Terrestrial oil spills are a major threat to environmental and human well-being. Rapid, accurate, and remote spatial assessment of oil contamination is critical to implementing countermeasures that prevent potentially lasting ecological damage and irreversible harm to local communities. Satellite remote sensing has been used to support such assessments in inaccessible regions, although mapping small terrestrial oil spills is challenging – partly due to the pixel size of remote sensing systems, but also due to the distinguishability of small oil spill areas from other land cover types. We assessed the usability of freely available Sentinel satellite images to map terrestrial oil spills with machine learning algorithms. Using two test sites in South Sudan, we demonstrated that information from the Sentinel-1 and -2 instruments can be used to map oil spills with more than 90 % classification accuracy. Classification accuracy was significantly increased (>95 %) with the addition of multi-temporal information and spatial predictor variables that quantify proximity to oil production infrastructure such as pipelines and oil pads. The mapping of terrestrial oil spills with freely available Sentinel satellite images may thus represent an accurate and efficient means for the regular monitoring of oil-impacted areas.

陆地石油泄漏是对环境和人类福祉的主要威胁。对石油污染进行快速、准确和远程空间评估对于实施防止潜在的持久生态破坏和对当地社区不可逆转的伤害的对策至关重要。卫星遥感已被用于支持在人迹罕至地区的此类评估，尽管绘制小型陆地石油泄漏地图具有挑战性——部分原因是遥感系统的像素大小，但也由于小型石油泄漏区域与其他土地覆盖类型的可区分性. 我们评估了免费提供的 Sentinel 卫星图像的可用性，以使用机器学习算法绘制陆地石油泄漏地图。使用南苏丹的两个测试站点，我们证明了来自 Sentinel-1 和 -2 仪器的信息可用于绘制溢油地图，分类准确度超过 90%。通过添加多时态信息和空间预测变量来量化与石油生产基础设施（如管道和油田）的接近程度，分类准确度显着提高 (>95%)。因此，利用可免费获得的 Sentinel 卫星图像绘制陆地溢油地图可能是定期监测受石油影响地区的一种准确而有效的手段。