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.

中文翻译：

---

使用深度学习分类绘制美国西北部的灌溉方法图


美国西部和世界其他地区的许多农业地区使用多种灌溉方法进行灌溉。需要绘制灌溉方法地图，但当不同的灌溉方法共存于同一景观时，现有技术通常无法区分不同的灌溉方法。在这项研究中，我们开发了一种可广泛应用的深度学习灌溉方法绘图工具。该技术使用在 Landsat 5 和 8 派生的输入图像上训练的 U-Net 模型。培训数据包括来自犹他州与水相关的土地利用数据集的农田灌溉方法，分类为洪水、喷水或其他，以及爱达荷州南部选定地区的附加标签。由 10 个经过训练的模型组成的集合的总体准确率为 0.78。洪水、洒水装置和其他的精确度分别为 0.73、0.82 和 0.80，而召回值分别为 0.75、0.74 和 0.84。在整个训练期间，模型表现总体稳定，但因领域而异。在大片斑块上采用统一灌溉方法的地区获得了最佳性能，而与周围环境对比灌溉方法的小田地则预测不充分。爱达荷州南部灌溉流域 2006 年、2011 年、2013 年和 2016 年的模型预测与之前发布的调查数据一致。该方法为水资源管理者提供了一个工具来估计农业流域的灌溉方法，这些流域在生长季节自然降水量较低，灌溉方法包括中心支轴灌溉、轮线灌溉和漫灌。