Irrigation uniformity assessment with high-resolution aerial sensors - Moshe Meron; Moti Peres; Valerie Levin-Orlov; Gil Shoshani; Uri Marchaim; Assaf Chen (International Journal of Applied Earth Observations and Geoinformation)
Irrigation uniformity is a key factor in optimizing water use efficiency and maximizing crop yields, particularly in semi-arid regions. This study investigates the use of high-resolution unmanned aerial vehicle (UAV) thermal and visible light imagery, to assess irrigation uniformity in three systems: surface, linear move, and solid-set irrigation. The research aims to quantify irrigation variability, identify its sources, and propose practical solutions to improve irrigation management through UAV-based mapping technologies. Case studies were conducted in surface-irrigated vineyards in the Murray River Valley (Australia), linear move-irrigated peanut fields in the Hula Valley (Israel), and solid-set orchards in Northern Israel. Thermal imagery was used to calculate the Crop Water Stress Index (CWSI), while the Green-Red Vegetation Index (GRVI) was employed to assess long-term crop vigor. Irrigation uniformity was quantified using the Christiansen Uniformity Coefficient (CUC). The study revealed significant variability in irrigation uniformity across all systems. In surface irrigation, significant variability was detected between the furrow head and tail due to uneven water distribution, as captured by thermal imagery. For linear move systems, RTK-GNSS monitoring revealed irregularities in tower movement creating a zigzag irrigation pattern, leading to areas of over- and under-irrigation. In solid-set systems, unexpected variability in crop stress was attributed to soil heterogeneity and historical land management practices. UAV-based imagery offers precise insights into irrigation uniformity, enabling targeted interventions. Variable-rate irrigation, emitter adjustments, and customized irrigation schedules are practical solutions for improving water distribution. Future research should focus on integrating AI and multi-sensor data to further enhance irrigation efficiency and provide actionable insights for farmers.