2025/12/02 by Wanxue Zhu, Elif Dönmez, Hugo Storm +2 · 1 voice
Agricultural and Biological Sciences · Environmental Science · Computer Science · #Irrigation Practices and Water Management #Soil and Land Suitability Analysis #Advanced Computational Techniques in Science and Engineering
paper · doi:10.1016/j.agwat.2025.110037
openalex created_date 2025/12/02 · openalex publication_date 2025/12/02 · openalex updated_date 2026/07/25
Robust mapping of large-scale irrigation area is critical for sustainable water management and climate-resilient agriculture. However, a comprehensive synthesis of the conceptual foundations, inherent limitations, comparative strengths, and developmental trends of existing methodologies and datasets remains lacking, hindering methodological advancement and context-appropriate dataset selection. To bridge this gap, we (i) take stock of global and regional irrigation area datasets, elucidating their methodological frameworks, characteristics, interrelations, and current spatial–temporal–thematic gaps; (ii) benchmark ten datasets across Europe against EUROSTAT 2020 gridded-type statistics, showing the importance of survey-based statistics for ensuring mapping accuracy, particularly in humid regions where remote sensing exhibits inherent limitations; (iii) review prevailing mapping frameworks, identifying scarce, inconsistent, and restricted-access ground-truth and statistics data as the primary bottleneck, while proposing response strategies for future research. By integrating these dimensions, our analysis delineates the state of global irrigation mapping and charts a pathway toward precision monitoring, ultimately empowering robust water resource modeling and safeguarding the resilience of agricultural ecosystems in an era of escalating climatic variability.