## 5 Discussion
### Implications for Geomorphic Analysis  
Our analysis of DEM quality with respect to image texture is restricted to the flat salar. Here, quality suffers primarily from lack of image texture, leading to correlation blunders across all algorithms and parameters. Although increasing the CK size appears to reduce DEM variance (improve DEM quality) on the flat salar, we also see that these larger CKs smooth the landscape and reduce high curvatures (Figure 9). However, the question remains: where are these high curvatures actually occurring on the landscape, and do their spatial patterns inform us on the DEM quality for geomorphic applications from the different algorithms and parameters? Figure 11D-E show that there is higher PAN variance in valleys, ridges, and even on some hillslopes, which indicates higher quality DEM results in these geomorphically relevant locations. Higher image texture here is caused by erosive features and lighting differences at these areas of high curvature and changing aspect. This is a good sign, and to answer the posed question in greater detail, we analyze the noise patterns in the tangential curvature. Our study area is geomorphically smooth, with only localized rocky outcrops and no vegetation that could lead to widespread rough optical DEM texture (Figure 1). Therefore, high curvatures should mostly be restricted to ridge crests and valley bottoms. To identify the high curvature locations, we took the 3 \(\times\) 3 pixel window tangential curvature map for each SPOT6 DEM and differenced it with a Gaussian-smoothed (standard deviation of 1 and cutoff at 5 \(\times\) 5 pixels) version of itself. In Figure 12, we observe orthogonal (approximately N-S and E-W oriented) artifacts in the BM algorithm (the linen-cloth hillshade texture previously referenced), which obscure the ridge and valley high curvature areas. These artifacts are somewhat reduced (lower magnitude difference compared to the Gaussian-smoothed version) at larger CK sizes (Figure 12F) but are still visible, and there are still many high curvature areas apparent on smooth hillslopes. In the MGM results (Figure 13), we see the smallest CK (5 \(\times\) 5 pixels. Figure 13B) produces many localized blunders (spikes and pits shown as dark salt-and-pepper colors scattered randomly). However, going up to the 9 \(\times\) 9 pixel CK (Figure 13F), these blunders are smoothed, and the darker colors (high curvature) in deeply incised valleys become apparent, with minimal high curvatures on hillslopes. This implies that the MGM is producing a less noisy, more geomorphically useful result, particularly with a larger CK in our landscape, where the hillslopes have lower texture (Figure 11E). For a side-by-side comparison of the curvature differences for the two matching algorithms, see Figure S7. The finer features identified with a smaller CK used in the MGM algorithm are in agreement with [PERSON] et al. (2018), who also noted similar blunders (spikes and pits) in DEMs produced with the MGM approach in low-texture areas where the algorithm interpolates disparity values. We cannot say what the true landscape curvature is without additional data (e.g., lidar), but we can say that there is a trade-off between smoothing and identifying the sharper features in the landscape, and the MGM algorithm appears (from the curvature difference map view) to better identify ridges and valleys, where we know the highest curvatures should be in the absence of bedrock outcrops or other high-curvature hillslope features. It is well-known that higher resolution DEMs (e.g., from lidar) are able to discern finer scale features of geomorphic interest (e.g., ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018); thus, the limits of satellite data should continue to be pressed.