## 4 Future Perspectives
### GNSS-Reflectometry (GNSS-R)
#### 4.1.2 GNSS-R for Snow Cover Estimation  
The IPT has been applied as well to measure the snow height, where multiple notches can be identified. For example, in [108], a very high correlation has been found between the retrieved and true snow height, and it has been shown that the antenna should be as high as possible to obtain better results. Some important work has also been performed by the University of Colorado in the estimation of snow depth [19,109,110]. The various experiments were performed with geodetic receivers from the EarthScope network. In [110], the authors were able to estimate the snow depth from observations of the modulated SNR to an accuracy of between 9 and 13 cm. However, as stated in [19], this technique only works well when the modulation frequency of the multipath signals can be resolved, _i.e._, when the area surrounding the GNSS antenna acts as a specular reflector or when the surface is nearly planar, large enough to encompass the first Fresnel zone (FFZ) at lower elevation angles, and free of significant vegetation. This may be a severe limitation for the subalpine region populated with trees, but may not be for the alpine region, in particular in winter when many surface irregularities are filled with snow. For a ground-based installation, the FFZ for low elevation angles is relatively small (e.g., only a few tens of meters for an antenna 2 m above the ground), and gets larger with the antenna height [19]. The footprint of the antenna will thus depend on the snow height, and will decrease as the snow height increases. For non-horizontal sites, the inclination of the ground should also be taken into account. One possibility suggested in [19] is to observe bare ground in summertime to estimate the vertical distance to the ground. Other possibilities include the use of LIDAR technology or elevation maps. Other parameters that affect this measurement technique are the humidity of the soil (the variation in vertical distance to the ground from this effect is only 2-3 cm) and to a lesser extent the snow density [19]. In the same context, an algorithm has been developed to measure the snow depth based on the geometry-free linear combination, L4, which is the difference of the phases of the L1 and L2 carriers. This algorithm is motivated by the fact that L1 and L2 phases are always recorded by GPS stations, whereas it is not the case for the SNR. However, although the results obtained with this algorithm match those using SNR-based algorithm, the precision is lower [111]. Further studies will include the newer GNSS signals, expecting better precision. Very few studies have been performed which measure not only the snow depth, but also the snow density, from which the SWE can be obtained. In [112, 113], the author proposes an algorithm based on a simple ray model that includes a specular reflected signal along with a direct signal, and includes a nonlinear least squares fitting. The initial results seem promising, but require validation. Indeed, the estimations of the SWE were well within the common values in the region, but there were no _in situ_ field measurements to estimate the validity and accuracy of the results. We note here the similarities between the above method and the radar measurement methods (see Section 3.2.3, meaning that radar and GPR measurement methods are a good reference for the consideration of measurement errors which may be encountered in GNSS-R). A recent and innovative technique was proposed to characterize the internal layering of dry snow masses [114]. This technique uses the Fourier Transform of the cross-correlation results (also called waveform) and shows a relation between the frequency stripes and the depth of the different snow layers. This technique has been validated by measurement campaigns, however, the current precision is too low for the alpine environment (about 10 m). This is still a preliminary work, and the precision may be improved in the future by improving the model and the use of newer GNSS signals.