Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing

Effective and sustainable management of aquifers in regions with intensive groundwater use for irrigation requirements accurate mapping or irrigated areas to control water resource exploitation and plan rational water usage. This study proposes a cost-effective methodology based on satellite images to identify irrigated areas utilizing surface water and groundwater resources. The methodology integrates soil moisture estimations, environmental variables, and variables that affect to retention of water soil, that join a ground truth dataset, to estimate irrigated surface through a machine learning method during the irrigation period of 2021. Spectral data derived parameters and crop morphology, along with official data on agricultural parcels, were utilized to define vineyard irrigation areas at the plot scale within the Requena-Utiel aquifer in Eastern Spain. A machine learning classification technique was applied,yielding a remarkable precision of 91.8 % when compared to ground truth data.Discrepancies between the remote sensing-based irrigated area estimation and official data are highlighted. This study represents the most accurate plot-scale irrigation mapping of woody crops in the region to date. • Multispectral data combined with ML efficiently measures groundwater resource exploitation and monitors agricultural changes. • A ML model using remote sensing data accurately identifies irrigated areas, aiding water management and agricultural policy decisions. • Rigorous reference data ensures precision in results. • Accurate irrigation area estimation can provide water allocation decisions and enhance water use.