Articles | Volume 17, issue 3
https://doi.org/10.5194/essd-17-855-2025
© Author(s) 2025. 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-17-855-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
06 Mar 2025
Data description article |
| 06 Mar 2025
| 06 Mar 2025
GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
Fuyou Tian
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Bingfang Wu
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Hongwei Zeng
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Miao Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Weiwei Zhu
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Nana Yan
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yuming Lu
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yifan Li
School of Computer Science, China University of Geosciences, Wuhan 430078, China
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Related authors
Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-346, https://doi.org/10.5194/essd-2023-346, 2023
Preprint withdrawn
Short summary
Short summary
We developed the first time-series gridded dataset of maize, wheat, rice and soybean production from 2010–2020. It offers detailed spatiotemporal information of crop production, allowing for advanced monitoring of agricultural productivity and for research into the factors that drive production trends across regions and over time. This dataset can help assess food security, evaluate climate impacts, and inform policies for more sustainable, resilient crop production worldwide.
Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-346, https://doi.org/10.5194/essd-2023-346, 2023
Preprint withdrawn
Short summary
Short summary
We developed the first time-series gridded dataset of maize, wheat, rice and soybean production from 2010–2020. It offers detailed spatiotemporal information of crop production, allowing for advanced monitoring of agricultural productivity and for research into the factors that drive production trends across regions and over time. This dataset can help assess food security, evaluate climate impacts, and inform policies for more sustainable, resilient crop production worldwide.
Miao Zhang, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang, Mohsen Nabil, Fuyou Tian, José Bofana, Awetahegn Niguse Beyene, Abdelrazek Elnashar, Nana Yan, Zhengdong Wang, and Yiliang Liu
Earth Syst. Sci. Data, 13, 4799–4817, https://doi.org/10.5194/essd-13-4799-2021, https://doi.org/10.5194/essd-13-4799-2021, 2021
Short summary
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.
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Short summary
Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Our study introduces GMIE, a high-resolution global map of irrigated cropland at...
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