## 3 Results
### Sap Flow Phenology and Comparison with Leaf Phenology  
The daytime total sap flux density (SFD\({}_{\text{day}}\)) also showed clear seasonality, with high values during summer and low values in early spring and late autumn (Figures 4A,B and A3). However, large day-to-day variations governed by radiation (\(R_{\text{s}}\)) and vapor pressure deficit (VPD) were found throughout the seasons, making comparisons between sap flow and leaf phenology difficult (Figure 4C-F). Thus, we estimated SFD\({}_{\text{day}}\) from the phenological metrics using the Bayesian model (Equation (6)), and compared the estimated values with the measured values (Figures 5 and A6). Although significant correlations between the measured and estimated SFD\({}_{\text{day}}\) were found irrespective of the type of phenological metric, predicting ability largely differed among the metrics as follows: CLC\({}_{\text{ground}}\) > Hue > GRVI > GCC \(\approx\) GEI. For example, the SFD\({}_{\text{day}}\) estimated by CLC\({}_{\text{ground}}\) had the best correlation with the measured data in 20 of the 33 individuals. With respect to Hue, the best correlations were found in 10 of the 33 individuals. In contrast, SFD\({}_{\text{day}}\) estimated by GCC or GEI showed the worst correlations for most individuals, with the exception of the _Chengiopanaxxicadophylloides_ ([PERSON] & Sav). C.B.Shang & J.Y.Huan tree measured in 2020. Figure 3: Comparison of leaf phenological transition dates derived from UAV metrics and those derived from crown leaf cover based on ground observations (CLC\({}_{\text{ground}}\)). Each point represents data for an individual tree. Dotted lines are 1:1 lines, and solid lines are linear regression equations (\(p\) < 0.05) as follows: Panel (**A**) GCC: \(y\) = 0.69\(x\) + 38.75, \(P^{2}\) = 0.53; GEI: \(y\) = 0.73\(x\) + 32.16, \(P^{2}\) = 0.51; GRVI: \(y\) = 0.51\(x\) + 62.51, \(r^{2}\) = 0.37; Hue: \(y\) = 0.73\(x\) + 30.89, \(r^{2}\) = 0.37; all \(p\) < 0.001. Panel (**B**) GCC: \(y\) = 0.62\(x\) + 49.84, \(r^{2}\) = 0.62; GEI: \(y\) = 0.69\(x\) + 39.26, \(r^{2}\) = 0.69; GRVI: \(y\) = 0.50\(x\) + 66.11, \(r^{2}\) = 0.59; Hue: \(y\) = 0.64\(x\) + 44.86, \(r^{2}\) = 0.56; all \(p\) < 0.001. Panel (**C**) GCC: \(y\) = 0.45\(x\) + 76.80, \(r^{2}\) = 0.54, \(p\) < 0.001; GEI: \(y\) = 0.40\(x\) + 83.07, \(r^{2}\) = 0.54, \(p\) < 0.001; GRVI: \(y\) = 0.26\(x\) + 105.25, \(r^{2}\) = 0.24, \(p\) < 0.01. Panel (**D**) Hue: \(y\) = 0.34\(x\) + 180.98, \(r^{2}\) = 0.19, \(p\) < 0.05. Panel (**E**) GRVI: \(y\) = 0.57\(x\) + 114.37, \(r^{2}\) = 0.52; Hue: \(y\) = 0.50\(x\) + 145.04, \(r^{2}\) = 0.53; all \(p\) < 0.001. Panel (**F**) GCC: \(y\) = 0.67\(x\) + 100.22, \(r^{2}\) = 0.56, \(p\) < 0.001; GEI: \(y\) = 0.52\(x\) + 144.68, \(r^{2}\) = 0.35, \(p\) < 0.001; GRVI: \(y\) = 0.57\(x\) + 127.77, \(r^{2}\) = 0.47, \(p\) < 0.001; Hue: \(y\) = 0.39\(x\) + 185.80, \(r^{2}\) = 0.26, \(p\) < 0.01. Abbreviations: GCC, green chromatic coordinate; GEI, green excess index; GRVI, green-red vegetation index. Figure 4: Seasonal trajectories of daytime total sap flux density (SFD\({}_{\text{day}}\)) and optimized sap flux density (SFD\({}_{\text{opt}}\)) derived from Equations (6) and (7) (**A,B**) with daily total solar radiation (\(R_{s\_day}\)) and daytime mean vapor pressure deficit (VPD\({}_{\text{day}}\)) (**C,D**) and SFD\({}_{\text{day}}\) response to the environmental drivers (**E,F**) for _Bettula grossa_ Siebold & Zucc. tree. Values of SFD\({}_{\text{day}}\) and SFD\({}_{\text{opt}}\) are normalized to the scale from 0 to 1. The data of SFD\({}_{\text{day}}\) and SFD\({}_{\text{opt}}\) for other individuals is shown in Figure A3. Abbreviation: DOY, day of the year. Figure 5: _Cont._  
For a more detailed comparison of phenology between leaf and sap flow, we reproduced the optimized SFD\({}_{\text{day}}\) (SFD\({}_{\text{opt}}\)) using Equations (6) and (7) and compared the phenological transition dates of SFD\({}_{\text{opt}}\) and leaf phenological metrics (Figure 6).