Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung-Yangbajain Basin, Qinghai-Tibet Plateau

Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung-Yangbajain basin, Qinghai-Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from -14.7 degrees C to 26.7 degrees C, with an average of 8.6 degrees C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from -22.1 degrees C to 23.3 degrees C, with an average of 6.2 degrees C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 degrees C to 23.3 degrees C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments.

Automated summary

This study proposes a method for efficiently calculating multi-temporal land surface temperature (LST) using Google Earth Engine (GEE) in the Qinghai-Tibet Plateau. The method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The results reveal the distribution of geothermal potential areas in the study area and identify areas for further research.