Understanding glacier melting dynamics is critical for assessing the impacts of climate change on mountain cryosphere and water availability. This study presents the results from the application of an enhanced T-index melt model on the whole surface of the Passu glacier (Pakistan), integrating multi-sensor remote sensing data with in situ observation from Automatic Weather Stations (AWS), being part of the project “Glaciers & Students” network, and ablation stakes to improve the estimation and distribution of radiative energy fluxes and glacier melt for the entire year 2023. We leverage Harmonized Landsat Sentinel-2 (HLSL30) imagery on Google Earth Engine to derive high-resolution spatiotemporal albedo distributions, providing key inputs for the melt model. Incoming shortwave radiation fluxes (SWin) are distributed using astronomical, topographic and meteorological corrections. The model to estimate longwave radiation fluxes (LWin and LWout) is validated using thermal remote sensing imagery and AWS data, providing a robust assessment of the glacier’s radiative fluxes. Additionally, in situ ablation stakes data further strengthen the model’s accuracy by offering direct validation of ice melt rates. Our results demonstrate the crucial role of integrating remote sensing with ground-based measurements for improving glacier melt modeling, particularly in data-scarce, remote high-altitude regions like the Third Pole. This study highlights the potential of such comprehensive analysis for refining melt estimations and advancing cryosphere monitoring methodologies.