Coastal zones are challenging areas for sensing by satellite altimeters because reflected signals from non-water surfaces and from calm sea surfaces in small bays and ports inside the radar footprint lead to erroneous powers in return waveforms. Accordingly, these contaminated waveforms do not follow the so-called Brown model in conventional retracking algorithms and fail to derive qualified ranges. Consequently, the estimated water level is erroneous as well. Therefore, selecting an optimized retracker for post-processing waveforms is significantly important to achieve a qualified water level estimation. To find the optimized retracker, we employed a methodology to minimize the effect of erroneous powers on retracked range corrections. To this end, two new approaches were presented, one based on a waveform decontamination method and the other based on a waveform modification method. We considered the first meaningful sub-waveforms in the decontaminated waveforms and in the modified waveforms to be processed with a threshold retracker. To assess their performance, we also retracked the decontaminated and modified full-waveforms. The first meaningful sub-waveform and full-waveform in the original waveforms were retracked to compare the performance of the modified and decontaminated waveform retracking with the original waveform retracking. To compare the results of our sub-waveform retracking algorithms with those of external sub-waveform retracking algorithms, the (Adaptive Leading Edge Sub-waveform) ALES database was also used. In our retracking scenarios, we used the Sentinel-3A SRAL Altimeter to estimate the water levels over the study area within 10 km from the coastlines in both the Persian Gulf and the Bay of Biscay from June 2016 to October 2020. The water levels from processing L2 products were estimated as well. We evaluated our retracking scenarios and L2, as well as the ALES processing results, against the tide gauges. Our analysis showed that within 0–10 km from the coast, the first meaningful sub-waveform of the decontaminated waveforms had the best performance. We reached maximum RMS improvements in this scenario of 53% and 86% over the Persian Gulf and the Bay of Biscay, respectively, in comparison with L2 processing. Over these distances from the coast, the first sub-waveform from the original waveforms and the modified waveforms stayed in the second and third order of performance. The ALES database with an RMS ranging from 13 to 51 cm had a worse performance than all of our sub-waveform retracking scenarios.

Dynamic topography (DT) refers to the time-varying component of the sea surface height influenced by factors like ocean currents, temperature, and salinity gradients. Accurate estimation of DT is crucial for comprehending oceanic circulation patterns and their impact on climate. This study introduces two approaches to estimating DT: (1) utilizing satellite altimetry to directly observe sea surface height and (2) considering the steric and non-steric components of sea level anomalies. The steric term is calculated using salinity and temperature data obtained from local buoy data, Argo observations, and the World Ocean Atlas model. The non-steric term is calculated using GRACE Satellite gravimetry data. To estimate the assimilated DT, four methods are utilized, including variance component estimation (VCE), Bayesian theory, Kalman filter, and 3D variational (3DVAR). These methods assimilate the two aforementioned schemes. The validity of the estimated DT is assessed by comparing the calculated sea surface current, derived from the obtained DT, with observations from local current meter stations. The results indicate that the VCE method outperforms other methods in determining the final DT. Furthermore, incorporating the steric and non-steric terms of sea level in determining DT in coastal areas enhances the accuracy of estimating sea surface currents.

The efficiency of satellite altimetry in monitoring coastal areas and lakes is limited due to the contaminated waveform caused by non-water features included in the satellite footprint. Therefore, to mitigate these limitations, waveforms need to be retracked. In this research, the Generalized Logistic Function (GLF) has been introduced with Analytical (GLFA) and Numerical (GLFN) approaches to retrack the first sub-waveform. The results have been compared with those obtained from on-board retrackers existing in Level-2 altimetry data, the retracking of the full-waveform, the first sub-waveform, and the mean of the sub-waveforms using the threshold retracker. The Level-2 and Level-1B data of the Sentinel-3A (SRAL) mission for passes 141, 700, 244, and 311, respectively, passing over Vättern and Hjälmaren Lakes in Sweden, and 0–2 km distance from the coasts of the Bay of Alcudia and the Northeast Gulf of Bothnia from January 2019 to December 2022, were investigated. The results of the retracking approaches used in this study were evaluated against tide gauge data in terms of RMSE and its improvement percentage. The results demonstrate the superiority of the GLFA over the GLFN in coastal areas, while over lakes, the results are nearly equivalent. The improvement percentages of RMSE for the GLFA and GLFN compared to on-board retrackers, respectively, are as follows: for Vättern Lake, 53% and 58%; for Hjälmaren Lake, 40% and 33%; for the Bay of Alcudia, 81% and 46%; and for the Northeast Gulf of Bothnia, the GLFA shows a 36% improvement, while the GLFN yields results equivalent to on-board retrackers. The GLF has shown better performance compared to other approaches, except for Vättern Lake, which yields results almost equivalent to the first sub-waveform retracking approach. Additionally, the mean of the sub-waveform retracking approach by making use of the threshold algorithm has mostly demonstrated weaker performance compared to other methods.