## 1 Introduction  
Geothermal resources, as a key form of renewable and clean energy, are receiving growing attention amid the global energy transition and the pursuit of carbon neutrality [1, 2]. Geothermal energy is not only environmentally friendly and sustainable, but also plays a vital role in reducing reliance on fossil fuels and mitigating climate change [3]. Although highly reliable, traditional geothermal exploration methods--including geological [4, 5, 6], geophysical [7, 8, 9], and geochemical approaches [10, 11, 12, 13]--often require substantial human, material, and financial resources. This is particularly true in regions with complex natural conditions,such as the Tibetan Plateau, where these methods face high implementation costs and technical challenges [14]. With the advancement of remote sensing technology, thermal infrared remote sensing (TIRS) has emerged as an innovative solution for geothermal anomaly detection, offering wide coverage, low cost, and adaptability to complex terrains [15; 16]. TIRS technology detects surface temperature variations to effectively identify areas of underground thermal activity. It has been widely applied in numerous regions abundant in high-temperature geothermal resources, including Dandong, Liaoning [17; 18], Tengchong and Ruili, Yunnan [16; 19; 20], Kangding, Sichuan [21], Rizhao, Shandong [22], as well as internationally in the Tulu Moye region of Ethiopia's Main Rift [23], the Kenya Rift [24], the Thrace Basin of Turkey [25], and Mount Ungaran in Indonesia [26]. Among these, the inversion method based on land surface temperature (LST) has become a key tool for identifying geothermal anomaly zones [19; 27; 28; 29; 30]. However, single-phase thermal infrared data are often influenced by various natural factors, including solar radiation and surface properties, leading to random errors and uncertainties in the extraction results [16]. In recent years, multi-temporal long-term LST data have been widely applied to improve the accuracy of geothermal anomaly detection. By identifying stable high-temperature areas, they effectively eliminate false anomalies and significantly enhance the reliability of the results [23]. Compared to daytime data, nighttime LST is less affected by solar radiation and more effectively captures thermal exchange between the surface and subsurface, making it a valuable tool for geothermal exploration [17; 31; 32]. However, the low acquisition frequency and limited temporal resolution of nighttime thermal infrared imagery remain significant limitations. As a result, effectively leveraging existing data resources to overcome spatial and temporal resolution constraints has become a key research focus. To address this challenge, researchers have proposed a joint daytime-nighttime analysis strategy that integrates both datasets to reduce the influence of surface property differences and improve the reliability of geothermal anomaly detection [19; 23; 33; 34]. Furthermore, the spatiotemporal discontinuity of remote sensing data and the uncertainty associated with localized anomalies remain major challenges in current research [35]. Google Earth Engine (GEE) [36], a cloud-based geospatial data processing platform, provides technical support to address discontinuities in LST time-series data and significantly enhances the efficiency of large-scale processing through its robust computational and analytical capabilities [37; 38]. Meanwhile, Dempster-Shafer (D-S) evidence theory serves as an effective framework for integrating multi-source, multi-perspective data. By fusing geothermal anomaly evidence from different sources, it reduces the errors associated with single-method approaches and improves the accuracy of nighttime anomaly detection [30; 39]. Building on the aforementioned context, this study focuses on geothermal anomaly detection in complex geological settings and proposes a novel detection framework that integrates multi-source and multi-temporal thermal infrared remote sensing data.