Land Surface Temperature (ST), also known as skin temperature, represents the radiometric temperature of the Earth’s surface, typically measured in the thermal infrared spectrum. ST plays a crucial role in various research fields, including climate variability, land cover change, cryosphere studies, and urban heat analysis. Due to its sensitivity to factors like soil moisture, geology, and topography, ST exhibits significant spatial and temporal variations, requiring high-resolution observations. Satellites provide the only feasible means of obtaining ST measurements with extensive spatial coverage and high temporal resolution. Since the 1970s, researchers have used satellite-derived ST for climatology, meteorology, hydrology, and ecology, particularly in regions with limited ground-based data. Modern satellite sensors, such as MODIS, enable frequent ST observations at sub-daily to weekly intervals, contributing to improved environmental monitoring. However, accurately estimating ST from satellite thermal infrared data is challenging due to atmospheric effects, surface emissivity variations, and land cover influences. This study evaluates the performance of satellite-derived ST in estimating daily temperature trends in the Alps. It utilizes MODIS sub-daily ST products and compares them with trends from 78 ground-based radiometric stations of the Intercantonal Measurement and Information System (IMIS). The assessment relies on three statistical metrics: temporal correlation, bias, and root mean square error (RMSE). Additionally, the study examines potential biases related to land cover and direct shortwave radiation effects on satellite-derived ST trends.