GMIA-NEXT: Next-Generation Global Map of Irrigated Areas

GMIA-NEXT is a medium-resolution (30 m) global irrigation dataset that maps irrigated croplands for the 2023–2024 growing season. The dataset includes both irrigation probability and binary irrigation maps derived from multi-source Earth observation and environmental datasets using machine learning techniques. The associated ground-truth data used for model development and validation are available in a separate repository (https://doi.org/10.5281/zen… ).

Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here we integrate multi-source Earth observation and environmental datasets and use machine learning to develop a medium-resolution (30 m) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information, hydroclimatic variables (e.g., evapotranspiration and proximity to water bodies), and topographic variables (e.g., slope). We subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%, while the resulting maps were further validated against independent global and national irrigation datasets and statistics. demonstrating broad agreement in the spatial distribution of irrigated areas This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management.