## 4 Discussion  
In this study, a seasonality analysis revealed differences in how the phenology of temperate forest canopy production can be represented by different remote sensing proxies. Studies have shown that EVI, GPP, and especially SIF are more indicative of physiological responses of vegetation to the environment than NDVI [99; 100], but relatively few studies have examined the seasonality of EVI, GPP, or SIF compared to NDVI (e.g., [5; 101; 102; 51; 63]). Here, seasonality measurements were based on SIFi, and SIFd allocated production to the base and growing season pulse in similar proportions to GPP. SIFi and SIFd signals are known to incorporate both physiological (photosynthetic rate) and physical information (light absorption and SIF scattering) about the vegetation canopy (e.g., [103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115]). GPP from MODIS is fundamentally a different satellite remote sensing measurement whose model incorporates LAI, fPAR, VIs, and information on vegetation type [106; 107; 51; 108]. Despite the fundamental differences in measurements of SIF from OCO-2 and GPP from MODIS, a strong agreement in production and timing between them is evident. This agreement may stem from a correlation between the radiation information contained within the SIF signal and the inclusion of fPAR in the MODIS GPP algorithm. Additionally, GPP from MODIS could be used in tandem with OCO-2 SIF for spatial and temporal gap filling, for further seasonality and phenology studies, as tools for monitoring vegetation changes in a changing climate and developing new productivity datasets using OCO-2 SIF and MODIS GPP products similar to those presented by [PERSON] et al. [109]. In this study, different satellite-based productivity metrics, intermediate variables, and environmental factors were investigated to determine how they captured the productivity and phenology of a DBF on a regional scale using a range of analyses, from more reliant on visual inspection to more quantitative. The inspection of time series and intra-annual cycles of SIF metrics (SIFi, SIFd, and SIFy), VIs (GPP, NDVI, and EVI), intermediate variables (fPAR and LAI), and environmental variables (LST, Tcan, Tair, and VPD) displayed expected  
Figure 9: Results of seasonality analysis for SIFi, SIFd, SIFy, NDVI, GPP, and EVI; (**a**) beginning and end of season day of year (DOY); (**b**) normalized values of seasonal base (green) and pulse (orange) production. t trends of changes corresponding to the growing and non-growing seasons, except SIFy (Figures 3 and 4). NDVI maintained similar values throughout the growing seasons, while other productivity metrics showed more variability, especially during spring and fall (Figure 3a). This is consistent with previous studies, where NDVI has been shown to saturate in the growing season (e.g., [69]). Visual inspection of normalized annual cycles also revealed NDVI differences from SIF metrics and EVI, especially higher values in the late summer and early fall months. Normalized annual cycles were also the first step to examine the potential influence of environmental variables and the timing of seasonal changes. For instance, the environmental variables peaked in August, while the productivity metrics peaked in earlier months, which could be due to a high temperature threshold after which productivity tends to decrease (e.g., [50], Figure 4a-c). An analysis of the time series and normalized annual cycles revealed no anomalies, despite a known drought occurring in 2016. This result indicates that the resolution, either spatial, temporal, or both, was insufficient for detecting drought-induced changes for this event for a DBF using any of the metrics. As suggested by previous studies, SIF metrics have increased sensitivity for monitoring stress over VIs, but they currently vary in the degree to which events are detectable due to spatial or temporal scales and regions e.g., [24; 35; 100; 101]. Using the methods in this study, OCO-2 SIF did not benchmark the event at this juncture.