## 4 Discussion
However, seasonality analyses add to the growing body of research showing that SIF captures more information regarding the function of vegetation, while NDVI indicates changes in structure [18; 33; 34; 100; 104]. Furthermore, EVI and GPP from MODIS demonstrated more similar capturing seasonality to SIF than NDVI (e.g., [17; 18; 75; 76; 77]). Also, NDVI correlation behavior might disappear in vegetation with less variation in seasonal temperature (tropics) or vegetation with fewer structural seasonal changes (evergreen), which supports findings in previous studies, e.g., [43; 62; 89; 99; 106]. Taken together with the evidence from other studies (e.g., [17; 18; 27]), NDVI likely responds to the most pronounced changes in the structure of the forest canopy combined with increasing soil and background. While NDVI is an often-used metric for seasonality analysis, it was found to provide the least information beyond the most pronounced structure changes [17; 18; 22; 23]. In contrast, EVI captured deciduous vegetation changes missed by NDVI through the exploitation of the NIR and additional blue spectral region. The proportion of production for the base and growing seasons indicated by EVI was more similar to GPP than NDVI, although it was not as similar as SIF to GPP. EVI also indicated a relatively larger pulse-integrated production than NDVI, even though the LOS for NDVI was approximately 18 days longer. EVI achieved this likely because of reduced atmospheric and soil background effects and less saturation than NDVI [7; 48; 89; 89]. These results indicate that seasonality analysis using EVI has potential applications in tracking vegetation phenology beyond that of NDVI and may be more comparable to SIF and GPP. Taken together with the widespread availability and long record of satellite EVI, EVI remains a valuable and relevant source of phenological information. The agreement between \(\text{SIF}_{i}\)\(\text{SIF}_{d}\), and GPP may stem from the greater sensitivity of these remote sensing proxies to changing environmental factors than NDVI or EVI. The GPP product from MODIS incorporates information regarding environmental conditions via a model, and SIF has proven to be sensitive to light and environmental conditions (e.g., [3; 8; 10; 12; 14]). For instance, NDVI clusters for cool versus warm months to a greater degree than GPP or SIF metrics and saturates in the growing season (Figures 6 and 7). As mentioned previously, NDVI correlations that exist with SIF indices are likely driven by seasonal clustering rather than tracking incremental changes. SIF metrics and GPP are more evenly distributed across the months in response to changes in \(\text{T}_{\text{can}}\) and \(\text{T}_{\text{air}}\), as well as VPD, which illustrates that \(\text{SIF}_{i}\), \(\text{SIF}_{d}\), \(\text{SIF}_{y}\), GPP, and even EVI capture more subtle responses of the forest to environmental factors than NDVI. This can be especially applicable in phenological studies or monitoring ecosystem responses related to changing climates [4; 7; 117; 118]. A method to estimate \(\text{SIF}_{y}\) using OCO-2 and MODIS fPAR was tested. A technique to reliably calculate \(\text{SIF}_{y}\) using satellite data would open the possibility of greater photosynthetic information available at the greatest spatial scales. However, the \(\text{SIF}_{y}\) time series resulted in unreliable seasonality calculations; thus, no phenological insights or improved interpretation of SIF patterns were found. The \(\text{SIF}_{y}\) result indicates that the SIF signal is composed largely of fPAR and the remaining information is noisy (Figure 3e). The calculation did, however, remove the influence of fPAR as indicated by the relative importance of variable analysis (Figure 8), but it is suspected the remaining data are not helpful for the interpretation of SIF and its relationship to carbon uptake.