Monitoring coastal sea level change is critical as sea level rise becomes inevitable due to climate change. The global tide gauge (TG) network is sparse and suffers from data gaps, while satellite altimetry performance near the coast remains spatially inconsistent. Here, a novel dynamically varying search radius algorithm (DSR algorithm) is developed that uses bathymetry to choose satellite observations that represent coastal sea level variability better. The DSR algorithm is applied to the low-resolution (1 Hz) coastal product, X-TRACK SLA L2P v2.2 (XTRACK), and is evaluated through comparison with TG and the high-resolution (20 Hz) coastal product, XTRACK/ALES (Along-track sea level anomalies and trends v3.0). To assess the benefit of DSR, existing observation-selection algorithms are applied to both XTRACK and XTRACK/ALES and compared against TG using detrended deseasonalized coastal sea level anomalies. This inter-algorithm comparison at 272 TG stations demonstrates broader applicability of DSR across various coastal regimes, with consistently higher median correlation and lower median Normalized Root Mean Square Error. The improvement using DSR is especially pronounced along narrow coastal shelf regions. Additionally, the DSR-applied XTRACK's ability to capture coastal sea level anomalies at multiple temporal scales is assessed at 264 stations, showing improved agreement for both seasonal (annual) and non-linear trend (interannual and trend) signals. In some regions, such as the eastern Australian coast and Gulf of St. Lawrence, lower correlation and higher Root Mean Square Error for residual signals are reported, which are discussed. Further, Coastal Sea Level Trends (CSLT) for 1994–2023 (30 years) from DSR-based XTRACK match TG trends within their uncertainties. Notably high CSLT values are observed in the Gulf of Mexico (5.09±0.57 mm/yr from altimetry and 5.2±0.67 mm/yr from TG) and the Baltic Sea (4.62±1.3 mm/yr from altimetry and 4.24 ±1.37 mm/yr from TG).