Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDF_S) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDF_S values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDF_S estimated from snow data with those using spatially uniform DDF_S calibrated on runoff. The runoff performances using estimated DDF_S are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km². Approximately 70% of the basin is covered by snow in the early spring season.