We propose a revisited approach to estimating sea level change trends based on the integration of two measuring systems: satellite altimetry and tide gauge (TG) time series of absolute and relative sea level height. Quantitative information on vertical crustal motion trends at six TG stations of the Adriatic Sea are derived by solving a constrained linear inverse problem. The results are verified against Global Positioning System (GPS) estimates at some locations. Constraints on the linear problem are represented by estimates of relative vertical land motion between TG couples. The solution of the linear inverse problem is valid as long as the same rates of absolute sea level rise are observed at the TG stations used to constrain the system. This requirement limits the applicability of the method with variable absolute sea level trends. The novelty of this study is that we tried to overcome such limitations, subtracting the absolute sea level change estimates observed by the altimeter from all relevant time series, but retaining the original short-term variability and associated errors. The vertical land motion (VLM) solution is compared to GPS estimates at three of the six TGs. The results show that there is reasonable agreement between the VLM rates derived from altimetry and TGs, and from GPS, considering the different periods used for the processing of VLM estimates from GPS. The solution found for the VLM rates is optimal in the least square sense, and no longer depends on the altimetric absolute sea level trend at the TGs. Values for the six TGs’ location in the Adriatic Sea during the period 1993–2018 vary from −1.41 ± 0.47 mm y−1
(National Research Council offshore oceanographic tower in Venice) to 0.93 ± 0.37 mm y−1
(Rovinj), while GPS solutions range from −1.59 ± 0.65 (Venice) to 0.10 ± 0.64 (Split) mm y−1
. The absolute sea level rise, calculated as the sum of relative sea level change rate at the TGs and the VLM values estimated in this study, has a mean of 2.43 mm y−1
in the period 1974–2018 across the six TGs, a mean standard error of 0.80 mm y−1
, and a sample dispersion of 0.18 mm y−1
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