# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 4 Discussion
### Monitoring Vegetation Phenology
#### 4.1.3 Problems Comparing Phenological Datasets  
Studies utilising phenological data from multiple sensors (e.g., comparing phenocam with UAV or satellite data) face a number of challenges. Firstly, the accurate comparison of datasets relies on the assumption that all sensors are observing the same vegetation phenology [56; 97; 98]. The two camera systems employed in the present study (phenomen, multispectral) had different and non-overlapping fields of view. The phenocam was mounted next to the EC tower (Figure 1a) and viewed vegetation to the north (oriented in line with phenocam protocol; [71]). In contrast, the multispectral sensor on the UAV acquired data over the predominant flux footprint area south-west of the EC tower. Despite these differences, based on expert knowledge of vegetation at the site, including extensive quadrat surveys for ICOS and other long-term monitoring activities [99], vegetation composition captured by the phenocam was assumed to be representative of the UAV survey area. Although treated with caution, the similar temporal trends in greenness of the two datasets suggest that vegetation communities monitored by both setups were comparable (see Figure 4).  
Secondly, as vegetation canopies are non-Lambertian surfaces, viewing geometry has a strong impact on the signal measured by the sensor and any calculated VIs [100; 101; 102]. In the present study, the phenocam had an oblique viewing angle, whereas the UAV imagery was collected at nadir. The size of this impact on calculated VIs can be large. A study by [PERSON] and [PERSON] [101], for example, found that NDVI calculated from measurements conducted with a viewing angle (as opposed to nadir) differed from their counterparts by as much as 50%. Studies comparing phenocam and remote sensing data also find that differences in viewing geometry lead to disparity in calculated phenological transition dates [98; 103]. Phenocams, for example, may observe a more rapid senescence of vegetation than satellite data, as their oblique viewing angle enables them to better capture senescing flowers and grass stems compared to the nadir view of aerial imagery [104]. Although the present study did not compare phenological transition dates from the two datasets, this is an important aspect to consider in future work.  
Finally, monitoring vegetation phenology from above (i.e., from nadir with the UAV, or from a viewing angle as with the phenocam) in ecosystems with complex multi storey canopies (e.g., peatlands, grasslands) can be problematic, for example because of shading, and the fact that a number of species (e.g., _Nardus stricta, M. caerulea_) green structurally from the bottom up. These structural differences can create a time lag between flux- and remotely sensed phenology datasets, as the latter will only register greening of the upper canopy. In an alpine grassland ecosystem, [PERSON] et al. [105] estimated the size of this lag to be around one week. Although in the present study, we observed strong correlations between GPP and phenocam-derived \(G_{cc}\), viewing geometry has an important influence on the relationship between VIs and CO\({}_{2}\) fluxes, particularly during the green-up stage.