## 6 Conclusions  
This paper investigates techniques based on optical and radar sensing for the retrieval of depth, area, and volume variations in a surface water body. AHDL, one of the most challenging hot spots at both local and global levels, was chosen as a case study to apply the proposed methodology. We determined water depth, surface area, and volume variations over the time series variation in AHDL. For what concerns water depth, we experimented with a combination of optical Sentinel-3 imagery data in the green, red, and NIR bands for both instruments, SLSTR and OLCL, with field survey bathymetric data of Nubia Lake and Allaqi Khor (parts of AHDL). The developed relationship between spectral reflectance and depth was a simple regression model based on a polynomial equation, describing the functional relationship between the optical measurement and the corresponding depth. Promising correlation values were obtained, R\({}^{2}\) > 0.8 and NRMSE < 0.4. Additionally, we explored the possible use of the combination of optical imagery and Sentinel-3 altimetry water level to estimate water storage variations in AHDL by using [PERSON]'s algorithm. The developed model has R\({}^{2}\) = 0.83 and NRMSE = 0.128. Similarly, the lake's surface area in relationship with the depth was explored. Preliminary results for AHDL, shown in this paper, represent a promising experience for monitoring the change in depth, area, and volume of AHDL water. Further tests are needed to firmly demonstrate these techniques in order to enhance accuracy. In addition, further improvements are expected when integrating additional data from current and future satellite missions to generalize the relationship model to be valid for all geographic regions of AHDL. We recommend the use of Sentinel-3 satellite data with its moderate resolution to calculate the bathymetric  
Figure 15: Relation between water surface area variation and volume variation of AHDL. (**a**) Result of correlation between estimated \(\Delta\)V by Sentinel-3 altimetry water level in MCM and \(\Delta\)A in Km\({}^{2}\) “Test set” and “Validation set” estimated \(\Delta\)V by in situ water level in X axis. (**b**) Water volume variations estimated by the Heron method over a time period and during variation in water surface area with NRMSE value between test set and validation set. properties of large lakes, but the methodology still needs calibration and verification using large datasets on different lakes for a final confirmation. Additionally, smaller water bodies and rivers may require a higher spatial resolution, suggesting the usage of other missions, such as Sentinel-2. Moreover, and in general, the application of generalizable models leads to a better understanding of their potentialities, for deep water lakes and inland water. Furthermore, improved integration between deduced data from field survey and remote sensing data, such as limnology, hydrology, and ecology, leads to a wider usage of remote sensing models in scientific studies of management water resources. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] shared their thoughts to formulate the idea. [PERSON] applied the methodology and drafted the first version of the manuscript. [PERSON] and [PERSON] supervised [PERSON] during the phases of applying the methodology and writing the first draft of the manuscript. Both [PERSON] and [PERSON] provided their comments and revisions until the submission phase of the manuscript. [PERSON], [PERSON] and [PERSON] supervised [PERSON] during the implementation of the comments and revisions. All authors have read and agreed to the published version of the manuscript. This research work received funding from Academy of Scientific Research and Technology (ASRT) of Egypt and the Italian National Research Council (CNR) in the framework of the bilateral CNR-ASRT mobility project titled \"Experimentation of the new Sentinel missions for the observation of inland water bodies on the course of the Nile River\". Not applicable.