## 1 Introduction
### Photogrammetric Workflow Optimisation  
A common limitation to achieving efficient surveys mentioned in previous work, and finding important echo in time-constrained (e.g., tidal) environments, is the need for a dense and well-measured network of GCPs for the accurate scaling and georeferencing of photogrammetric results (e.g., Ref. [43]). In the absence of other external controls, this requirement is particularly important, shown by observations of decreasing DEM quality with distance increases from the nearest GCP [44; 45; 46]. Relief can be procured through the installation of fixed (i.e., permanent) targets, although it is generally only partial and, in application to the coast, implies that fixed targets are limited to dry zones [37].  
The uptake of high-precision RTK-GNSS technology for drone navigation can further improve survey efficiency. With RTK drones, image geolocations serve as airborne controls, which, despite a generally lower precision compared to GCPs and a repartition outside the measurement volume, can compensate for these caveats due to the comparatively very-large number of controls provided [47]. Previous research showed that results of a similar quality or even better can be achieved using a reduced number of GCPs when the camera positions are used as external controls [48]. Using the camera information as the only external control during photogrammetric processing forms the basis behind direct georeferencing (DG) approaches (e.g., Refs. [49; 50; 51]). The DG of photogrammetry is particularly advantageous when access to the field site and the measurement of GCPs are problematic. However, previous studies showed that DG generally comes at the cost of reduced measurement quality (e.g., lower accuracy and precision).  
The commercialization of RTK drones at competitive prices (e.g., the Phantom 4 RTK quadcopter) has reinstated large interest in determining the capability of DG approaches across different settings, including building facades, urban and rural landscapes as well as river and coastal environments. Previous studies investigated the differences between drone positioning corrections provided either in real time (RTK) from a nearby base station managed by the field operators or through RTK networks, or post-processed (PPK) using observations at known base stations [52; 53]. They also assessed the effect of the image viewing angle (nadir vs. oblique [54]), as well as changes in measurement quality with and without the provision of GCPs (e.g., Refs. [52; 53; 54; 55; 56]).  
These studies concluded that, in the absence of GCPs, systematic error mainly through vertical bias can impact the measurement quality, which was related to the imprecise calibration of internal orientation parameters (IOP). Yet, contrasting results were obtained when it comes to the number of GCPs to introduce to correct this effect. Some studies reported that one GCP was enough, although the GCP position may be important, while other studies recommended using at least three GCPs. With the exception of the effort to survey 109 points with a theodolite on a building's facades by [PERSON] et al. [52], error evaluation is generally limited to a few check points (ChkPIs), whose number and repartition may not be adequate for reliable error characterization. This, in turn, limits the generalization of the findings such that the measurement capabilities of RTK-assisted UAV photogrammetry over large and GCP-poor coastal tracts are not fully understood.