What is the better way to deal the missing and negative cells of satellite snow cover data
You could fill the gaps with the analysis products of numerical weather prediction. NCAR for example (RDA UCAR) hosts the NCEP North American Regional Reanalysis (NARR) with data from 1979 to 2018 including snow cover (NARR) in 3-hourly intervals.

But I would guess it depends on your goal: 
You can make an educated guess (bayesian approach, combining measured and predicted variables) about the true state of the snow cover, but what was not measured can not be pulled out of nowhere.