## 3 Methodology
### Definition of Snowpack Wet-Snow Dynamics
#### 3.1.1 SAR Processing
Therefore, we selected several summer images in four combinations: (1) the average valueof all the summer months of the subsequent year; (2) the average value of only the images that have 15 previous days without rain of all the summer months of the subsequent year; (3) the average value of all the summer months of the previous year; and (4) the average value of only the images that have 15 previous days without rain of all the summer months of the previous year. The results of the sensitivity analysis indicated that no big differences were found between the four options tested when selecting reference imagery (Figure A1, see Appendix A). When analyzing them in pairs, the average difference was 0.34 dB, ranging from 0.33 to 0.36 dB. Finally, we decided to select the average value of the previous summer as reference dataset. There were two reasons: its calculation simplicity and the fact that this constitutes antecedent information to the analyzed snow season and therefore can be used in near real-time applications. * Wet-snow threshold definition Once the difference image (\(\Delta\sigma\)) is derived, the threshold to identify Wet-snow conditions needs to be fixed. Thresholds in the range from \(-2\) to \(-3\) dB have been previously applied [34; 94]. However, the particularities of Mediterranean mountain landscapes and seasonal behavior require a re-testing of the threshold in order to analyze dry and wet snow in these areas and to compare the results with other study areas. make this threshold difficult to verify. This wet-snow threshold was obtained by evaluating the two distributions of wet snow and dry/non-snow pixel values and identifying where they overlapped [95; 90; 95]. This overlapping defines the minimum threshold value to identify whether a pixel is dry or wet. In our case, more than 2500 pixels of the area of the Sierra Nevada were chosen as regions of interest and their statistics were calculated to finally obtain this threshold value. Different dates were chosen from several years. This allowed us to consider more than one type of snow conditions and more than one season, making the results more robust. The selected scenes correspond to the following dates: (i) 5 December 2016 (ii) 5 March 2017, (iii) 16 February 2018, (iv) 28 February 2018, (v) 12 April 2019, and (vi) 24 April 2019. Threshold values varied from \(-2.20\) dB to \(-3.88\) dB for the different selected images, as can be seen in Figure A2 (see Appendix A). Average values indicate a threshold value of \(-2.83\) dB. Based on this result and for reasons of simplicity, we have used a \(-3.00\) dB threshold. * Cross-Evaluation To evaluate the accuracy of the retrieved wet snow maps, we followed the methodology proposed by [34]. This approach uses snow cover maps derived from Landsat, which have been commonly employed as benchmarks for assessing snow maps across different sensors [96]. The method assumes that on dates in which all snow is wet, both maps should coincide. To assure this hypothesis, we have chosen for the comparison late dates during the long-lasting spring melting cycle. In this way, a uniform warming of the snowpack without it undergoing cooling processes in the higher layers is guaranteed. Seven dates were chosen according to the coincidence within a 2-day time window, ensuring the availability of both datasets: (i) 5 March 2017, (ii) 17 March 2017, (iii) 10 April 2017, (iv) 17 April 2018, (v) 11 May 2018, (vi) 6 May 2019, and (vii) 18 June 2019. In this comparison, an elevation threshold was applied to wet snow maps to remove single outlier pixels at low elevation. This threshold corresponded to the snow line, which was defined for each wet snow map as the elevation when a significant change was observed in the normalized elevation histogram of the image. Binary Landsat snow cover maps were generated using the methodology proposed in [4] for the study area and assuming that a pixel was covered by snow if the percentage was greater than 0.75 [34].