Snow accumulation and melt is highly variable in space and time in complex mountainous environments. Therefore, it is necessary to provide high-resolution spatially and temporally distributed estimates of sub-basin snow water equivalent (SWE) to accurately predict the timing and magnitude of snowmelt runoff. In this study, we compare two reconstruction techniques (a commonly used deterministic reconstruction vs a probabilistic data assimilation framework). The methods retrospectively estimate SWE from a time series of remotely sensed maps of fractional snow-covered area (FSCA). In testing both methods over the Tokopah watershed in the Sierra Nevada (California), the probabilistic reconstruction approach is shown to be a more robust generalization of the deterministic reconstruction. Under idealized conditions, both probabilistic and deterministic approaches perform reasonably well and yield similar results when compared with in situ verification data, whereas the probabilistic reconstruction was found to be in slightly better agreement with snow-pit observations. More importantly, the probabilistic approach was found to be more robust: unaccounted for biases in solar radiation impacted the probabilistic SWE estimates less than the deterministic case (4% vs 7% errors for water year (WY)1997 and 0% vs 3% errors for WY1999); the probabilistic reconstruction was found to be less sensitive to the number of available observations (6% vs 10% errors in WY1997 and 13% vs 44% errors in WY1999 from the nominal cases when four fewer FSCA images were available). Finally, results from the probabilistic reconstruction approach, which requires precipitation inputs (unlike the deterministic approach), were found to be relatively robust to bias in prior precipitation estimates, where the nominal case mean estimates were recovered even when an underestimated prior precipitation was used. The additional robustness of the probabilistic SWE reconstruction technique should prove useful in future applications over larger basins and longer periods in mountainous terrain. Copyright © 2013 John Wiley & Sons, Ltd.