Studying Land Surface Temperature (LST) provides various scientific, environmental, and practical advantages. It aids climate monitoring, urban heat island studies, environmental assessments, agriculture, urban planning, infrastructure development, and remote sensing. Algorithm selection depends on research constraints and goals, considering different algorithm strengths and limitations. In this study, the LST around the Sabalan volcanic peak was estimated using Landsat 8 satellite images captured by the OLI and TIRS, employing the Single Channel JM&S and Split Window algorithms. The obtained surface temperatures from both algorithms were converted to air temperature. Subsequently, the pixel air temperatures were compared to the corresponding synoptic station air temperatures of the studied region, considering the satellite's passage time. This comparison was executed using regression analysis (R2 and correlation coefficients) and statistical indices such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to validate the data. The results indicated R2 values and correlation coefficients of 0.04 and 0.97, respectively, for the Single Channel algorithm, and 0.12 and 0.69 for the SW algorithm, signifying a positive relationship and close alignment between these datasets. Furthermore, the examination of the results revealed root mean square error and mean absolute error with minimum errors of 4.20, 3.88, and 8.14, 8.97 °C in the SW method. By assessing the difference between mean LST and the synoptic station air temperatures, the SW approach exhibited lower temperature discrepancies across all stations and lower estimation errors compared to the Single Channel method, thus displaying higher accuracy and closer conformity to the actual temperature.