## 3 PhenoCam as a Promising Technology for Long-Term and Continuous Phenological Monitoring  
The PhenoCam system tracks vegetation phenology at a high spatial and temporal resolution using images from digital cameras mounted on a tall tower [32] or small towers situated at a vantage point. This innovative technique allows for the monitoring of vegetation phenology at both individual and local area levels with minimal atmospheric errors. Researchers can analyze the captured images to identify data patterns or anomalies, facilitating a deeper understanding of the vegetation dynamics [33] (Figure 2). The PhenoCam system captures images in red, green, and blue (RGB) and, optionally, in infrared (IR) bands. Since these images are captured under open atmospheric conditions, fluctuations in brightness due to cloud cover and the time of day can influence the quality of data. However, these effects can be mitigated by calibrating the images using a reference panel kept in the field of view [34]. Data generated from PhenoCam imagery have been utilized for various purposes, such as to assess satellite phenological products [35; 36; 37]; to define and validate new phenological models [38]; to better understand the interactions between canopy phenology and the dynamics of ecosystems [39]; and to study the seasonal changes in leaf-level physiological processes that are connected to variations in leaf color [40; 41]. In this review, we systematically compared observations from PhenoCCam to satellite-based phenological products, the eddy covariance tower, and the ground observation of different ecosystems for which data are available in the literature. The comparison between PhenoCCam and various other methods in terms of their performance, as reported in the existing literature, is depicted in Figure 3. The figure presents the correlation coefficients (\(r\) values) collected from relevant studies, providing a visual representation of the comparative performance of these methods for phenological monitoring. All the literature on global PhenoCCam work referenced in this review is included in Table S1. First, we compared PhenoCCam-derived time series green chromatic coordinates (GCC) with MODIS and VIIRS satellite time series data (NDVI) (low-resolution satellites) (Figure 3a). In deciduous forests, the average correlation was found to be 0.87, while in grasslands, it was 0.85 (n = 12). The evergreen forest showed the lowest correlation with a value of 0.7 (n = 7). We also examined the correlation with moderate-resolution satellites such as Landsat and Sentinel. In grasslands, PhenoCCam data demonstrated an average correlation of 0.91 (n = 9), while in deciduous forests, it was approximately 0.87 (n = 4). Furthermore (Figure 3b), we evaluated the correlation between extracting SOS and EOS from MODIS data and PhenoCCam observations (n = 12). In the deciduous forest, the average correlation for SOS was 0.89, and for EOS, it was 0.81. In grasslands, the correlation between MODIS and PhenoCCam for SOS was 0.94, while for EOS, it was 0.70. These studies show that Deciduous forests and Grasslands exhibit a stronger correlation with satellite data, primarily because they vividly display phenological stages. By contrast, Evergreen forests show a relatively lower correlation, which is mainly attributed to the absence of distinct phenological stages and the limitations in temporal resolution provided by satellite data. Next, we compared PhenoCCam time series GCC with gross primary productivity (GPP)-based phenological monitoring using the eddy covariance tower across different ecosystems (Figure 3c). Among the ecosystems studied, grasslands exhibited the highest correlation, with an average R-value of 0.91 (n = 8), followed by deciduous forests with an average R-value of 0.87 (n = 11), and evergreen forests with an R-value of 0.84 (n = 5). Lastly, we compared ground observation data with PhenoCCam-derived GCC for various phenological metrics.