Temporal and spatial irrigation performance indicators are crucial in informing decisions for improving the efficiency and sustainability of water and land resources. However, evaluating these indicators requires reliable and cost-effective data, which is challenging to obtain, particularly for small-scale irrigation schemes. This study aimed to assess the performance of a small-scale irrigation scheme using remote sensing and ground truth data for the 2021/22 and 2022/2023 irrigation seasons employing the Shimburit irrigation scheme in Northwestern Ethiopia, predominantly cultivated with wheat, as a case study. The performance indicators, including equity, adequacy, overall consumed ratio (OCR), and productivity, were assessed. The actual evapotranspiration (ET), the main input for performance assessment, was estimated using the surface energy balance for land – improved (SEBALI) model in the Google Earth Engine (GEE) platform. The results revealed good equity within the scheme, with a coefficient of variation of ETa value per field inside the scheme are 1.90 and 1.63 for the respective seasons. The water use adequacy across the fields was assessed to be very good in the two seasons. The scheme’s overall consumed ratio (OCR) was 0.54 and 0.43 during the two subsequent seasons. Water productivity of wheat is 3.03 kg/m3 and 3.06 kg/m3 in the two seasons. However, due to untimely rainfall during harvest, land productivity declined from 3.25 tons/ha in the first season to 2.08 tons/ha in the second season. The study demonstrates the potential of using remote sensing to evaluate irrigation performance indicators and water productivity in smallholder irrigated fields.

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