## 3 Results
### Comparison of Light Use Efficiency Parameters and Canopy Structure Parameters  
SRS-sPRI values were compared to LUE and \(\alpha\) to determine its suitability as a proxy of light use efficiency. Comparison of SRS-sPRI and LUE time series (Figure 7a) showed similar seasonal patterns and were significantly correlated (\(R^{2}\) = 0.62, \(p\) < 0.05). PRI was also able to resolve periods of high LUE changes. However, smaller fluctuations observed throughout the LUE time series were not as clearly resolved by the SRS sensors. Further separation of the LUE-SRS-sPRI relationship by phenolic stages (Figure 8a) showed higher correlation during maturity (\(R^{2}\) = 0.54) than during leaf senescence (\(R^{2}\) = 0.43). Also, higher variability in SRS-sPRI values was observed during maturity (\(\sigma\) = 0.15) than during leaf senescence (\(\sigma\) = 0.069). Correlation analysis of SRS-sPRI and \(\alpha\) (Figure 7b) showed a strong significant correlation (\(R^{2}\) = 0.72, \(p\) < 0.05). Temporal dynamics of \(\alpha\) were also able to be better tracked by SRS-sPRI, especially during maturity (Figure 8b) (\(R^{2}\) = 0.67). During leaf senescence (Figure 8b), SRS-sPRI started to increasingly depart from \(\alpha\) values leading to a weaker relationship (\(R^{2}\) = 0.51). Comparison of LUE and \(\alpha\) (Figure 7c) resulted in highly correlated arrays (\(R^{2}\) = 0.88, \(p\) < 0.05). The \(\alpha\) time series followed the dynamic changes in LUE but with less short term variations, likely due to the 2-day temporal binning methodology used to calculate the \(\alpha\) term. Figure 6: (**a**) Time series and (**b**) regression analysis of gross primary productivity (GPP) and apparent canopy quantum yield (alpha). Correlation between GPP and alpha was analyzed for the whole season (black), maturity (green), and leaf senescence (brown). Figure 8: Seasonal (black), maturity (green), and leaf senescence (brown) correlations of scaled photochemical reflectance index from SRS sensors (SRS-sPRI) and (**a**) light use efficiency (LUE), and (**b**) apparent quantum yield (alpha). Dotted lines show linear fits. Figure 7: Time series (left panels) and regression plots (right panels) of (**a**) light use efficiency (LUE) and scaled photochemical reflectance index from SRS sensors (SRS-sPRI), (**b**) apparent quantum yield (alpha) and SRS-sPRI, and (**c**) LUE and \(\alpha\). Dotted lines in regressions plots (right panels) show linear regressions fitted to the data. Parametrization of light use efficiency must be done on the basis of green vegetation [5]. As collected fAPAR values represented the total light conditions of the forest stand, an NDVI-fAPAR relationship (Figure 9) was established to ensure that fAPAR was, in fact, a good proxy of vegetation greenness changes. Daily NDVI and fAPAR values strongly correlated (\(R^{2}\) = 0.96, \(p\) < 0.05), but the relationship was non-linear. NDVI was seen to saturate as values reached above approximately 0.65 (_f_APAR \(\approx\) 0.75). The effect of canopy structure and pigment pool changes on the PRI signal was evaluated by comparing SRS-sPRI values with measured fAPAR. Analysis of the seasonal relationship (Figure 10a) showed fAPAR and SRS-sPRI as being poorly correlated (\(R^{2}\) = 0.14, \(p\) > 0.05). The seasonal time series was further divided into maturity and leaf senescence, and correlations were derived (Figure 10a). During maturity, we observed a poor relationship between fAPAR and SRS-sPRI values, suggesting a minimal effect of canopy structure on PRI signals. However, during leaf senescence, a linear relationship (\(R^{2}\) = 0.52, \(p\) < 0.05) formed between fAPAR and SRS-sPRI values. To confirm the effect of canopy structure on efficiency during maturity and leaf senescence, the same comparisons were performed using LUE values, and lead to comparable results (Figure 10b).