Preprints
https://doi.org/10.5194/essd-2023-536
https://doi.org/10.5194/essd-2023-536
06 Feb 2024
 | 06 Feb 2024
Status: this preprint is currently under review for the journal ESSD.

GMIE-100: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method

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

Abstract. Irrigation stands as the primary sector of human water consumption and plays a pivotal role in enhancing crop yields and mitigating drought effects. The precise distribution of irrigation is crucial for effective water resource management and the assessment of food security. However, the existing global irrigated cropland map is characterized by a coarse resolution, typically around 10 kilometers, and is often not regularly updated. In our study, we present a robust methodology that leverages irrigation performance during drought stress as an indicator of crop productivity and water consumption to identify global irrigated cropland. Within each irrigation mapping zone (IMZ), we identified the dry months occurring during the growing season from 2017 to 2019 or the driest months from 2010 to 2019. To delineate irrigated cropland, we utilized collected samples to calculate normalized difference vegetation index (NDVI) thresholds for the dry months of 2017 to 2019 and the NDVI deviation from the ten-year average for the driest month. By combining the results with the higher accuracy between these two methods, we generated the Global Maximum Irrigation Extent dataset at 100-meter resolution (GMIE-100), achieving an overall accuracy of 83.6 %. GMIE-100 reveals that the maximum extent of irrigated cropland encompasses 403.17 million hectares, accounting for 23.4 % of the global cropland. Concentrated in fertile plains and regions adjacent to major rivers, the largest irrigated cropland area is found in India, followed by China, the United States, and Pakistan, ranking 2nd to 4th, respectively. Importantly, the spatial resolution of GMIE-100, at 100 meters, surpasses that of the dominant irrigation map, offering more detailed information essential for supporting estimates of agricultural water use and regional food security assessments. Furthermore, with the help of the DL method, the global central pivot irrigation system (CPIS) was identified using Pivot-Net. We found that there are 11.5 million hectares of CPIS, accounting for about 2.9 % of total irrigated cropland. In Namibia, the US, Saudi Arabia, South Africa, Canada, and Zambia, the CPIS proportion was larger than 10 %. To our best knowledge, this is the first effort to identify irrigation methods globally. The GMIE-100 dataset containing both or irrigated extent and CPIS distribution is accessible on Harvard Dataverse at: https://doi.org/10.7910/DVN/HKBAQQ (Tian et al., 2023a).

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

Status: open (until 14 Mar 2024)

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  • RC1: 'Comment on essd-2023-536', Anonymous Referee #1, 21 Feb 2024 reply
  • RC2: 'Comment on essd-2023-536', Anonymous Referee #2, 22 Feb 2024 reply
  • RC3: 'Comment on essd-2023-536', Anonymous Referee #3, 26 Feb 2024 reply
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li

Data sets

GMIE: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, and Yuming Lu https://doi.org/10.7910/DVN/HKBAQQ

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

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Short summary
Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
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