The potential to map geothermal anomalies using remote sensing information has attracted recently much research, reflecting thus the increasing interest for renewable energy resources. Aim of the present work is to highlight areas with geothermal anomalies, as demonstrated by increased Land Surface Temperature (LST) values, that could potentially indicate possible locations for geothermal field development. We hypothesized that an area with increased geothermal potential can possibly have a surface expression through increased LST, that discriminates it from other areas of low geothermal interest. LST is known to be affected by increased heat flow but also from other parameters such as altitude, land cover and meteorological conditions. Therefore, there is need to develop a methodology capable to extract LST signals corresponding to the geothermal component. To delineate areas with constantly higher LST values from surrounding locations, we analyzed Landsat 8 derived LST time series, and accounted for different land cover types and altitudes. To test our hypothesis, we used a well-known geothermal field in Aristino-Alexandroupolis, NE Greece, where it was shown that spatial means of winter LST were significantly greater within geothermal zones. Furthermore, our results indicated that areas within geothermal fields demonstrate winter LST values greater than a certain threshold value for each different land cover type. Therefore, we developed a logical operator algorithm and applied our methodology to Thrace basin – NE Greece. The produced geothermal potential map depicted correctly spot areas, which make part of the known geothermal fields in Eastern Macedonia and Thrace Tertiary sedimentary basins, but also indicated other possible sites with increased potential for future research.

使用遥感信息绘制地热异常图的潜力最近吸引了很多研究，因此反映出人们对可再生能源的兴趣日益增加。当前工作的目的是突出显示具有地热异常的区域，如升高的地表温度（LST）值所表明的那样，这可能潜在地指示了开发地热领域的可能位置。我们假设地热潜力增加的区域可能通过增加LST而具有表面表达，从而将其与其他地热关注度低的区域区别开来。已知LST受热量增加的影响，但也受其他参数（例如海拔，土地覆盖和气象条件）的影响。因此，需要开发一种能够提取对应于地热成分的LST信号的方法。为了描绘周围位置LST值不断升高的区域，我们分析了Landsat 8得出的LST时间序列，并考虑了不同的土地覆盖类型和海拔高度。为了验证我们的假设，我们使用了希腊东北部阿里斯蒂诺-亚历山大城的一个著名的地热场，该场表明，在地热区内冬季LST的空间均值明显更大。此外，我们的结果表明，对于每种不同的土地覆盖类型，地热田地区的冬季LST值均大于某个阈值。因此，我们开发了一种逻辑算子算法，并将我们的方法应用于色雷斯盆地–希腊东北部。