## 3 PhenoCam as a Promising Technology for Long-Term and Continuous Phenological Monitoring
In the deciduous forest, the correlation for budburst was found to be 0.96 (n = 4); for the leaf area index (LAI), it was 0.93 (n = 2); for the chlorophyll concentration, it was 0.95; and for leaf fall, it was 0.96 (n = 2) (Figure 3d). Figure 2: A representation of different components of the PhenoCCam system. Traditionally, the observation of flowering and fruiting phenophases relied on labor-intensive direct ground observations. However, the introduction of the PhenoCam network has revolutionized this process, allowing for the more detailed monitoring of these phenophases. For instance, [PERSON] et al. (2022) utilized the PhenoCam network to monitor different stages of flowering (budding, initiation, and maturation) and fruiting (fruit development and maturation) phenology in _Rhododendorh arboremum_ Sm., located in the Himalayas. Their findings reveal a strong correlation between these phenophases and winter soil temperature [42]. Additionally, [PERSON] et al. (2022) developed an automated method using time-lapse cameras to accurately extract flowering phenology (onset, peak, and end of flowering) for two Arctic species, achieving a high accuracy rate of over 90 percent compared to ground observation [43]. Also, [PERSON] et al. (2020) reported a strong correlation (r = 0.98) between the within-population variability of budburst (WPVbb) calculated using PhenoCam data and ground observations. This supports the reliability of PhenoCam-derived metrics in capturing fine-scale phenological variations [44]. In addition to this, [PERSON] et al. (2010) employed linear regression analysis to demonstrate that greenness (GCC) is closely associated with soil moisture at depths ranging from 0.30 m to 1 m, which corresponds to the densest distribution of creosote bush roots [45]. This finding reinforces the link between PhenoCam-derived observations and ecological factors. Moreover, in the context of evergreen forests, where the correlation between PhenoCam-based observations compared to satellite phenological products tends to be low, [PERSON] et al. (2020) discovered that red chromatic coordinates (RCC) offer a more precise prediction of GPP-based SOS and EOS [46]. This indicates the potential for an improved phenological assessment in evergreen forest ecosystems through the use of RCC data. These  
Figure 3: Correlation between PhenoCam data with other phenological monitoring methods, (**a**) PhenoCam to satellite-based NDVI, (**b**) PhenoCam-derived SOS and EOS to MODIS SOS and EOS, (**c**) PhenoCam GCC to eddy covariance gross primary productivity data and (**d**) PhenoCam to ground observations. studies highlight the potential of PhenoCam data, in combination with satellite data, to accurately monitor phenological changes and bridge the gap between satellite observations and real-world ecological dynamics. However, RCC, GCC, and NDVI may not reveal the same timings of SOS or EOS as the physical processes associated with these indices are different. RCC and GCC reveal normalized energy from the red and green band from the PhenoCam RGB images, and they reveal that chlorophyll absorbed and reflected energy, hence revealing more about the chlorophyll pigment content in plants. On the other hand, NDVI uses red and NIR bands, and hence, it reveals more about the biomass as well as the health of the plant. During the senescence, the NIR value depends more on fiber content (intercellular arrangements of cells and pigment concentration, respectively), and the red reflection increases due to the absence of chlorophyll absorption and, hence, the magnitude of the NDVI value becomes low as the difference between NIR and RED is diminished [41; 46]. Due to these reasons, RCC and GCC may show an early SOS, but NDVI may show a delayed SOS. Additionally, NDVI and other satellite-derived indices are influenced more by the soil background, atmospheric conditions, topography, etc., while GCC and RCC are relatively free (or negligible) from these influences.