# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 4 Discussion
### Monitoring Vegetation Phenology
#### 4.1.1 Phenocam Data  
The phenocam imagery at Auchencorth Moss had a number of limitations for monitoring phenology. Firstly, the combined effect of camera inclination, the patchy nature of vegetation, and the strong micro-topography at the site made it difficult to examine greenness trajectories of the individual species. Although not conducted with imagery from our instrument setup, pixel-level analysis of phenocam imagery is possible and has been performed across a range of ecosystems to highlight spatial variability in vegetation phenology [42, 53, 73]. Secondly, the phenocam imagery used in the present study was not spectrally calibrated as the UAV data were. Brightness values for each band were variable across images and influenced by weather conditions during image capture. In our study, we conducted a visual inspection to screen poor-quality images from analysis (i.e., those affected by sun-glint, lens flare, variable scene illumination conditions, see Figure 2). This level of quality control is however rare in phenocam studies, where quality filtering is not standardised and a range of approaches are used [72, 93, 92]. Despite the adoption of  
Figure 9: Correlation between daily phenocam \(G_{\alpha c}\) and GPP90 for the years 2019, 2020 and 2021 (top to bottom). Plots from left to right show data for: the complete growing season; green-up; peak growing season; and green-down. Shown at the bottom of each plot is the Spearman’s rank correlation coefficient, \(\tau_{\rm s}\) (G\({}_{GC}\), GPP90) and its statistical significance: ** indicates \(p<0.01\); ** indicates \(p<0.05\); else \(p\geq 0.05\).**the recommended 'fixed white balance' camera setting and the 90 th percentile of 3-day \(G_{cc}\) values [74], the lack of spectral calibration and limited quality control of phenocam data mean that the calculated index (i.e., \(G_{cc}\)) has poor accuracy and cannot be reliably compared across sites or between discrete points in time. This limits analysis to broad phenological trends over periods of weeks, rather than direct comparison between days, where differences in illumination and meteorology during image capture likely exceed changes in greenness. Here. it is important that protocols (e.g., the use of reference panels; [72; 93]) are developed surrounding the calibration of phenocam imagery and that a standardised, robust approach for quality filtering the collected imagery is developed.  
Despite these caveats, phenocams are valuable tools for monitoring peatland phenology. In this study, phenocam-derived \(G_{cc}\): (i) captured the seasonal trend in greenness at the site; and (ii) displayed a strong correlation with spectrally calibrated UAV data. The near-continuous temporal coverage and automated monitoring facilitated by phenocams allow for large amounts of data collection, which are particularly valuable for estimating the timing of phenological stages and monitoring change [34; 35]. Furthermore, phenocams are often installed alongside EC setups [43], and as such, can provide important datasets to study the impact of phenology on inter-annual variability in CO\({}_{2}\) flux (e.g., ref. [94]). Improving the understanding of changes in phenology on peatland carbon balance will be key to predicting the long-term response of these ecosystems to climate change.