## 3 Methodology
### Definition of Snowpack Wet-Snow Dynamics
#### 3.1.1 SAR Processing  
In this study, we followed the change detection approach by [PERSON] [34; 79; 80] to differentiate wet snow phases within the snowpack. The SAR intensity signal depends on the following parameters: (i) the sensor characteristics; (ii) the snowpack properties; and (iii) the ground properties. The interaction of the SAR signal with the snowpack is determined by the presence of water particles in the snowpack. As moisture increases, the SAR backscattering reduces in intensity since part of it is absorbed by the water particles [43; 80; 81]. The followed approach foresees the use of two images (i) a reference image acquired during snow-free or dry conditions [19; 34; 47; 80; 82]; and (ii) an image of the period of interest, such as the melting season. The difference of the two images allows the detection of main changes such as those related to wet snow presence and reduces the influence of other factors such as the topography. In this direction, the two images need to be acquired with the same geometry [83]. The methodology can be described in three main steps: (i) SAR image preprocessing; (ii) reference image selection; and (iii) wet-snow threshold definition. * SAR image preprocessing  
The time serial S-1 SAR images were processed to obtain the operable ortho-corrected product with a pixel size about 10 m \(\times\) 10 m [84]. We use the Google Earth Engine platform, which included the S-1 Toolbox to preprocess the \"COPERNICUS/S1 GRD\" collection. In particular, the interferometric wide swath (IW) with polarization VH was chosen. The cross-polarization VH was selected because it is highly sensitivity to changes in water content and therefore sensitive to wet snow within in the snowpack [43; 44; 85]. The pre-processing applied in GEE consists of (i) GRD border noise removal, (ii) thermal noise removal, (iii) radiometric calibration, and (iv) terrain correction. Moreover, a threshold filter of \(-\)25 dB was applied to consider the noise floor of the sensors [86; 87]. Additionally, areas in layover and shadow considering the local incidence angle (LIA) for each pixel were masked [88]. SAR images were resampled to 30 m \(\times\) 30 m using areal interpolation. This resolution was considered optimal for snow studies over this area [3]. In addition, this spatial resolution contributes to reducing the speckle noise in S-1 SAR imagery. * Reference image selection  
The derivation of wet snow pixels in a certain area was based on a change detection approach by comparing the image under analysis with a reference image [79; 80], which was acquired with the same geometry on a date where the areas were supposed to be covered by dry snow or non-snow. This approach has the advantage of removing common features of the images such as topography that can influence the backscatter signal. Therefore, the first step was the definition of this reference image. For this reference image, two approaches have been used, either using images where it is certain that the snow is dry, for example in the month of January, or images where there is no snow such as July or August [34; 79; 80; 89; 90]. This is due to the fact that the backscattering in both cases is similar as dry snow is transparent for a C-band signal [48; 91; 92]. Therefore, it was important to objectively define our period of reference. A sensitivity analysis was carried out to identify the best period for the reference images [93]. Indeed, the characteristics of the snow in this Mediterranean area, with several accumulation-ablation cycles throughout the year and in general quick melting processes, makes it difficult to select a dry-snow winter image.