Satellite altimeters provide sea-level measurements along satellite track. A mean profile based on the measurementsaveraged over a time period is then subtracted to estimatethe sea-level anomaly (SLA). In the spectral domain, SLA ischaracterized by a power spectral density (PSD) whose slopein a log–log scale is a parameter of great interest for ocean monitoring. Estimation of this spectral slope is usually done through a cumulated periodogram using a large number of signal samples. The location and dates of the data induce the spatial and temporal resolution of the slope estimates. To improve this resolution, this article studies a new parametric method based on an autoregressive model combined with a warping of the frequency scale (denoted as ARWARP). This ARWARP model provides a PSD estimate, with a lower variance than the classical Fourier-based ones and is reliable in the case of a small sample number. To give a reference in the performance of the SLA slope estimation, the corresponding Cramér–Rao bound is derived. Then, rather than performing linear regression on the spectral estimates, a new estimator of the slope is suggested, based on a model fitting of the PSD. A statistical validation is proposed on simulated SLA signals, showing the performance of slope estimation using this ARWARP spectral estimator, compared toclassical Fourier-based methods. Application to Sentinel-3 real data highlights the main advantage of the ARWARP model,making possible SLA slope estimation on a short signal segment, i.e., with a high spatial and/or temporal resolution.