[PERSON] and [PERSON] [15] estimated water depth from decades of radiometry images by bottom albedo signal, where it successfully retrieved depth values with a coefficient of determination (R\({}^{2}\)) of 0.90 for all the depth values sampled and R\({}^{2}\) of 0.70 for a deep depth range to 20 m. [PERSON] [1] estimated water depth for each pixel and it was found that the correlation coefficient between actual depth and estimated depth was 0.94, with Root Mean Square Error (RMSE) of 2.71 m. Based on a polynomial model using high resolution multispectral IKONOS data, the blue and green ratio of wave bands were identified that were constant for all bottom types. The fluctuation in water depths of the lake leading to changes in the surface area as well as the different geometric features of the lake was estimated by classification of the imagery data in spectral reflectance [16]. A few studies applied this method to deep waters in oceans [17; 18] and seas [8; 19; 20]. Satellite altimetry is a remote sensing technique that has been successfully used to derive water level data in oceans and inland water such as reservoirs, rivers, floodplains, and wetlands, providing data for more than 25 years. It was originally developed to measure ocean surface topography and has been used successfully to obtain valuable information for inland hydrology by estimating the variation in lake water levels [21; 22; 23]. The accuracy of altimetry based water levels can vary from 5 to 80 cm depending on the altimetry data used (i.e., from Topex/Poseidon and ERS-2 to Envisat and Jason-2 [24]. Many applications of satellite radar altimetry to monitor water level and storage variations of inland water bodies are available in the literature. [PERSON] [25] used a radar altimetry technique in combination with radiometer imagery to estimate stored water volumes and combined this with limited in situ data for deferent water levels; the researcher concludes with an area-level relationship with high correlation coefficient. [PERSON] [26] used a radar altimetry technique with radiometer imagery over AHD Lake from 1998 to 2004. A good playground in this paper was also found in the water level fluctuation of Toshka basins during the period when the Toshka Lakes were created by diversion of AHDL water. The water volume of the reservoirs derived from the integration of AHDL specific area-level relationship for the periods 1999-2002 and 2005-2009 was found with correlation coefficient (R\({}^{2}\) = 0.9985). The water contour was estimated from Landsat images based on a SWIR band, while the water level was from satellite altimetry, and the Heron formula was proposed for estimating water volume variations. In all these works, water level and water surface information were used to estimate stored water volumes, in combination with limited in situ data pertaining few different water levels. [PERSON] [27] also applied the Heron method for the period from 2014 to 2015 to estimate water volume variations relative to reference water level for the southern part of AHDL in Egypt. She used available free Landsat and radar altimeter datasets with R\({}^{2}\) = 0.98, and RMSE values were 13.2% of the mean volume variation. The main objective of this study is to evaluate approaches to estimate water depth, surface area, and water volume of AHDL using Sentinel mission data for the first time. Particular attention is given to a feasibility study about inferring water depth from optical observations in this particular deep-water context. The remote sensing resources used in the images are composed by multi-spectral imaging radiometers (in the optical domain) and satellite radar altimetry (in the microwave domain). The data used are open and easily accessible these days, and represent a unique resource for retrospective and present data, offering a great opportunity to experiment some remote monitoring of reservoirs. The paper is structured in six main sections.