The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) provides spatially and temporally complete values for SIT with areal averages validated to SIT satellite observations from ICESat and CryoSat-2. While PIOMAS can assimilate sea-ice concentration, sea-ice velocity, and sea surface temperature, it does not currently assimilate SIT from satellite observations. Here, we use a hierarchal model within a Bayesian statistical framework to incorporate SIT observations and PIOMAS sea ice output to improve upon the spatial distribution and temporal coverage of SIT. We provide a statistical method of integrating submarine, satellite, and modeled data with an emphasis in reducing the error and uncertainty which may arise by only using existing SIT datasets independently. This results in estimates that combine information regarding SIT from multiple sources and thus better examine the interannual variability and long-term trends of SIT and SIV in the Arctic.