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As a crucial source of runoff in headwater regions, seasonal snowmelt plays an important role in ensuring water availability downstream, particularly during low flow periods. As the major constituent of China's water towers, the headwater region of the Yangtze River (HRYR) provides water to hundreds of millions of people downstream. Therefore, accurately simulating snowmelt is critical to developing a better understanding of the hydrological processes, which would, in turn, benefit water supply management, irrigation, hydropower generation, and ecological integrity over the HRYR and its lower reaches. However, it is a considerable challenge to conduct hydrological modeling for ungauged and poorly gauged headwater regions, owing to a lack of in situ measurements to appropriately constrain the model and evaluate its results. Satellite remote sensing provides an unprecedented opportunity to capture hydrological state variables globally, such as snow cover area (SCA) based on optical remote sensing and snow water equivalent (SWE) based on passive microwave remote sensing. This study simulates snow and glacier meltwater of the HRYR (above the Zhimenda gauging station), and quantifies proportional meltwater contributions to total runoff using multisource remote sensing data and a distributed hydrological model. We, for the first time ever, evaluate the snowmelt simulations based on the hydrological consistency among precipitation, air and land surface temperatures, and remotely sensed SWE/SCA. Results show that the snowmelt simulations using either SWE or SCA as a reference for calibrating parameters of the hydrological model are highly consistent, with snow and glacier meltwater contributing similar to 7% and similar to 5%, respectively, to the total runoff during 2003-2014. This study serves as a basis to simulate snowmelt to understand runoff generation and evolution under climate change across ungauged and poorly gauged headwater regions using multisource remote sensing data.