 3305, Mozambique
Awetahegn Niguse Beyene
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Tigray Agricultural Research Institute, P.O. Box 492, Mekelle 251, Ethiopia
Abdelrazek Elnashar
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt
Nana Yan
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
Zhengdong Wang
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Yiliang Liu
National Remote Sensing Center of China, Beijing 100036, PR China
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Short summary
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution global CI is not available. We used multiple satellite data on Google Earth Engine to develop a first 30 m resolution global CI (GCI30). GCI30 performed well, with an overall accuracy of 92 %. GCI30 not only exhibited high agreement with existing CI products but also provided many spatial details. GCI30 can facilitate research on sustained cropland intensification to improve food production.
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution...
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