Neupane, A.; Sawada, Y. Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data. Remote Sens.2023, 15, 4633.
Neupane, A.; Sawada, Y. Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data. Remote Sens. 2023, 15, 4633.
Neupane, A.; Sawada, Y. Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data. Remote Sens.2023, 15, 4633.
Neupane, A.; Sawada, Y. Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data. Remote Sens. 2023, 15, 4633.
Abstract
With growing global concern of food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world, where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications on national policy, project’s annual budgets, and donor fund-ing. Here we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006-2011) and Landsat 8 OLI (2013-2022) images were used to develop a machine-learning model which classifies irrigated and non-irrigated areas in study areas. The random forest classification achieved overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and the consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine-learning model outperforms the existing global cropland map, highlighting the need of such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated area with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
