## 3 Materials and Methods
### Landsat 8 LST Data Products and Time-Series Processing  
This study utilized Landsat 8 thermal infrared data to identify daytime geothermal anomalies, owing to its high spatial resolution and accessibility, making it ideal for monitoring large-scale surface temperature variations. To eliminate reliance on atmospheric profile \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Resolution** & **Temporal Scope** & **Data Source** & \begin{tabular}{c} **Total** \\ **Senses** \\ \end{tabular} & **Processing Steps** & **Research Role** \\ \hline ASTER & 90 m & 3 November 2023, 9 December 2023 & \begin{tabular}{c} NASA LP DAAC, \\ ASTER Surface \\ Temperature \\ Product (AST\_08 \\ V003) \\ \end{tabular} & 2 & \begin{tabular}{c} 1. Extract thermal anomalies using a multi-view \\ approach (global, local, elevation, and \\ structural thresholds). \\ 2. Integrate multi-view data using D-S \\ evidence theory. \\ \end{tabular} & **Research Role** \\ \hline MODIS & 1000 m & 2003-2023 (21 years, 252 months) & \begin{tabular}{c} NASA LP DAAC, \\ MODIS Land \\ Surface Temperature \\ Daily Product \\ (MOD11A1 V061) \\ \end{tabular} & 7640 & \begin{tabular}{c} 1. Extract thermal anomalies using a multi-view \\ approach (global, local, elevation, and \\ structural thresholds). \\ 2. Integrate multi-view data using D-S \\ evidence theory. \\ \end{tabular} & **Research Role** \\ \hline MODIS & 1000 m & 2003-2023 (21 years, 252 months) & \begin{tabular}{c} NASA LP DAAC, \\ MODIS Land \\ Surface Temperature \\ Daily Product \\ (MOD11A1 V061) \\ \end{tabular} & 7640 & \
\begin{tabular}{c} 1. Construct a 252-month long-term LST time series. \\ 2. Calculate the 21-year monthly mean LST. \\ 3. Analyze interannual and monthly LST variation \\ trends (2003–2023). \\ \end{tabular} &
\begin{tabular}{c} Analysis of \\ magnitude chamber \\ activity and \\ activity \\ long-term \\ geothermal dynamic \\ evolution. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Schematic diagram of the geothermal anomaly detection workflow using multi-source LST data. The workflow involves the following steps: (1) Processing of Landsat-8 daytime LST time series data; (2) Multi-view extraction of ASTER nighttime LST data combined with Dempster–Shafer (D–S) evidence fusion; (3) Integrated analysis of the intersection between daytime and nighttime LST data; (4) MODIS long-term LST data are utilized to infer magmatic activity and heat source distribution; (5) Correlation analysis and result validation using geological information. parameters in traditional Landsat data inversion (since NASA no longer provides these parameters), this study employs the Landsat Collection-2 L2 surface temperature product from USGS [49]. The product has undergone radiometric calibration, atmospheric correction, and geometric correction, utilizing a single-channel algorithm to achieve temperature inversion accuracy within 0.5 K to 1 K [50]. Moreover, the default image registration in the GEE platform ensures spatial consistency across the study area, eliminating the need for additional manual alignment. The temperature conversion formula is as follows:  
\[\text{ST}=\text{DN}\times 0.00341802+149-273.15 \tag{1}\]  
where ST denotes the surface temperature in Celsius (\({}^{\circ}\)C), DN is the digital number, 0.00341802 is the scaling factor provided by the USGS for Landsat Collection 2 Level 2 Surface Temperature products, and 149 is the offset value defined by the United States Geological Survey (USGS). To convert ST from Kelvin to Celsius, 273.15 is subtracted [49]. To accurately capture geothermal signals, a long-term LST time series for winter seasons (November to the following March) from 2013 to 2023 was generated, and the average LST for each winter was calculated using the GEE platform [37]. In addition, the multi-year average winter LST for 2013-2023 was computed. The data processing steps are as follows:  
1.