Geothermal systems can be used to produce low-emission energy throughout the day and night, regardless of the weather conditions. These features make geothermal systems a sustainable and reliable energy source, which can be exploited on a much larger scale than it is now. Remote sensing techniques can support detecting areas potentially suitable for geothermal energy production, thereby reducing the costs of preliminary exploration. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can provide nighttime thermal imagery, which can be used for geothermal anomaly detection. This paper presents a method for automated detection of geothermal anomalies using nighttime ECOSTRESS data of the study area in Olkaria, Kenya. The proposed detection method is a kernel-based one, and includes adaptions of kernel size for the cases of large geothermal anomalies. The accuracy of the method is verified with reference data acquired during field work. A producer’s accuracy of 82% is achieved, which is on average 56% points better than in randomised anomaly maps. The possible sources of errors in detection are heat capacity of surfaces, terrain features and vegetation masking the thermal signatures. The high producer’s accuracy proves potential for application in global mapping of geothermal anomalies.

中文翻译：

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使用 ECOSTRESS 检测奥尔卡里亚地热田地表温度异常

无论天气条件如何，地热系统都可以全天候生产低排放能源。这些特征使地热系统成为一种可持续且可靠的能源，可以比现在更大规模地开发利用。遥感技术可以支持探测可能适合地热能生产的区域，从而降低初步勘探的成本。空间站生态系统星载热辐射计实验（ECOSTRESS）可以提供夜间热图像，可用于地热异常探测。本文提出了一种利用肯尼亚奥尔卡里亚研究区夜间 ECOSTRESS 数据自动检测地热异常的方法。所提出的检测方法是基于内核的方法，并且包括针对大型地热异常情况的内核大小的调整。利用现场工作中获得的参考数据验证了该方法的准确性。生产者的准确度达到 82%，比随机异常地图平均高出 56%。检测误差的可能来源是表面热容量、地形特征和掩盖热特征的植被。生产器的高精确度证明了其在全球地热异常测绘中的应用潜力。