Assimilation systems absorb both satellite measurements and Argo observations. This assimilation is essential todiagnose and evaluate the contribution from each type of data to the reconstructed analysis, allowing for better configuration of assimilation parameters. To achieve this, two comparative reconstruction schemes were designed under the optimal interpolation framework. Using a static scheme, an in situ-only field of ocean temperature was derived by correcting climatology with only Argo profiles. Through a dynamic scheme, a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection, and then a combined field was reconstructed by correcting the synthetic field with in situ profiles. For both schemes, a diagnostic iterative method was performed to optimize the background and observation error covariance statics. The root mean square difference (RMSD) of the in situ-only field, synthetic field and combined field were analyzed toward assimilated observations and independent observations, respectively. The rationale behind the distribution of RMSD was discussed using the following diagnostics: (1) The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters, as in the Northwest Pacific Ocean; this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity. (2) The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes; this is determined by the actual noise-to-signal ratio of ocean temperature. (3) The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics; this is determined by both the geographical feature of the synthetic field RMSD (smaller at depth in the extratropics) and that of the covariance correlation scales (smaller in the extratropics).