## 1 Introduction  
Leaf phenology is the study of annually recurring biological events in plant leaves, such as budburst, development, and senescence, which are tightly linked to plant productivity and water use, especially in deciduous tree species. Leaf phenology is highly sensitive to changes in climatic conditions, particularly air temperature [1, 2]. For example, field and remote sensing evidence, predominantly from northern hemisphere regions, shows that a warming climate has advanced the timing of leaf-out and prolonged the growing season in the last half-century [3, 4], which may alter the seasonal cycle of ecosystem COfluxes and energy balance [5]. However, when predicting such impacts on biogeochemical cycling with dynamic vegetation and ecosystem process models, leaf phenology is one of the most difficult processes for parameterizing such models because the mechanism behind leaf phenology and its response to environmental drivers are complex [6; 7; 8]. In this context, simultaneous observation of leaf phenology and physiological functions in forest ecosystems provides important implications for improving the parameterization and structure of such process-based models and, thus, our understanding of how plant communities and terrestrial ecosystems respond to climate change [9]. Leaf phenology has traditionally been evaluated using ground-based visual observations. However, this method is labor intensive, prone to human error, and difficult to apply to trees in dense and multi-layered forests wherein an observer cannot see the entire canopy. Consequently, remote sensing approaches using satellite and tower-mounted digital cameras have become popular for monitoring leaf phenology in forest ecosystems because they enable continuous monitoring of leaf phenology due to fixed viewing geometry and allow objective evaluation of leaf phenology using the reflectance data of acquired images. Especially, a tower-mounted digital camera called a \"phenocam\", which is categorized as a near-surface remote sensing approach, can provide ecosystem-level leaf phenology at a higher temporal frequency and finer spatial resolution than satellite remote sensing without being obscured by clouds, enabling detailed comparison of leaf phenology with ecosystem carbon and water flux obtained by the eddy covariance method [10; 11]. Because canopy color derived from phenocam imagery depends on both the leaf amount and the color of individual leaves, it reflects both physiological and structural characteristics of the canopy, such as leaf area index (LAI), leaf chlorophyll and carotenoid concentrations [12; 13; 14; 15; 16; 17], which are key components of canopy photosynthesis and transpiration. Therefore, phenocams have been installed in many flux sites in various ecosystems, such as temperate and boreal forests, savannas, and grasslands, and time-series data of canopy greenness or redness have been used to analyze seasonal changes in ecosystem gas flux, especially for CO\({}_{2}\), e.g., [9; 10; 18; 19; 20; 21]. Although several studies have reported tight correlations between phenocam-derived color metrics and gross primary production (GPP) across seasons in temperate deciduous forest ecosystems, e.g., [22; 23; 24], many others found large discrepancies between GPP and phenological metrics, especially for the pattern within the growing season; that is, phenocam-derived metrics tend to result in both earlier spring peak and later onset of autumn decrease compared to canopy photosynthesis [9; 13; 14; 18]. Although mid-growing season phenology has received less attention than phenology at the beginning and end of the growing season, it determines the attainment of full photosynthetic capacity and its period, thus affecting the annual sums of GPP.