Integrating Remote Sensing Techniques and Meteorological Data to Assess the Ideal Irrigation System Performance Scenarios for Improving Crop Productivity

To increase agricultural productivity and ensure food security, it is important to understand the reasons for variations in irrigation over time. However, researchers often avoid investigating water due data availability challenges. This study aimed assess performance of system winter wheat crops using a high-resolution satellite, Sentinel 2 A/B, combined with meteorological Google Earth Engine (GEE)-based remote sensing techniques. The area located north Erbil city Kurdistan region Iraq (KRI) consists 143 farmer-owned center pivots. also analyze spatiotemporal variation key variables (Normalized Difference Moisture Index (NDMI), Normalized Vegetation (NDVI), Precipitation (mm), Evapotranspiration (ETo), Crop evapotranspiration (ETc), Irrigation (Hours), during wheat-growing season drought year 2021 variance field performance. finding revealed that usage fluctuated significantly across seasons, while yield gradually increased from season. In addition, notable correlation between soil moisture based on (NDMI) vegetation cover reduction gap, specifically middle growing (March April). Integrating supplementary systems can improve agriculture resource management by boosting yields, improving crop quality, decreasing consumption, minimizing environmental impacts. innovative technique potentially enhance security promote sustainability.