## 6 Conclusions  
This study employed Landsat 8 and ASTER LST data, integrating multi-temporal and multi-perspective surface temperature analysis methods. By incorporating D-S evidence theory and a combined daytime-nighttime analysis strategy, it effectively identified geothermal anomalies in the Yangbajing-Yangyi Basin under complex terrain conditions. During daytime data processing, long-term winter Landsat 8 imagery was processed using the GEE platform. Key preprocessing steps, including water body removal and elevation correction, significantly reduced non-geothermal interference. The local block threshold method further enhanced the accuracy of small-scale anomaly detection, successfully identifying daytime geothermal anomaly zones. During nighttime data processing, single-scene ASTER LST images were used to independently extract anomaly information from four perspectives: global, local, elevation-based, and fault buffer zones. The D-S evidence theory was subsequently applied to integrate the results from each perspective, further enhancing the accuracy of nighttime anomaly detection. Building on this, the combined analysis of daytime and nighttime LST data successfully identified theoretically reliable geothermal anomaly zones. The results show that high-temperature regions identified in both datasets significantly reduced misidentifications caused by thermal differences between bare land and water bodies, thereby improving the reliability of geothermal anomaly detection. The jointly extracted anomaly zones closely matched known geothermal fields in Yangbajing and Yangyi, as well as several documented hot spring sites. Furthermore, the method successfully predicted potential geothermal anomaly zones, providing valuable guidance for future geothermal resource exploration. Additionally, long-term time-series analysis of MODIS nighttime LST data identified four major geothermal anomaly zones: the Yangbajing, Yangjingxue, Regaguo, and Yangyi geothermal fields. The results suggest that these zones are closely associated with suspected subsurface magma chamber activity. Their spatial distribution further supports the reliability of geothermal anomaly detection using daytime and nighttime LST data and reveals the spatial configuration of the subsurface thermal system in the study area. In conclusion, the findings suggest that geothermal anomalies in the study area are primarily driven by deep magma activity, with fault structures acting as key conduits for heat transfer. The multi-source data fusion method proposed in this study effectively captured geothermal activity characteristics and provided valuable insights for geothermal resource exploration and potential heat source identification. Future research will combine higher-resolution remote sensing data and geophysical exploration methods to further enhance geothermal anomaly identification accuracy and reveal the evolutionary characteristics of deep magma heat sources. Conceptualization, C.L. ; methodology, C.L. and N.G. ; formal analysis, C.L. ; data curation, C.L. ; writing--original draft preparation, C.L. ; writing--review and editing, C.L., N.G., Y.L., H.L., Y.Z., S.D. and X.L. ; visualization, C.L. ; supervision, N.G. ; funding acquisition, N.G. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Department of Tibet Autonomous Region, grant number XZ202021 ZD0004G. The APC was funded by XZ202021 ZD0004G. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. We sincerely acknowledge the administrative and technical support provided by the research team. The Landsat-8 Collection 2 Surface Temperature Product was generously provided by the U.S. Geological Survey, and we greatly appreciate the accessibility of MODIS NLST (MOD11A1 V061) and ASTER LST (AST_08 V003) data products from NASA's LP DAAC. These data were essential for the successful implementation and validation of this study.