## 4 Discussion
The stomata of senescent leaves lose responsiveness to environmental variables and cannot be closed efficiently, even under low-light conditions (dull leaf phenomenon [43; 49; 50]), maintaining leaf \(g_{\text{s}}\) and thus SFD\({}_{\text{day}}\) at high values during the autumn leaf senescence period. In fact, nighttime SFD tended to increase from summer to autumn for more than half of the sampled trees (Figure A4, [37]), supporting this point of view. Therefore, the lack of stomatal control by leaf senescence may explain why CLC\({}_{\text{ground}}\) showed better performance for predicting SFD\({}_{\text{day}}\) than the UAV metrics. This implies that camera-based modelling of SFD\({}_{\text{day}}\) during the summer to autumn period should be based on phenological metrics reflecting changes in leaf amount rather than leaf physiological characteristics for deciduous tree species in cool temperate climates. This finding contrasts with canopy photosynthesis in temperate deciduous forests; that is, autumn decline occurred several weeks earlier for photosynthesis than for LAI, e.g., [9; 13], probably because leaf photosynthesis starts to decrease with or before leaf coloring [25]. Although plant transpiration is inevitably linked to photosynthesis through the stomatal control of CO\({}_{2}\) absorption, our study suggests that phenological metrics different from photosynthesis should be used to model transpiration phenology. Although the ability to predict sap flow phenology was slightly lower for Hue than for CLC\({}_{\text{ground}}\), Hue showed a seasonal trajectory similar to that of CLC\({}_{\text{ground}}\) compared to the other UAV-derived metrics (Figures 3 and A5). This result is consistent with previous findings that Hue can be used as a proxy for seasonal variations in leaf amount in deciduous broad-leaved species [12; 24]. Additionally, [PERSON] et al. [24] showed that Hue values were relatively stable for camera models, camera settings, and sky conditions compared with the other indices. Considering the labor intensity of ground observations, Hue would be a suitable factor for modelling tree transpiration on a large scale. However, the seasonal trajectory of Hue is affected by species-specific differences in leaf color. For example, for species showing bright leaf coloring in autumn, such as _Acer sieboldianum_ Miq., the start date of the decreasing phase (SOD) was 20-30 days earlier for Hue than for CLC\({}_{\text{ground}}\) (Figure A5), leading to an underestimation of autumn SFD\({}_{\text{day}}\). In contrast, for species showing unclear leaf coloring (i.e., with a certain amount of chlorophyll remaining in senescent leaves), such as _Peterostyrax hispidus_ Siebold & Zucc., SOD was markedly later for Hue than CLC\({}_{\text{ground}}\), causing an overestimation of SFD\({}_{\text{day}}\). The timing and extent of leaf coloring vary not only with species but also with the local growth environments of individual trees and inter-annual variations in climatic conditions [8; 51; 52]. Therefore, the ability of Hue to predict sap flow phenology can also vary with these factors, which should be considered when Hue is used for modelling transpiration phenology. Previous research based on tower-mounted phenocams has reported that normalized difference vegetation index (NDVI) is more sensitive to changes in canopy LAI than GCC in deciduous broad-leaved forests [53; 54]. This is because NDVI incorporates bands at near-infrared (NIR) wavelengths, where the reflectance of leaves is governed by structural characteristics rather than pigmentation. Therefore, phenological metrics using an NIR band may perform better than Hue. However, because NDVI uses a red color band for the calculation, its seasonal trajectory can also be affected by spatiotemporal variations in leaf color [55; 56]. For example, NDVI showed an obvious decrease when leaves turned from green to yellow during early autumn in a Japanese deciduous forest [57].