Detection of geothermal potential based on land surface temperature derived from remotely sensed and in-situ data

Abstract

Geothermal energy is a renewable and environmentally sustainable resource of increasing importance. However, areas with geothermal potential are not easily detected by traditional field investigations, requiring the development of new, robust, and reliable models for detection. In this study, remote sensing data and ground-based variables were used to detect and analyze geothermal resource potential areas. General Land Surface Temperature (GLST) was integrated using 5 years of remote sensing data. Landsat 8 daytime GLST (Landsat-GLST), Moderate Resolution Imaging Spectroradiometer (MODIS) daytime GLST (MODIS-DLST), and MODIS nighttime GLST (MODIS-NLST) data were integrated with Landsat Nighttime Land Surface Temperature (Night-LST), which not only filled the gap of Landsat Night-LST but also improved the spatial resolution of MODIS nighttime temperatures. Specifically, three independent variables (Night-LST, Distance From Known Geothermal Resource Points [DFGP], and Distance From Geological Faults [DFF]) were used to develop a weighted model to form a Geothermal Detection Index (GDI) based on Principal Component Analysis (PCA). Along with field verification, the GDI was successfully used to identify three geothermal activity areas in Tengchong City, Yunnan Province. Overall, this work provides a novel method for detecting geothermal potential to support the successful exploitation of geothermal resources.