## 1 Introduction  
Topographic data in the form of gridded digital elevation models (DEMs) are fundamental data for planetary science, geology, geomorphology, hydrology, glaciology, urban studies, and many other fields. DEMs have been developed from a variety of data sources, since the early days of digitized contour maps [1; 2; 3]. One of the most popular methods for modern DEM generation at \(<\)5 m spatial resolution for local (i.e., 10-10\({}^{4}\) km\({}^{2}\)) study areas is stereogrammetry from optical satellite imagery (e.g., [4; 5; 6]), although these data can be extended to regional studies [7]. Higher quality DEMs with sub-meter resolution can be obtained from UAV flights or lidar surveys (e.g., [8; 9]), but such techniques typically cover much smaller areas (\(<\)10 km\({}^{2}\)) and may not be feasible in remote or larger study areas [10]. The accuracy and precision of DEMs are vital statistics for any use case [2; 3; 11; 12]. Thus, researchers strive for higher quality (high accuracy and precision) DEMs from avail able data sources. For optical satellite DEM generation, quality can vary widely depending on sensor- or orbital-related artifacts and biases [7; 13; 14; 15] and also from processing-related issues. Both can lead to systematic or random errors [3]. Processing issues can be caused by both poor matching between scenes in areas of lower image texture (the local variability in digital number value) and by the selection of algorithm and associated parameters used to carry out the matching. Poor quality in optical DEMs resulting from these issues is particularly concerning for geomorphic studies. Derivatives of elevation (e.g., slope and curvature) are the basis for many geomorphic analyses. However, noise in the initial elevation model propagates or even amplifies in the data products derived from it [12]. This can detrimentally impact flow routing, flood modelling, and tectonic geomorphic assessments, among other applications (e.g., [4; 16; 17; 18; 19; 20; 21]). The utility of DEMs generated from wide-coverage (single scenes often \(>\)10\({}^{4}\) km\({}^{2}\)), high-resolution (panchromatic pixel ground resolutions \(<\)2 m) satellite imagery has led to a growing patchwork of techniques, algorithms, and software (both open- and closed-source) for stereogrammetry. Much of this represents a back-and-forth between the computer vision community, often concerned with robotic guidance and autonomous driving, and the photogrammetry community, concerned with topographic representation of urban, mountain, cryosphere, and even extraterrestrial environments [22]. All of these developments are concerned with obtaining the highest DEM quality from available overlapping images. In this study, we rely on a popular open-source software for optical DEM generation, which incorporates many of the varied algorithms and techniques of several decades of ongoing developments in the computer vision and satellite stereogrammetry communities: NASA's Ames Stereo Pipeline (ASP) [23; 24; 25]. We use a \(\sim\)2700 km\({}^{2}\) SPOT6 tristero scene from 2014 and focus on smooth, diffusive mountainous landscapes that are vegetation-free and of interest for studying geomorphic processes. Our study area covers a topographically diverse region, providing a unique opportunity to better understand the relationship between image texture and DEM uncertainty. It is well-known that the success of matching two images is highly dependent on the presence of unique features within them [26; 27]. The impact of image texture, however, is often mixed with other influencing factors, such as topography and its derivatives, making it difficult to disentangle. The salt flats on the Altiplano-Puna Plateau--large, paleolake beds with virtually no relief but strong variations in image texture--serve as an ideal natural laboratory to study the quality of stereo-derived DEMs independent of topography.