## 4 Combining Ascending and Descending Sentinel-1 Images to Analyze  
**Unstable Areas**  
The 4 zones showing displacement (Figure 4) are located in the inactive part of the mine, the Atalaya pit, and its surroundings. We focus on these passive elements because, ideally, they should remain stable and, in case there is movement it should be under control  
Figure 4: Historical review of Riotinto mine with 3 ESA satellites: ERS-1&2, ENVISAT, and Sentinel-1. Result for descending orbits. Dots represent mean velocity values (cm/y, in line of sight direction, LOS) of each cell. Green dots mean stable and reddish colors mean movement away from the satellite. The numbers on the maps show four areas under deformation discussed in the following sections. The graphics show accumulated deformation in stable and unstable points. It is worth noting the coverage improvement with Sentinel-1. Figure 3: Interferometric cloud of the three constellations ERS, Envisat, Sentinel-1. The numbers in the graphs correspond to the radar images (numbered by its acquisition date) and the connection lines to the interferometric pair. The temporal and spatial baseline improvement provided by Sentinel-1 is remarkable. to prevent risks. We will focus the analysis on this area using both ascending and descending Sentinel-1 images for mapping and quantifying the displacements and the potential causes. We use the aforementioned results from the descending track 154 and, in parallel, we processed the ascending dataset corresponding to track 147, using the same software, the same period, and similar processing parameters (Figure 1). The challenge is now to combine both processing results, descending and ascending, to produce a comprehensive description of the deformation field. Due to their opposite perspective, both processing results identify the same anomalies but provide complementary information about the scene. Geometric distortions and shadows are complementary, ascending dataset performs better in characterizing eastern slopes, and the descending dataset western slopes (Figure 5). It is interesting to observe how ascending provides little information from Zone 4 (the eastern steep wall of Atalaya with western dip), but, on the contrary, it provides better resolution of Zone 3 which is bounded by slopes facing east. We assume that velocity values between \(-0.5\) and \(+0.5\) cm/y are below our resolution capacity so those are stable points. mapping areas in motion and preliminary evaluation of the deformation field. However, for accurate quantification, Vlos needs to be projected into the georeferencing system. On the other hand, the decomposition of the Vlos into the geocoordinates xyz entails a problem inherent to the image acquisition: radar satellites have a low sensitivity for displacements in the track direction, \(\pm\) 10\({}^{\circ}\) north in the case of Sentinel-1. A common strategy for Vlos decomposition is considering only cases where the north-south component of displacements can be neglected; then, Veast-west and Vz can be estimated wherever ascending and descending measurements are available [33; 34; 35; 36]. Another projection strategy applied to the study of landslides assumes that the movement occurs mainly in the direction of the maximum slope [37; 38; 39; 40]. In this work, we follow a similar strategy assuming Vlos is the slant-range component of Vslope (velocity along the maximum slope direction) in the steep topographies. With regard to flat topographies, we assume displacements are entirely in the vertical direction, so the Vlos in this case is the slant-range component of Vz (vertical subsidence). It is worth pointing out that these projections can have an amplification effect that can be very important, providing meaningless velocity values on some unfavorable topographies.