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Seasonal snow cover is an important component of the Earth's hydrological and energy cycles, affecting water resources and climate feedback mechanisms. Snow water equivalent (SWE), representing the water content of a snowpack, is a key characteristic of snow cover. SWE estimates can be retrieved from passive microwave radiometer data. Global satellite-based passive microwave radiometer measurements are available from 1978 onwards allowing construction of long SWE time series. Radiometer-based SWE retrievals can be improved with the assimilation of synoptic snow depth observations. This thesis aims to advance assimilation-based SWE retrieval method with parametrization of snow density and bias correction. Publication I presents a method for creating climatological spatially and temporally dynamic snow density fields. The effect of post-processing SWE retrieval with these fields is also studied in the publication. Post-processing improves the overestimation of small SWE values and small improvements in the underestimation of large SWE values are also present. Publication II investigates implementing dynamic snow densities into the SWE retrieval. Similarly to post-processing with dynamic snow densities, implementing them into the retrieval improves the accuracy of (small) SWE estimates. Additionally, the reduction in hemispheric peak snow mass seen when post-processing with dynamic snow densities is smaller when snow densities are implemented into the SWE retrieval. Implementation of dynamic snow densities into SWE retrieval also delays peak snow mass timing and thus improves the seasonal evolution of SWE. Publication III updates previously studied monthly bias correction method for monthly SWE estimates with new reference data. Monthly bias correction is also expanded to a daily time scale in this publication. Updated monthly bias correction improves monthly estimates and daily bias correction slightly improves the accuracy of large SWE estimates. More importantly adds a significant amount of snow to the hemispheric snow mass estimation. Together these three studies improve SWE estimations and our ability to monitor seasonal snow cover.