## 4 Discussion
As such, the strong correlation of between SRS-sPRI and \(\alpha\) provide even more evidence that PRI values from the SRS sensors can serve as a proxy for efficiency. Although sPRI was well correlated to light use efficiency parameters, their association appeared to vary within different developmental stages. The close correlation between sPRI, LUE (\(R^{2}=0.54\)) and \(\alpha\) (\(R^{2}=0.67\)) during maturity show ability to track the facultative changes of xanthophyll cycle pigment pools. This was further supported by the weak correlation between _f_APAR and sPRI (\(R^{2}=0.14\)), indicating the low effect of vegetation structure that drives constitutive pigment pool changes [38]. During leaf senescence sPRI was less strongly correlated to efficiency parameters (light use efficiency \(R^{2}\) = 0.43, quantum yield \(R^{2}=0.51\)) and correlated with _f_APAR (\(R^{2}=0.52\)), suggesting that the PRI signal is influenced by canopy changes. In deciduous vegetation, senescence marks a period of substantial changes in canopy structure composition as leaves start to senesce and fall. During this marked period of structural variation, senescence PRI would most likely represent a mixture of facultative and constitutive changes, with constitutive changes confounding the physiological signal from leaves. This aligns with studies [79; 80] suggesting that dominant effects of canopy structure can confound physiological leaf traits detection. Future parametrization of LUE models will need to take into account PRI's contrasting effect on photosynthetic activity during different time periods [21]. The high temporal resolution of the automatic SRS-PRI sensors and their ability to serve as a proxy of LUE at different temporal scales could be useful towards the better parametrization of LUE models. A close analysis of PRI and light use efficiency parameters provided some underlying mechanism affecting efficiency at our site. The close relationship of \(\alpha\) and GPP during senescence indicate that efficiency changes are influenced by canopy structure, this being the primary driving mechanism of GPP changes. Coinciding efficiency peaks present in sPRI, LUE and \(\alpha\) time series were associated with high water availability (soil and atmospheric), lower temperatures and low PPFD. In the context of PRI, we can infer that periods of low PPFD would account for the little need for non-photochemical quenching as canopy would not be exposed to saturating light conditions. Diffused light conditions have been shown to result in higher canopy light use efficiency [81; 82; 83] and could explain the increases in LUE rates. This effect of diffused light on light use efficiency rates has been difficult to account for in remote sensing modeling as most of the driving data come from non-continuous satellite-based imagery. Continuous PRI measurements have the potential to solve this, as illumination can be used to classify continuous datasets into illumination groups and used to empirically describe the effects of different light conditions on LUE through time. Many described canopy scale variables have been described that can confound PRI signals including canopy structure, view and illumination angles, soil types, shadow fractions, phenology and pigment pools [19; 21; 38; 84; 85]. One approach to mitigate some of these effects is through the normalization of PRI (calculating daily \(\Delta\)PRI) [86]. This has been shown to reduce the effects of canopy structure, vegetation cover, and soil background. Although not applied in this study, PRI normalization is a topic of future work as it may improve PRI-LUE relationships from continuous sensors. Some of the variables previously mentioned also impact our sPRI dataset. We can also assume that our continuous measurements will have a wide range of impacts as the higher temporal resolution will naturally capture more of these interactions.