## 4 Discussion
Taken together, these results support findings of previous studies showing that VPD and productivity of grasslands and seasonally dry tropical forest (measured with NDVI) vary widely and that change in climate conditions (VPD) in the wet season correlates with greater productivity [116]. However, the IGBP classification utilized in our study does not allow us to distinguish seasonally dry tropical forest or to match our results in-kind to previous studies. In order to distinguish particular vegetation class productivity behavior in future work, improved classification resolution or alignment of classification with other studies, including a seasonally dry tropical forest, would be an important component to better understand productivity in the Caatinga. We found the Cerrado mean AVC \(\mathrm{SIF_{d}}\) was not different from the Atlantic Forest, Pampa, and Pantanal at the biome level, but was different than the Caatinga and Amazon. Also, analysis of mean \(\mathrm{SIF_{d}}\) among vegetation classes did not further distinguish the Cerrado. However, the Cerrado and Caatinga mean \(\mathrm{SIF_{d}}\) correlations to \(\mathrm{T_{can}}\), \(\mathrm{T_{air}}\), and VPD were negative and strongest with VPD followed by \(\mathrm{T_{can}}\), in contrast to other biomes, especially the Pampa, where mean \(\mathrm{SIF_{d}}\) correlations were positive and strongest with \(\mathrm{T_{can}}\). and in contrast to the Pantanal, where no significant correlations of mean \(\mathrm{SIF_{d}}\) with \(\mathrm{T_{can}}\), \(\mathrm{T_{air}}\), nor VPD occurred (Table 1). These results suggest that to capture variation in vegetation function, it is necessary to investigate the individual vegetation class \(\mathrm{SIF_{d}}\) in the context of climate variables because differences in the response of SIF in the Cerrado and Pampa would have been missed without investigating correlations of \(\mathrm{SIF_{d}}\) with \(\mathrm{T_{can}}\), \(\mathrm{T_{air}}\), and VPD. The Cerrado was further distinguished from the Atlantic Forest, Pampa, and Pantanal biomes by seasonality analysis of \(\mathrm{SIF_{d}}\). In the Cerrado, for instance, base integrated \(\mathrm{SIF_{d}}\) is lower for all classes than Atlantic Forest, while total and pulse integrated \(\mathrm{SIF_{d}}\) is higher all vegetation classes. So, year-round \(\mathrm{SIF_{d}}\) in the Cerrado is less than in the Atlantic forest, but there is a higher degree of seasonality, likely driven by more pronounced cycle of environmental factors in the Cerrado versus the Atlantic Forest. Cerrado EBF base integrated \(\mathrm{SIF_{d}}\) exceeds the base integrated \(\mathrm{SIF_{d}}\) in the Pampa and Pantanal, but has a smaller seasonal pulse, while GRA, SAV, and WSAV SIFd in the Cerrado have lower base and seasonal pulses compared to the Pampa and Pantanal. These results indicate that the climate in the Pampa and Pantanal is more favorable to GRA, SAV, and WSAV productivity year-round and seasonally than the Cerrado, but only seasonally for \(\mathrm{EBF\ SIF_{d}}\). Thus, productivity of GRA, SAV, and WSAV in the Cerrado is more hindered than in the Pampa or Pantanal, likely due to the climate of the Cerrado being warmer and drier than the Pampa and drier than the Pantanal (Figure 5). In all three biomes, however, the largest values of \(\mathrm{SIF_{d}}\) are captured in the EBF classes, consistent with higher biomass as expected. Furthermore, our seasonality analysis indicates the average length of season in the Cerrado is not different than any other biome (within one day of the Caatinga), but differences occur in seasonal and year-round levels of \(\mathrm{SIF_{d}}\), indicating that OCO-2 SIF captures photosynthetic activity differences. To capture subtler spatiotemporal differences, we suggest it is necessary to investigate more than differences in means based on temporal patterns that would be missed without seasonality analysis.