U.S. Geological Survey, Dakota Water Science Center, Rapid City, South Dakota, USA.
U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, Louisville, Kentucky, USA.
J Environ Qual. 2023 Jul-Aug;52(4):907-921. doi: 10.1002/jeq2.20493. Epub 2023 May 26.
Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%-96% and balanced accuracy of 52%-54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.
了解地下排水（管排）的范围对于理解景观如何应对降水事件和随后的干燥天数以及土壤特性和土地管理如何影响溪流响应至关重要。因此，管排范围的时间序列将提供土地管理的一个方面的信息，这使得我们难以解释溪流流量和水质作为气候变化或保护管理的函数。我们训练了一个 UNet 机器学习模型，这是一种卷积神经网络，旨在突出图像中感兴趣的对象，以便在没有土壤、地形或历史管排范围的额外数据的情况下，对全色卫星图像中的管排网络进行描绘。这是通过训练模型以匹配人工跟踪卫星图像中管排表面表示的准确性来实现的。我们的方法从一个图像库开始，这些图像用于训练和量化模型的准确性，然后在两个未用于训练模型的区域的图像上测试模型的性能。卫星图像包括 2008 年至 2020 年的采集日期。训练图像来自美国大湖盆地的农业区。验证图像来自俄亥俄州印第安纳州莫米河支流，即伊利湖支流上流域。我们对卫星图像的分析与气象和土壤数据相结合，发现春季期间，相对较高的太阳辐射、中等土壤水分含量和裸露田地的组合使模型性能达到最佳。每个感兴趣的区域都有大量的管排，更好地了解从田地到下游水体的水、养分和沉积物的运动是管理有害藻类水华和缺氧的关键。经过训练的 UNet 模型成功地识别了验证图像中可见的管排，准确率为 93%-96%，平衡准确率为 52%-54%，与训练数据的性能相似（分别为 95%和 63%）。模型性能将受益于对培训库的持续贡献。
