With the recent increase in global focus on green energy, the application of thermal infrared remote sensing data for the detection of geothermal anomalies has attracted wide attention as it can overcome the difficulty of using only ground surveying. This study aimed to highlight areas of geothermal anomalies with land surface temperature (LST) time series data in winter derived from thermal infrared remote sensing. To extract LST anomaly areas in the Ruili Basin for geothermal prospecting, nine types of data on the study area in winter during 2014 ~ 2021 from Landsat 8 were analyzed. Landsat 8 LST inversion data based on the mono-window algorithm (MWA) can be used to identify hot springs, volcanoes, and other heat-related phenomena. Superimposing LST anomalies for each cycle through drilling data, excluding the heat island effect, geothermal anomaly regions could be plotted. The results show that the accuracy of MWA LST varied within 2 K, which is acceptable for geothermal energy and higher than those of the radiative transfer equation (RTE) algorithm and MODIS LST products. Three high-LST regions in the southeast of the study area were identified as geothermal anomaly areas (A, B, and C), and region B was further verified through a comprehensive field investigation of geothermal wells, supplemented by the temperature gradient (TG) method. The findings reveal that the distribution of geothermal anomaly areas and high-LST areas are highly consistent with the northeast trending fault structure; faults act as thermal channels and help in accurately detecting local LST anomalies. Overall, the infrared remote sensing method proved to be a valid technique for detecting LST anomalies. Considering the synergy between thermal infrared surface detection and subsurface exploration methods, the identification of known geothermal fields (B) and other possible areas (A and C) has significance in the upscaling of local geologic information to regional prospecting, thus providing a direction for future geothermal research.
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