Abstract. In many areas of the globe, the installation of artificial drains on naturally poorly drained soils is a necessary part of farm management. Identifying the location of artificially drained areas is an important step in achieving environmentally sustainable agricultural production. However, in many regions, data on the presence or the distribution of artificial drainage systems are rare. We outline an approach to identify artificially drained soils using Earth observation (EO) satellite imagery and digital elevation data. The method exploits the contrasting phenology of grass during a peak growth stage to identify artificially drained and undrained soils. Two machine-learning techniques, support vector machine and random forest, were tested. Classification accuracy up to 91% was achieved using photointerpreted accuracy points using higher resolution satellite imagery. Additional investigations would be required to establish whether the drained conditions identified were a result of artificial drainage or from naturally well-drained soils occurring within larger soil units. Herein, the Republic of Ireland is used as a test case. Based on our findings, the area of artificially drained grassland within the study area could be revised upward, with 44% (or ∼345 , 000 ha) of pasture currently classed as “poorly drained” identified as “artificially drained.” At one location, a change in the modeled drainage condition at field level was demonstrated following drain installation. The presented method demonstrates the ability of EO satellites to quickly and accurately map field drainage status at farm management scales over a wide area. This has the potential to improve management decisions at local scales, but also has implications in terms of national policy development and regulation in areas such as water quality and climate change mitigation.

中文翻译：

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使用机器学习和地球观测图像识别人工排水的牧场土壤

摘要。在全球许多地区，在自然排水不良的土壤上安装人工排水管是农场管理的必要组成部分。确定人工排水区的位置是实现环境可持续农业生产的重要一步。然而，在许多地区，关于人工排水系统的存在或分布的数据很少。我们概述了一种使用地球观测 (EO) 卫星图像和数字高程数据识别人工排水土壤的方法。该方法利用草在生长高峰阶段的对比物候来识别人工排水和不排水的土壤。测试了两种机器学习技术，支持向量机和随机森林。使用更高分辨率卫星图像的照片解释精度点实现了高达 91% 的分类精度。需要进一步调查以确定所确定的排水条件是人工排水的结果还是较大土壤单元内自然排水良好的土壤。这里以爱尔兰共和国作为测试案例。根据我们的发现，研究区内人工排水草地的面积可以向上修正，目前有 44%（或约 345,000 公顷）的牧场被归类为“排水不良”，被确定为“人工排水”。在一个地点，在安装排水管后，现场水平的模拟排水条件发生了变化。所提出的方法证明了 EO 卫星能够快速准确地绘制大面积农场管理规模的田间排水状况。这有可能改善地方层面的管理决策，但也对水质和减缓气候变化等领域的国家政策制定和监管产生影响。