GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and  deep learning methods

Tian, Fuyou; Wu, Bingfang; Zeng, Hongwei; Zhang, Miao; Zhu, Weiwei; Yan, Nana; Lu, Yuming; Li, Yifan

doi:https://doi.org/10.5194/essd-17-855-2025

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.

https://doi.org/10.5194/essd-17-855-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 3

Data description paper

|

06 Mar 2025

Data description paper |

| 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, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li

Supplement

https://doi.org/10.5194/essd-17-855-2025-supplement

Data sets

GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived through irrigation performance during drought stress and machine learning method Fuyou Tian et al. https://doi.org/10.7910/DVN/HKBAQQ

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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.