4.7 Article

Geoid Studies in Two Test Areas in Greece Using Different Geopotential Models towards the Estimation of a Reference Geopotential Value

Journal

REMOTE SENSING
Volume 15, Issue 17, Pages -

Publisher

MDPI
DOI: 10.3390/rs15174282

Keywords

Greece; geoid; global geopotential models; residual terrain modeling; zero-height geopotential level

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In this study, the contribution of different global geopotential models in geoid computation is examined using the remove-compute-restore approach. Gravity measurements were conducted in two test areas in Greece, and the measurements were used along with selected geopotential models to produce the reduced gravity field and derive the residual gravity field. These geoid models were validated with gravity/GNSS/leveling measurements, and the accuracy was influenced by the inclusion of non-recent data and the geopotential model used.
In the present study, we first examine the contribution of different, satellite-only or combined, global geopotential models in geoid computation employing the remove-compute-restore approach. For this reason, two test areas of about 100 km2 each, one in northern and one in central Greece, were selected, and gravity measurements were conducted. These new gravity measurements were used along with the selected geopotential models to produce the reduced gravity field for the study areas. The classical and spectral residual terrain modeling effect is also removed to derive the residual gravity field. The latter is used for geoid computation using the 1D fast Fourier transform. The validation of the geoid models is carried out with gravity/GNSS/leveling measurements, which were conducted in two traverses located in the study areas. Special attention is given to the tidal approach, the geoid separation term as well as the coordinate reference system. Next, the northern study area is extended by incorporating gravity measurements obtained during the last five decades, and geoid models are recomputed. Lastly, using the geoid models computed, reference geopotential values are computed for both areas. From the results achieved for both study areas, the combined model XGM2019 provides the best overall statistical results with differences of 0.065 m and 0.036 m in terms of root mean square error. The incorporation of not recent data into the solutions leads to a degradation in accuracy by about 1.2 cm in terms of standard deviation. Lastly, the computed reference geopotential values present discrepancies between the two study areas, revealing network inconsistencies as well as the dependency on the geopotential model used for the geoid computations.

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