*2.4. Alternative Photogrammetric Processing Scenarios*

To validate our selection of the standard processing parameters, different tests were performed, which are shown in Table 2. The first test focused on image alignment accuracy setting, which was varied from "Low" to "Highest" and encompassing "Medium" and "High" accuracy settings, while other parameters were left unchanged. Using "High" accuracy, image alignment is performed using original images. Using "Highest", the software upscales images by a factor two in each direction, while lower accuracy settings will see image resolution decreasing incrementally by a factor two. A compound test was performed in the case of "High" image alignment accuracy to assess the effect of disabling the reference pair preselection, which uses image coordinates to help finding matches.

Optimum strategies during photogrammetric model optimization were assessed through the declination of different scenarios (cf. Table 2). The Sillon de Talbert field site, with reduced superficies and a comparatively large number of well-distributed targets, was used as a testing ground. The first scenario (S-GCP) used all ground targets (*n* = 21) as GCPs with camera information disabled during processing. This scenario is equivalent to the classical approach relying solely on GCPs for camera calibration and model georeferencing. The second scenario (S-RTK-GCP) used all targets as GCPs, as well as camera information (location and attitude), for the optimization. Other scenarios using camera information were implemented by varying the number of GCPs used, hereafter referred to as S-RTK, S-RTK-1GCP, S-RTK-3GCP, S-RTK-5GCP and S-RTK-9GCP, corresponding to

using 0 (i.e., no GCPs), 1, 3, 5 and 9 GCPs, respectively. Scenarios using 1 and 3 GCPs were replicated (differentiation is done using subscripts <sup>1</sup> and 2) in that targets selected as GCPs were changed to test the effect of GCP location on model quality.

To facilitate results' comparison for the different scenarios tested and to expedite data processing, evaluations were performed on1mresolution DEMs created from the sparse point clouds. This decision was supported by the fact that dense matching has limited effect on the overall model georeferencing and geometry (e.g., 3D deformations that may be present in the sparse models following model optimization), improving mainly the representation of fine-scale features through a higher model resolution.
