Assessing the spatio-temporal variations of wheat yield and water productivity under centre pivot irrigation systems using open-access remote sensing data.

Understanding the reasons behind Crop Water Productivity (CWP) variations is a practical approach to improve food security in the face of increasing water scarcity. Substantial efforts have been made to develop workflows for monitoring water productivity using open-access satellite data such as Landsat and Sentinel(s) which covers entire world. However, unleashing the potential of these complex satellite datasets and deriving a positive change on the ground remains challenging. The study explores the opportunities and procedures for using open-access remote sensing data and the Surface Energy Balance Algorithm for Land (SEBAL) to explain the reasons behind CWP variations in 140 farmer-owned centre pivot irrigation systems located north of Erbil City, Iraq. Winter wheat’s crop water use, yield, and water productivity were calculated for five seasons (2016/17–2020/21) using high-resolution Landsat 7/8 (30 m) and sentinel-2 A/B (10 m) satellite data. On thid basis, high-performing (bright-spots) and low-performing (hot-spots) centre pivots were identified based on crop yield and water productivity, and their biophysical stresses factors were compared to investigate the underlying reasons for performance differences. SEBAL models demonstrated significant predictive capability, with percent bias (PBIAS) for water use and crop yield of only 0.8 % and 7.5 %, respectively. The results show a gradual increase in crop yield from 3000 Kg/Ha in 2017/18 to 5700 Kg/Ha in 2020/21, possibly due to the adoption of recently locally developed wheat genotypes (Erbil-2 and Erbil-4) in the study area. The results revealed that water and nitrogen stresses impacted crop yield at different growth stages, leading to the variation. In contrast, the inter-seasonal difference in water use was marginal, with an average 500 ± 50 mm/season. Overall, open-access remote sensing data and SEBAL showed a remarkable ability to predict wheat yield and water use and identify critical biophysical stress factors affecting water productivity. However, further calibration and testing of the model are needed for different crops and climates. • Developed a framework to reveal drivers of water productivity variation from remote sensing and guide action. • Observed stable seasonal water use with wheat yields rising over five cropping seasons. • Classified pivots as bright/hot spots, revealing gaps from weak fertilizer and irrigation scheduling. • High yields aligned with optimal irrigation at grain-filling and improved nitrogen application. • PySEBAL compared with field data (PBIAS: ETa 0.8%, yield 7.5%) confirmed precision irrigation value