## 4 Discussion
It is therefore difficult to determine an optimal recording time. All 15 study sites were considered together in the present study, and this mix was regarded as a representative forest sample. However, there are significant differences in accuracy between the different canopy cover intensities. If the different canopy covering ratios are taken into account [8; 70; 71], the results can be considered in a more differentiated way. Therefore, further studies should include objective forest stand metrics to compare and correlate different overshadowing situations. A possible method is the Fisheye photos classification algorithm [96], which was used in [16; 21; 23]. As the reception conditions under large trees are particularly challenging, this is also seen in the considerable variation of error values for all GNSS receivers (Figure 10). There are receiving conditions, especially on the north slope in heavily overshadowed, dense forest, where all types of receivers face challenging conditions for deriving an accurate position. There is a strong significant correlation between canopy density and positional accuracy [21; 23], and the accuracy achievable can range from millimeters to tens of meters depending on the operating environment [17]. The dual-frequency smartphones performed best on average across all recordings in this study. It is difficult to determine whether this is due to the multi-frequency technology or progress based on the development of antennas, more sensitive sensors, or improved algorithms. However, there are many differences between individual smartphones. The first multi-frequency smartphone, the Xiaomi Mi 8, achieved a higher accuracy (DRMS: 7.22 m) in this study than the average of the single-frequency smartphones (DRMS: 9.13 m), but individual single-frequency smartphones, such as the Samsung A7 (DRMS: 7.22 m)or the Huawei P20 (DRMS: 7.22 m), achieved a slightly higher accuracy than the Xiaomi Mi 8, as well as the Xiaomi Mi 8 pro (DRMS: 8.55 m). In other studies which compare smartphones under a canopy, the Xiaomi Mi (RMSE: leaf-on: 6.13 m, leaf-off 4.10 m, open arena 2.23 m) outperformed the Huawei P20 lite (RMSE: leaf-on: 8.12 m, leaf-off: 11.44 m, open arena: 3.44 m) [71]. Compared to studies of older smartphone generations under a leafy canopy, widespread accuracies also occur (RSME\({}_{xy}\): ZTE Blade: 11.45 m; LG G2 Android 4.4: 9.3 m; LG G2 Android 5.0: 6.74 m; Sony M4 Aqua: 7.48 m; Lenovo Yoga 8: 4.96 m) [89]. However, it must also be considered that the results of the different tests only offer an indication and cannot be directly compared with each other. The studies did not occur under the same conditions, and there are no objective indicators available to evaluate the environmental reception situation. The multi-frequency Xiaomi Mi 10 clearly stands out compared to single-frequency smartphones. It has a 31% lower absolute error under the CEP and a 34% lower absolute error under the CEP\({}_{95}\) compared with the best single-frequency phone. In general, it can be assumed that multi-frequency mobile phones can improve reception under forest conditions. At the same time, it should be noted that this is highly dependent on the capabilities of the individual smartphone and should not be over-generalised. For example, the developers at Xiaomi have significantly improved the reception quality of the Xiaomi Mi 8 and Xiaomi Mi 8 Pro in the Xiaomi Mi 10. In other studies, the Xiaomi Mi 9 showed lower accuracies than the Xiaomi Mi 8 [87]. The overall potential as a combination of available frequencies, hardware, and software will certainly not be exhausted yet. Further research studies on upcoming smartphones should provide more information. It should be expected that smartphones with identical or very similar design will produce identical results. Therefore, a comparison was made between identical models (Samsung Xcover 3s) and smartphones with the same GNSS chip (Xiaomi Mi 8 and Mi 8 Pro).