## 3 RS Applications in Ocean
### Sea Level (SL)
#### 3.4.2 Altimeter  
The first generation of altimeters measured SL at the nadir angle along the ground track with low spatial resolution. Consequently, it was required to merge obtained data of multiple acquisitions to provide full coverage over relatively large study areas. Therefore, they had limitations in estimating SL at the mesoscale and short time intervals [229; 237]. Moreover, the low spatial resolution of traditional altimeters and the waveform tracking effect at the sea-land boundary made these systems less applicable to near-shore regions [48]. To overcome these shortcomings, new principles were introduced to obtain high-resolution and wide-swath altimetry data. These new principles resulted in the invention of SAR and InSAR altimeters [49; 50].  
Generally, preprocessing (e.g., orbital correction and outlier observation removal) and geophysical correction (e.g., dry/wet tropospheric correction, sea state bias correction, dynamic atmospheric correction, ionospheric correction, and tidal effect correction [242; 243; 244; 245; 246]) steps are required to prepare altimetry data for further SL studies. For example, [PERSON] et al. [247] employed the Chinese Tiangong-2 Interferometric Imaging Radar Altimeter (InIRA) data to measure SL. The results were compared with one-dimensional SL data from previous altimeters of Jason-2, SARAL, and Jason-3. Then, the observed system  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline Passive & GNSS-R & Provides frequent all-weather data for regional to global studies & Requires data collected over a long period to enhance the accuracy of the SL estimation \\ Active & Altimeter & All-weather data acquisition with global coverage & Relatively coarse spatial resolution and low temporal resolution \\  & Gravimeter & All-weather data acquisition, global coverage, and unique ocean & Very coarse spatial resolution and unsuitable for regional studies \\  & & mass measurements & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different RS systems for SL Mapping along with their advantages and disadvantages.  
atic and parametric biases were analyzed and removed to obtain a standard deviation of 8.1 cm in OWH estimation, indicating the promising results of the InIRA SL measurements. Additionally, [PERSON] et al. [248] investigated the potential of the CrySat-2 SAR altimetry for SL retrieval along the coasts of the German Bight and West Baltic Sea. The computed SL values were compared with in situ and regional models between 2010 and 2016. Their results showed an acceptable consistency with the regional ocean model, with a standard deviation of the differences of 24 cm. Finally, [PERSON] et al. [249] investigated the coastal vulnerability of Bangladesh using the Composite Vulnerability Index (CVI). To this end, satellite altimetry data from TOPEX/Poseidon (1992-2006) and Jason 1-3 (2006-2017) were combined with other parameters (e.g., land use, mean tidal range) to map the CVI along Bangladesh's coast. It was observed that 16% of the total coastline (87 km) was highly vulnerable. Finally, [PERSON], et al. [250] developed a fusion approach based on deep belief network to integrate satellite altimetry and tide gauge data. The results revealed that the proposed method performed well when limited along-track altimetry and gauge data are available. Furthermore, it was observed that the distribution of altimetry and gauge datasets had a lower impact when the deep belief network was implemented.