## 2 Materials and Methods
### Observation of Leaf Phenology  
Leaf phenology of the sampled tree crown was monitored using ground-based visual observations and UAV observations conducted on the same day from late April to mid-November at a weekly to biweekly frequency, depending on weather conditions. Measurements were taken on 21 and 24 distinct occasions in 2019 and 2020, respectively (Table A1). With respect to ground-based visual observation, the observer looked at the target crowns from various directions using binoculars and estimated the percentage of unfolded leaves and the mean leaf size as the percentage of mature leaf size. Photographs of the target crown were also taken on each occasion to check the accuracy of the mean leaf size derived from visual observations. Crown leaf cover (CLCground) was then calculated as a product of the percentages of unfolded leaves and leaf size, and the result was categorized into one of the seven classes as follows: 0%, 10% (1-19%), 30% (20-39%), 50% (40-59%), 70% (60-79%), 90% (80-99%), and 100% (Figure S3 in the supplementary materials of [28]). Ground observations were performed by the same person throughout the seasons to minimize any errors associated with different observers. Because changes in leaf color were not considered in the evaluation of \(\text{CLC}_{\text{ground}}\), \(\text{CLC}_{\text{ground}}\) represents changes in the relative amount of leaf area. A preliminary experiment showed that \(\text{CLC}_{\text{ground}}\) was closely correlated with the effective plant area index derived from hemispherical photographs (Figure A2). Further details of ground-based visual observation are described in the paper by [PERSON] et al. [28]. For UAV observations, aerial photographs were taken using a commercial drone (DJI Phantom 4 pro, DJI-Innovations Inc., Shenzhen, China) equipped with an RGB camera (DJI FC6310) with an 8.8 mm nominal focal length and a 1\" CMOS 20-megapixel sensor with \(2.41\times 2.41\) mm nominal pixel size. Prior to each UAV flight, the camera was configured for continuous shooting with the following settings: infinity focus, ISO 800, and exposure time of 1/1000 s. The photographs were stored in JPEG format with super-fine compression. Although a clear day was chosen to conduct UAV flights based on the weather forecast, weather conditions at the study site were generally unstable due to rapidly changing regional weather conditions (i.e., fog and cloud). Therefore, the UAV was manually operated to change flight plan flexibly according to the weather conditions. To ensure sufficient overlap of images to produce an orthophoto (80% longitudinal and 50% lateral overlap [33]), the UAV was operated at roughly 1.2 m s\({}^{-1}\), with the camera taking images every 2 s, and at least five flights were conducted over the 1.5 ha plot (25 min per flight). The flight height was 35-45 m above the ground (i.e., 15-20 m above the crown surface of sample trees). To minimize the effect of variable illumination on the UAV image due to the solar incidence angle, the flights were conducted at midday (between 10:00 and 14:00) on most occasions, whereas variable illumination conditions during flight inevitably occurred on some occasions due to the unstable weather conditions at the study site (Table A1). Each acquired image set was processed into a georeferenced orthophoto using the Context Capture software (Bentley Systems Inc., Exton, PA, USA). Although the entire image processing (i.e., bundle adjustment and reconstruction of three-dimensional model) was performed nearly automatically by the software [33], seven ground control points in the plot, determined using a global navigation satellite system with a precision of less than several centimeters (Figure A1), were included in the process to refine georeferencing. Additionally, to mitigate the negative effect of unsteady illumination conditions on orthophoto production, a color equalization algorithm was used in the software, which ensures consistent appearance of the acquired images even under largely different illumination conditions.