Mapping Irrigation Methods in the Northwestern US Using Deep Learning Classification

• Published in: Water resources research 2024-08, Vol.60 (8), p.n/a
• Main Authors: Nouwakpo, S. K., Bjorneberg, D., McGwire, K., Hoque, O.
• Format: Article
• Language: English
• Subjects: Agricultural equipment; Agricultural land; Agricultural watersheds; Datasets; Deep learning; Flood irrigation; Flood management; Flood predictions; Floods; Growing season; Image quality; Irrigated areas; Irrigation; irrigation method; Irrigation systems; Irrigation water; Land use; Landsat; Landsat satellites; Machine learning; Management methods; Managers; Mapping; Methods; Performance prediction; Pivots; Precipitation; Remote sensing; Satellite imagery; Sprinkler irrigation; Sprinkler systems; Training; U‐Net; Water quality; Water resources; Water resources management; Water supply; Watersheds
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2023WR036155

Many agricultural areas of the western United States and other parts of the world practice irrigation using a variety of irrigation methods. Maps of irrigation methods are needed but existing technologies are often unable to distinguish between different irrigation methods when they co‐exist on the same landscape. In this study, we develop a deep learning irrigation methods mapping tool for broad scale application. The technique uses a U‐Net model trained on Landsat 5‐ and 8‐derived input images. Training data consisted in irrigation method classified as Flood, Sprinkler or Other on agricultural fields from the Utah Water Related Land Use data set and additional labeling in selected areas of southern Idaho. An ensemble of 10 trained models had an overall accuracy of 0.78. Precision for Flood, Sprinkler and Other were 0.73, 0.82, and 0.80 while recall values were 0.75, 0.74, and 0.84 respectively. Model performance was generally stable throughout the training years but varied by areas. The best performance was obtained in regions with uniform irrigation method across large patches while small fields of contrasting irrigation method with their surroundings were inadequately predicted. Model prediction in an irrigated watershed of southern Idaho for 2006, 2011, 2013, and 2016 were consistent with previously published survey data. This methodology provides a tool for water resource managers to estimate irrigation methods in agricultural watersheds where natural precipitation is low during the growing season and irrigation methods include center pivots, wheel lines and flood irrigation. Plain Language Summary Many agricultural areas of the western United States practice irrigation using a variety of irrigation methods. Irrigation methods can be classified into 3 main groups: surface (or flood), sprinkler systems and micro‐irrigation systems. Flood and sprinkler irrigation account for 90% of irrigated areas in the United States but impact water resources differently. Flood irrigation has been associated with many adverse effects on water quality whereas sprinkler systems are promoted as improved irrigation alternatives to preserve water quantity and quality. Maps of irrigation methods are needed to improve assessment of irrigation methods on water quantity and quality. In this study, we develop an irrigation methods mapping tool by training a deep learning model on publicly available satellite imagery. The model was trained on the Utah Water Related Land Use dat