## 5 Sentinel-1: Opportunities
### DEM Estimation and Related Products  
The use of interferometric techniques for the evaluation of a DEM is in principle replicable with Sentinel-1 data. The difference in spatial resolution in stripmap mode (3 m for COSMO-SkyMed, 5 m for Sentinel-1) will cause a corresponding reduction of the resolution of the DEM from 9 m to 15 m. Such a limitation is not significant for many of the applications proposed in the WARM-SAR project.  
As discussed in Section 2, both optical and SAR data can provide observations of the reservoirs' retention area, while, in the absence of bathymetric information, the retained volumes remain unknown. To overcome this limitation, it is possible to use relationships between reservoirs' storage volumes and areas, which in case of homogeneous morphology can be valid for an entire region. For instance, an area-volume relation was developed in literature [34] performing an extensive bathymetric survey in the Upper East Region of Ghana:  
\[\text{Volume}=0.00857\text{ area}^{1.4367} \tag{2}\]  
Starting from SAR-derived data, a regression analysis was applied in order to obtain an area-volume relationship for the Yatenga district [15]:  
\[\text{Volume}=0.1012\text{ area}^{1.167} \tag{3}\]  
In order to assess the potentiality of Sentinel-1 data for this kind of application, the DEM acquired in the WARM-SAR project was resampled at the Sentinel-1 scale and a new dataset of area-volume for the Laaba reservoir was built. This dataset was compared to the one obtained for the same reservoir using the DEM extracted from COSMO-SkyMed satellite imagery in the WARM-SAR project framework.  
The analysis of the DEMs was performed computing the areas of water surfaces at fixed heights, with a step of 0.3 m. The water volumes stored in the reservoir for each step were calculated as a sum of those contained in each pixel of the water surface, which is assimilated to a water column, whose height \(h_{wc}\) is given by:  
\[h_{wc}=h_{c}-h \tag{4}\]  
where \(h_{c}\) is the elevation of the equipotential surface traced by the basin, and \(h\) is the DEM height corresponding to the considered pixel. The water volume \(V\) contained in the whole reservoir is then estimated as the summation of all the volume contributions, given as the product of the pixel area \(S_{i}\) and the water column height \(h_{wc}\):  
\[V=\sum_{i}S_{i}\times h_{wc} \tag{5}\]  
In Figure 3, the results obtained through the DEM analysis and the application of the area-volume relationship proposed in [15] for the Yatenga region are plotted. The overall trend is similar for both the analyzed DEMs. An apparent anomaly occurs for low water surface areas in the Sentinel-like DEM (for example, three blue dots refer to the same area but to different volumes). This is because the lowest area values correspond to few pixels, which in the Sentinel-1 DEM have a surface of 225 m\({}^{2}\). The higher resolution of COSMO-SkyMed-derived DEM made possible to compute more reliable values of the retained volume for such areas. This effect depends both on the morphology of the region and on the interpolation that produced differences in height between adjacent pixels greater than 0.3 m.  
The above described effect allows the estimation of the range of areas that can be safely retrieved by Sentinel imagery. In principle, reservoirs covered by some thousands of square meters can be effectively monitored. We can also remark that the area-volume relationship proposed in [15] underestimates the reservoir volume when the water surface is in the range between 1000 and 20,000 km\({}^{2}\).