# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Waveform Retracking
#### 3.2.2 Official Retracking Algorithms
Furthermore, although [58] used the LRM mode of Sentinel-3A, the OCOG retracker with Sentinel-3 and SARAL mission's data retrieved the measurements with the highest accuracy in terms of RMSE and R\({}^{2}\), followed by ENVISAT and ERS-2. Also, in the only publication where Sentinel-6 mission data were used, the results show that Sentinel-6 with the Ocean retracker has higher accuracy than Sentinel-3 with the Ocean algorithm in terms of RMSE and R [71]. Together with the Ocean algorithm of Jason-3, these two missions extract the most accurate water levels. Also, the evaluation of CryoSat-2 water level measurements from different retracking scenarios over inland water bodies was conducted in five publications [62; 63; 68; 69; 70]. The use of Full Bit Rate (FBR) SAR data in combination with the use of five proposed empirical retrackers show that the use of proposed retrackers is preferable to the OCOG and Threshold retrackers with a threshold of 0.75 [68]. In addition, it is shown that the use of the SAMOSA2 retracker is not recommended for inland water level monitoring since it is designed for oceanic applications. CryoSat-2 measurements with the empirical retrackers are superior to T/P and Envisat estimates but less accurate than those from SARAL [68]. SAMOSA+ and Threshold algorithms were used to derive the water levels from the Fully Focused-SAR (FF-SAR) waveforms with additional either pulse-peakiness or waveform-fit filtering [69]. Thus, they show the potential of the FF-SAR algorithm to extract inland water levels of really narrow canals, with high accuracy. This was the first time that satellite radar altimetry data were used to measure water levels in ditches a few meters wide. In [62] is shown that the use of Threshold and SAMOSA3 retrackers at the first and the mean-all sub-waveforms of SAR mode is appropriate to retrieve water level variation of complex shaped and small lakes, while over larger lakes the full-waveform retracking leads to better results. Also, the evaluation of the SAMOSA+ retracker, which is recommended for inland water level monitoring, would be really interesting. On the other hand, the most precise result for Envisat retrackers was obtained from the Ice-1 algorithm. Also, the importance of outlier detection before applying CryoSat-2 data is highlighted by [70]. Thus, the RMSE of CryoSat-2 water levels using SAMOSA2 retracker was greatly improved from 0.70 m to 0.27 m. In the last research, CryoSat-2 data were retracked with an empirical Narrow Primary Peak Retracker (NPPR) [23], showing that the RMSE between satellite and in situ observation is 0.38 m.  
Furthermore, the Jason satellite measurements using Ice-1/ICE retracker were validated from different retracking scenarios in three publications [78; 80; 140]. In [80] are highlighted the significant advantages of OL tracking mode using Jason-2 and Jason-3 data over French rivers and its high accuracy measurements over inland water bodies. Also, using the same retracker with Jason-1 and Jason-2 satellite water level measurements showed that the water level was highly correlated with measurements obtained by Landsat images [78]. Finally, Convolutional Neural Networks (CNN) using Jason-3 data obtain water level measurements [140]. It is the only research, among the 48 publications examined at the scoping review, in which a machine learning approach was used to extract water level heights. The results indicate the strong potential of CNN to measure surface water levels since they are more robust than using the OCOG/Ice-1 algorithm time series. Table 5 summarizes all the previous publications that use official retracking algorithms of radar altimeters for inland water surface height monitoring.