## 4 Discussion
Although superior characteristics of NDVI compared with metrics calculated from RGB images have been reported at the ecosystem level, no study has compared the seasonality of LAI, NDVI, and Hue at individual tree level. As UAVs equipped with multispectral sensors are commercially available, this issue should be tested in future research. During spring, although the start date of the rising phase (SOR) derived from the UAV metrics showed good agreement with that derived from SFD\({}_{\text{opt}}\), the end date (EOR, i.e., spring peak) derived from the UAV metrics preceded that derived from SFD\({}_{\text{opt}}\) by several weeks to two months for more than half of the individuals (Figure 6). Consequently, consistent overestimation of SFD\({}_{\text{day}}\) was found during the rising phase, irrespective of the type of UAV metric (Figure 14). Although studies comparing sap flow and leaf phenology are generally limited, similar discrepancies in the spring peak have been reported in the stand-level comparison for _Fagus sylvatica_ L. [58] and in the branch-level comparison for _Rhododendn occidentale_ ([PERSON]. [PERSON]. [PERSON]) [PERSON] [59]. Furthermore, large discrepancies in spring peaks have been frequently reported in comparisons between canopy photosynthesis and phenocam-derived phenological metrics at the ecosystem level, e.g., [9; 10; 13]. For example, [PERSON] et al. [13] showed that the spring peak of canopy GCC derived from phenocam preceded the peak of gross ecosystem production (GEP) by two weeks in temperate deciduous forests. Since they also found similar discrepancies against canopy LAI and leaf chlorophyll concentration, they concluded that the discrepancy in the spring peak between GEP and GCC could be attributed to the saturation of green signals at low LAI (approximately 2 to 2.5 m\({}^{2}\) m\({}^{-2}\)). However, in this study, not only the UAV-derived metrics but also \(\mathrm{CLC}_{\mathrm{ground}}\), which closely correlated with the effective plant area index derived from hemispherical photography (Figure 15), showed a large discrepancy in the spring peak against \(\mathrm{SFD}_{\mathrm{opt}}\), implying the involvement of factors other than the saturation of the green signal. [PERSON] and [PERSON] [25] showed that the annual peak of leaf photosynthetic capacity and \(g_{\mathrm{s}}\) occurred approximately two months after the peak of leaf chlorophyll concentration and crown leaf area for deciduous broad-leaved tree species in Japanese cool temperate forests. Therefore, late maturation of leaf physiological properties may explain the observed discrepancy in the spring peak between \(\mathrm{SFD}_{\mathrm{opt}}\) and the UAV metrics, as anticipated by [PERSON] et al. [18]. Because the spatial scale between sap flow and leaf phenological metrics was identical at the tree level in this study, we expected a much closer flux-leaf phenology relationship than the relationships obtained in the ecosystem-level comparison, which involves a mismatch of footprint size between flux and optical measurements [26]. However, contrary to our expectation, a large discrepancy in the spring peak was still observed, indicating that strict matching of footprint size is not always important for evaluating flux-leaf phenology relationships. Instead, disentangling the interaction between leaf physiological properties, crown structure, and reflectance is more important for predicting carbon and water fluxes using a remote sensing approach. Although the spring rising phase is relatively short compared to the peak and decreasing phases during summer and autumn (Figure 1), the transition dates and duration of the rising phase are sensitive to climate change, e.g., [60], which has a significant influence on inter-annual variations in carbon and water flux [5].