Irrigation accounts for the major form of human water consumption and plays a pivotal role in enhancing crop yields and mitigating the effects of drought. Accurate mapping of irrigation distribution is essential for effective water resource management and the assessment of food security. However, the resolution of the global irrigated cropland map is coarse, typically approximately 10 km, and it lacks regular updates. 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 of the growing season from 2017 to 2019 or the driest months from 2010 to 2019. To delineate irrigated cropland, we utilized the collected samples to calculate normalized difference vegetation index (NDVI) thresholds for the dry months of 2017 to 2019 and the NDVI deviation from the 10-year average for the driest month. By integrating the most accurate results from these two methods, we generated the Global Maximum Irrigation Extent dataset at 100 m resolution (GMIE-100), achieving an overall accuracy of 83.6 % ± 0.6 %. The GMIE-100 reveals that the maximum extent of irrigated cropland encompasses 403.17 ± 9.82 Mha, accounting for 23.4 % ± 0.6 % of the global cropland. Concentrated in fertile plains and regions adjacent to major rivers, the largest irrigated cropland areas are found in India, China, the United States, and Pakistan, which rank first to fourth, respectively. Importantly, the spatial resolution of GMIE-100 surpasses that of the dominant irrigation map, offering more detailed information essential to support estimates of agricultural water use and regional food security assessments. Furthermore, with the help of the deep learning (DL) method, the global central pivot irrigation system (CPIS) was identified using Pivot-Net, a novel convolutional neural network built on the U-net architecture. We found that there is 11.5 ± 0.01 Mha of CPIS, accounting for approximately 2.90 % ± 0.03 % of the total irrigated cropland. In Namibia, the United States, Saudi Arabia, South Africa, Canada, and Zambia, the CPIS proportion was greater than 10 %. To our knowledge, this is the inaugural study to undertake a global identification of specific irrigation methods, with a focus on the CPIS.
Taking inspiration from the fundamental purpose of irrigation, our aim is to identify periods of drought stress to highlight disparities in crop conditions between irrigated and rainfed croplands. We began by utilizing the 65 monitoring and reporting units (MRUs) established by CropWatch. These MRUs, which account for factors such as crop types, agricultural potential, and environmental conditions, served as the foundation for dividing global cropland into 110 irrigation mapping zones (IMZs). The first-level 65 agroecological zones provide a broad global overview. To address limitations in representing water stress and irrigation within zones, we introduced a more detailed classification, creating second-level agroecological zones based on arid indices, water availability, soil types, and landforms. Ultimately, we utilized 110 IMZs as the foundational units for determining the specific timing of drought stress, as illustrated in Fig. 1. This comprehensive approach enabled us to capture and amplify the distinctions in crop conditions between irrigated and rainfed croplands. Irrigated cropland is defined as agricultural land that benefits from human interventions and is equipped with irrigation infrastructure, including facilities like canals and central pivot systems.
The study was inspired by the purpose of irrigation, i.e. that it mitigates the effect of water stress. Basically, we assume that water stress can be regular or irregular. If there are crops during the dry season, the irrigation should occur regularly. Otherwise, irrigation is just complementary to rainfall in extremely dry years, which means irrigation is irregular. For regular irrigation, we could detect vegetation signal in the dry season (DM-NDVI) when precipitation cannot meet water demand for crops. For irregular irrigation, we compare the NDVI in extremely dry years to the 10-year average level and calculate the deviation (NDVIdev) to determine whether it is irrigated or not. To determine whether a region has regular or irregular irrigation, we used both of these indicators and chose the method with the higher accuracy.Then, with the support of the DL model, a CPIS identification model focused on circular shapes was trained and applied to the entire world to generate global CPIS distribution data. The extent of the CPIS was recognized as the extent of irrigation used to update the global extent of irrigation. Finally, we estimated the proportion of irrigation types of CPIS within irrigated cropland.
The data quality is good.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.DVN.DB6835.2025
10.7910/DVN/HKBAQQ
