<p>Under a warming climate, global lake thermodynamics are undergoing significant variation that affect internal nutrient and oxygen transport and in turn alter aquatic ecosystems. Observational data on the Qinghai-Xizang Plateau remain scarce, creating an opportunity to integrate satellite data into lake modeling to improve simulation performance of thermodynamic processes. This study reconstructed the long-term (2000–2024) thermal structure and surface energy balance of Nam Co by employing the LAKE 3.3 model incorporating satellite-derived albedo products. The simulations reveal that lake temperature and energy fluxes are highly sensitive to changes in both open-water and ice albedo. Model validation against lake surface water temperature (LSWT) observed from Landsat, the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Along Track Scanning Radiometer Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) datasets demonstrated significantly improved accuracy at daily and monthly scales. Monthly comparisons with Landsat LSWT achieved a correlation of <i>R</i>=0.96 and RMSE=2.70 °C, while the performance of deep-water temperature predictions also improved when compared against in-situ water temperature profiles. The simulation results indicate that increases in latent heat flux (0.4 W/(m²·decade)) and decreases in net shortwave radiation (−5.3 W/(m²·decade)) largely counterbalance atmospheric warming, resulting in an insignificant trend in LSWT (0.1 °C/decade) during 2000–2024. Nonetheless, significant warming was detected in December, attributed to accumulated heat storage and increased solar radiation during that month. The methodology presented here demonstrates the value of remote sensing data for advancing lake modeling and provides a valuable approach for understanding climate-driven thermodynamic changes in high-altitude lakes.</p>