## Appendix C Results of the Snow Imagery Pre-Processing  
The image pre-processing approach proposed in this study consisted of two steps, (1) image content enhancement and (2) radial lens distortion correction. Here the results of the outlined image-pre-processing tests are presented. 1. Contrast enhancement  
The improvement that pre-processing brought to the SfM framework was qualitatively assessed by the number of valid matched points between overlapping image pairs among all the test subsets. Matched points are defined as the common points identified between images by the feature tracking algorithm [35]. Contrast enhancement over the tests performed increased the number of valid matched points from 10% to 105% depending on the tested image and the quality of the reconstruction selected. Both _low_ and _high_ reconstruction quality settings within Photoscan showed improvement in the number of matches (Figures A2 and A3). The increase on matched points depended on the native image exposure, image quality, image format used for pre-processing (JPEG or RAW), and intensity of enhancement. The impact on the minimal set-up of areas Sval1, Sval2, and Green1 was less (between 20-40%) compared to the advanced set-up areas. Excessive image enhancement often resulted in more outliers and noisier reconstructions, particularly for sastrugi-sculpted snow on overcast days (Figure A4a,b). This is attributed to the matching of noise pixels generated from pre-processing poorly lit and high-frequency JPEG imagery instead of matching enhanced snow features. By setting a lower reconstruction quality in Photoscan (e.g., _medium_ to _low_), the images are downsampled, reducing the effect of noise but also the number of matched points (Figure A4a-c). For smooth and featureless areas mapped with the minimal set-up, the content enhancement was, however, essential providing key matching points on previously undetected relief features, as seen for area Green1 (Figure A3). For the advanced set-up, content enhancement had mostly positive effects, providing extra matched points while maintaining accurate reconstructions (Figure A2a-d). The effects of the pre-processing were not as crucial as for the minimal set-up since areas were mapped under relatively positive conditions (clear fair sky and/or slightly sculpted snow) with improved sensor capabilities. Nonetheless, negative results were observed when content was overenhanced or oversharpened, resulting in a higher point count but noisier and inaccurate reconstructions due to generated pixel noise (Figure A4d-f). 2. Radial lens distortion correction  
The overall reconstructed geometry noticeably benefited from the radial lens distortion correction phase as can be seen from a small exemplar areal subset (Figure A5). The systematic error mentioned by [33] was evident when subtracting the same DEM with and without the application of each of these approaches. Significant differences were observed towards the edge of the DEM due to radial distortion even for the best case scenario of the advanced set-up (Figure A5). The tested approaches such as the use of Agisoft Lens and the addition of oblique imagery greatly benefited the DEM accuracy, latter of which provided greatest improvement (Figure A5). To confirm the fidelity of the processed DEM compared to the non-processed, the Euclidean distance between different in situ points (e.g., rocks) at the edges was measured using the snow probe, confirming a better match than the processed one. Figure A3 also shows how fundamentally important the radial correction for the ultra-wide lens of the minimal set-up. This is seen as the relocation of the matched features across the image. Furthermore, Figure A5 shows the need to also correct such deformations in non-wide angle imagery, such as collected by the advanced set-up. 3. Image format  
The RAW format of the Nikon D3200 allowed for a wide range of manipulation that maintained image quality and generated less noise.