## 1 Introduction
Many possible explanations for the dissimilarity have been inferred, for example, earlier saturation of green signal at relatively low LAI [13], slower development of leaf physiological properties than leaf area [18], spring increase in leaf carotenoid content and subsequent decrease in the fraction of green signal from the canopy, [14] and earlier decrease in leaf physiological properties than the onset of leaf fall, e.g., [25]. However, it is difficult to clarify the cause of these discrepancies from ecosystem-level studies because the observed patterns and relationships between flux and leaf phenology integrate the responses of multiple species, age classes, and canopy layers within the ecosystem; thus, biological interpretation of these relationships is far from trivial. Additionally, phenocam tends to overrepresent dominant trees located closest to the field of view, which causes a footprint mismatch between the flux and optical measurements, especially in species-rich forests with complex structures, leading to errors in the flux-leaf phenology relationship [26]. Consequently, it is difficult to apply the flux-leaf phenology relationship obtained at the ecosystem level to other sites, even in the same biome. In this context, identifying the flux-leaf phenology relationship at the individual tree level is an important step for disentangling the cause of dissimilarity in the ecosystem-level relationship. Drones, also referred to as unmanned aerial vehicles (UAVs), are other near-surface remote sensing tools that have been increasingly used to monitor leaf phenology [27; 28]. Although the temporal resolution of the acquired images is lower than that of phenocams, UAV can produce aerial images at the same spatial resolution as phenocams and cover vast areas, ranging from hectares to multiple square kilometers. This enables the identification and analysis of an extensive number of individual crowns across seasons and years, with the same spatial standard. With respect to flux measurement at the tree level, there is no suitable approach for linking CO\({}_{2}\) flux measurement to UAV-derived phenology because tree-level CO\({}_{2}\) flux measurement requires enclosing the entire crown with a gas exchange chamber, impeding UAV observation [29]. In contrast, stem sap flow measurements using heat dissipation probes can provide a time series of tree-level transpiration data on an hourly to daily scale without enclosing the crown with the chamber, and thus can be directly linked to UAV observation. Additionally, the sap flow sensor is simple to construct, easy to install, and inexpensive compared with other approaches, such as the whole tree chamber, enabling us to measure multiple trees for examining inter- and intra-species variations in flux-leaf phenology relationships. Furthermore, although transpiration is an important factor influencing latent heat flux and photosynthesis, previous studies have primarily focused on the relationship between leaf phenology and canopy photosynthesis but not transpiration, probably because the H\({}_{2}\)O flux obtained by the eddy covariance method encompasses processes other than transpiration, such as evaporation of soil water, canopy intercepted water, and other free water surfaces, which do not directly correlate with leaf phenology. Although fundamental factors controlling tree transpiration (i.e., amount of leaf area and leaf physiological properties) are similar to those controlling photosynthesis, leaf water-use efficiency (i.e., the ratio of photosynthesis to transpiration) is not constant across seasons [30; 31]. Thus, we expected that leaf phenological metrics related to transpiration would differ from those related to photosynthesis. Furthermore, because the spatial scales of stem sap flow and UAV-derived crown leaf phenology are identical at the tree level, we can expect closer correlations between sap flow and UAV-derived phenology than the flux-leaf phenology relationships observed at the ecosystem level.