## 4 Discussion
In our upcoming work, however, revised methods or more recent satellite platforms and products, which are increasing exponentially, promise to benchmark such events in DBF. Correlations provided basic quantification of the relationships among the SIF and VIs, which were generally strong, positive relationships, except for SIFy (Table 2). These results are in alignment with earlier studies that have found strong relationships between SIF and satellite GPP, NDVI, and EVI using time series, annual cycles, and correlations (e.g., SIF correlated with EVI (r = 0.69) and NDVI (r = 0.6 to 0.69) and SIF correlated with GPP (r = 0.89 to 0.99)) [17; 69; 99; 100]. However, the strong correlations do not have the capacity to quantify differences in productivity metrics in terms of timing, which were seen in the normalized annual cycles (Figure 4a-c). Furthermore, correlations that have been used in the context of relating VIs to both satellite-based GPP and ground station GPP from flux towers have not often incorporated finer-scale seasonal timing and phenology. As mentioned in the recent literature (e.g., [3; 5; 33; 59; 5; 35]), if the examination of relationships stopped with correlations, it would appear as if SIFi, SIFd, GPP, NDVI, and EVI would serve as near equal proxies of GPP, but when paired with time series and other information related to timing, such as subsequent analyses in this study, the relationships change. For instance, the bivariate scatterplots reveal nonlinear relationships that change throughout the year (Figures 6 and 7). Bivariate scatterplots (Figures 6 and 7) show that SIF metrics, GPP, and EVI peak at temperatures just below maximum, and then they experience lower values of productivity at maximum temperatures. NDVI, however, saturates in the warm months. These results are consistent with studies that found that SIF is more sensitive to changes in function than NDVI [3; 8; 15; 17]. All productivity metrics, including NDVI, show more similar relationships to VPD. In a few studies that have examined satellite-based SIF or VIs relationships with VPD, SIF has been more sensitive to VPD and associated water and heat stress than Vis (e.g., [110; 111; 112]). The results, however, show that the scale of the satellite-based productivity metrics and the environmental data available (modeled and included in the remotely sensed data, except LST) were not able to capture the regional water stress that may have existed during 2017 drought conditions (Figures 6g-i and 7g-i). It was anticipated that by examining regional-scale environmental factors and using footprint-level comparisons of satellite-based productivity, SIF would capture this event and outperform VIs to a greater degree. But the results demonstrated that there was not sufficient information to capture subtle behavior within the same biome type or at regional scales, which is consistent with studies examining other biome types at different spatial and temporal scales ([3; 6; 30; 39; 40]); Figures 6 and 7). Instead, as suggested in [69], these subtleties and complexities require quantification for specific vegetation types and environmental conditions within the context of phenology and environmental variables that drive changes in these metrics [3; 8; 18; 60; 62; 65]. A time series of environmental factors, T\({}_{\text{can}}\), T\({}_{\text{air}}\), VPD, and LST, as well as intermediate variables (fPAR and LAI), were investigated due to their potential roles as drivers of change in productivity. Environmental and intermediate variables' time series plots and intra-annual cycles generally showed similar trends to those of SIF metrics and VIs, but some with leading or lagging patterns of minimum and maximum timing (Figures 3-5). To more quantitatively understand the relationships beyond correlations and trends, an analysis of the relative importance of variables indicated that SIF metrics and VIs were most influenced by intermediate variables, fPAR and LAI, followed by temperature (Tables 2 and 3, Figures 6-8).