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Musi estuary is the mouth of the Telang and Musi rivers directly adjacent to the Bangka Strait. During flood (ebb) we see the distribution of salinity increases (decreases) which is known through the vertical distribution using CTD. The TS diagram is used to see the water mass characteristics the study area. Data-Interpolating Variational Analysis (DIVA) method is used to interpolate and visualize data from vertical and spatial temperature, salinity and density data. The classification of the Musi estuary zone is identified based on the value of the distribution of salinity, which considers the exchange of circulating salinity at flood and ebb. The density of the water mass is significantly affected by the proven graded salinity. While the temperature distribution does not change significantly with depth, the spatial distribution indicates that the temperature in the estuary is lower than in the upstream and ocean areas. The spatial distribution of salinity indicates that high salinity enters the estuary towards the river further at flood than at ebb. Salinity distribution ranges from 0.5 to 30 psu and temperatures between 29 and 33 oC from horizontal and vertical sections. The pattern of salinity distribution in the Musi river estuary was identified, consisting of three zones representing salinity conditions in the study area, namely the Polyhaline, Mesohaline, and Olygohaline zones.

A long-term reliable sea surface temperature (SST) satellite data record is requisite resources for monitoring to understand climate variability. Creating a long-term data record especially for climate variability requires a combination of multiple satellite products. Consequently, missing data issues are inevitable. Hence, DINEOF (Data Interpolating Empirical Orthogonal Functions) has been applied to attain a complete and coherent multi-sensor SST data record with EOF-based technique by reconstructing the missing data. Unfortunately, the technique can lead to large discontinuities in the data reconstruction due to images depiction within long time series data. For that reason, filtering the temporal covariance matrix had been applied to reduce the spurious variability and more realistic reconstructions are obtained. However, this approach has not yet tested in tropical region with higher evaporation which cause incomplete satellite image coverage. Therefore, the objective of this research is to reconstruct SST of Lombok strait with data gaps up to 58.16% in one year. It is successfully reconstructed until the last iteration of 42 optimal EOF modes with the convergence achieved up to 0.9806×10-3, including previous set-aside data for internal cross-validation. The results highlight that the DINEOF method can effectively reconstruct SST data in Lombok Strait.