Mapping irrigation types in the northwestern US using deep learning classification

Nouwakpo, S.K. and Bjorneberg, D.L. (2024) Mapping irrigation types in the northwestern US using deep learning classification. IEEE Transactions on Geoscience and Remote Sensing. 60(8):1-16. 1 August 2024.

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Abstract

Many agricultural areas of the western United States practice irrigation using a variety of irrigation methods. Maps of irrigation methods are needed but no technology exist to produce these maps at broad spatial scales. In this study, we develop an irrigation methods mapping tool by training a U-Net model on Landsat 5- and 8-derived input images. Training data consisted in irrigation methods classified as Flood (F), Sprinkler (S) or Other (O) on agricultural fields from the Utah Water Related Land Use (WRLU) dataset and additional labeling in selected areas of southern Idaho. An ensemble of 10 trained models had an overall accuracy of 0.78. Precision for F, S and O 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 of sprinkler irrigation in an irrigated watershed of southern Idaho for 2006, 2011, 2013, and 2016 were consistent with previously published survey data. Performance improvements are expected with the utilization of higher resolution satellite products. This methodology provides a tool for water resource managers to estimate irrigation methods in large agricultural areas and identify priority areas in need of irrigation methods conversion.

Item Type:	Article
NWISRL Publication Number:	1791
Subjects:	Irrigation > Sprinkler irrigation Irrigation > Sprinkler irrigation > Infiltration
Depositing User:	Users 11 not found.
Date Deposited:	23 Aug 2024 19:31
Last Modified:	23 Aug 2024 19:31
Item ID:	1834
URI:	https://eprints.nwisrl.ars.usda.gov/id/eprint/1834