## 4 Discussion
Previous studies using finer resolution data suggest that forest GPP is more tightly coupled to canopy temperatures than air temperatures, but at the coarse resolution of this study, T\({}_{\text{can}}\) and T\({}_{\text{air}}\) exert a similar influence on SIF metrics and VIs (Figure 8). In contrast to the other environmental factors, LST from MODIS did not generally have as great an influence on productivity metrics, but the trends were similar for the three types of temperature measurements (Figures 6-8). MODIS LST has been widely used and validated using ground data, models, and cross-comparison to other satellites [113; 114; 115; 116]. LST was shown here to perform similarly to the other temperature metrics for explaining VIs in DBNF for the study period but not for SIF metrics. Seasonality analysis quantifies similarities and differences in productivity measured with these remote sensing metrics. SIF metrics had the shortest season of productivity, about one week shorter than GPP, two weeks shorter than EVI, and over two weeks shorter than NDVI, which corresponds to the relative base and pulse-integrated productivity (Figure 9). In the case of SIF metrics and GPP, the EOS DOY was almost identical; therefore, the difference in relative integrated productivity was attributed to an earlier GPP SOS. For SIF and EVI, the relative integrated productivity difference was due to both an earlier EVI SOS (just under one week) and a later EVI EOS (just under one week). Thus, differences in production measured with SIF metrics, GPP, EVI, and NDVI are not explained by simple differences in LOS for each quantity; rather, these satellite-based proxies are complex representations of the physiological and physical responses of vegetation. The SOS and EOS shifts, together with the integrated productivity results, suggest that SIF metrics and GPP versus EVI versus NDVI capture varying degrees of phenological and structural changes at different timings (Figure 9). The results show that SIF\({}_{\text{i}}\), SIF\({}_{\text{d}}\), GPP, and, to some lesser extent, EVI, respond to the same or coincident temperate forest vegetation production dynamics, such as canopy structural changes, light capture and scattering, and photosynthetic responses to the environment. Canopy level field measurements of the Natural Bridge area within DBNF during a period of NDVI senescence also indicate that EVI and NDVI capture different trends and variability and agree well with satellite data, but they were too few and spatially limited to directly validate the satellite scale reflectance measurements. Furthermore, as longer records of SIF, such as from OCO-2, OCO-3, and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5P mission, become available, GPP, SIF, EVI, and other satellite-derived datasets, as well as more refined estimates of SIF\({}_{\text{y}}\), can be used in tandem to improve our understanding of production estimates, as model inputs, and to spatially and temporally gap-fill data, as well as create new datasets to track vegetation dynamics with satellite remote sensing observations. SIF metrics, GPP, and EVI to a more moderate degree, indicate a more complex representation of drivers and responses than NDVI. The differences in relative integrated productivity derived from SIF versus NDVI can be attributed to NDVI SOS, which was just under a week earlier than SIF SOS, but NDVI EOS was approximately one month later than SIF EOS (26 and 32 days; Figure 9). NDVI showed the least agreement with all the other indicators of relative base or pulse-integrated production and LOS, indicating changes into senescence much later in the year. When relying on correlations, for instance,clustering of NDVI values, for instance, by season could indicate strong relationships with GPP and other productivity metrics. It is because of this strong correlation that NDVI is heavily relied upon in studies of vegetation productivity and seasonality, e.g., [47; 62].