# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 4 Discussion
### Apparent Influence of Vegetation Differences in SIF Responses  
Land cover analysis through ESA's GlobCover 2009 product agrees with the previously described dominance of shrulbands and open forest physiognomies at the Caatinga area, as described by major biogeographic studies [37; 89]. The prevalence of crop-related classes (mosaic classes, Figure 3 and Appendix A Tables A1 and A2) is supported by the previously reported presence of subsistence agriculture with little or no irrigation at the Caatinga [36; 40]. This simple land cover analysis supports our assumptions that Caatinga vegetation is a mosaic of diverse physiognomies, including widespread human occupation and land cover by subsistence agriculture, and shows that the ecoregions identified within the Caatinga [35] are different concerning not only geomorphology, climate and soil but also in their vegetation (Figure 3 and Appendix A Tables A1 and A2). Our results suggest that vegetation types and their particular characteristics significantly influence the observed seasonality of SIF and its apparent responses to abiotic stress (Table 4). We suggest that such influences might help to explain why some of our results seem counter-intuitive, or discrepant. For example, the seasonality of SIF\({}_{FR}\) and SIF\({}_{R}\) at the Ibiapaba-Aarripe Complex ecoregion shows a discrepant pattern with a very small seasonal variation in SIF\({}_{R}\) that doesn't seem comparable to that of SIF\({}_{FR}\) at that same region (Figure 4c). While we could speculate on differences in local vegetation composition (e.g., more presence of cacti, differences in deciduousness between vegetation strata) between the local types and those at other regions, we do not know the reasons for this particular discrepancy. Furthermore, we have also observed considerable differences in the correlation of SIF and SVIs between different areas of the Caatinga (Figure 10 and Appendix A Table A4). Considering the nature of these data we suggest that temporal differences in responses to environmental variation must influence the observed correlation discrepancies since, while SIF can respond near instantly to environmental constraints SVIs will depend on slower biosynthesis and conversion of pigments and on similarly slow structural changes (Figure 11). Daily cycles of environmental variation and seasonal change in the ranges of such daily environmental variation have been already been shown to influence chlorophyll fluorescence output in temperate vegetation [90] as well as in the tropical semiarid vegetation of Caatinga [91]. Therefore, the time of day when measurements are made is greatly significant for the interpretation of SIF but not for the interpretation of vegetation indices like EVI and NDVI. Thus, we suggest that daily maxima of SIF should be more directly comparable to vegetation indices in the context of seasonal studies. Nevertheless, considering the spatial pattern observed in the correlation between SIF and NDVI (Figure 10) and how that matches the long term ecoregion-specific correlation discrepancies (Appendix A Table A4), we believe it is likely that vegetation structure and composition are also influencing these results. Despite such uncertainties, the reliability of satellite measurements of SIF is further illustrated by the coherence of LMM slopes and land cover classification data. As our results have shown, the higher the percentage of the ecoregion's vegetation was classified as forest, the weaker was that ecoregion's response to tested environmental variables (Table 4). This is reasonable considering that the environmental factors modelled in LMMs were soil moisture and surface temperature (related to variations in water availability and vapour pressure deficit effects on vegetation) and that root depths in forest physiognomies are greater and also that moisture is conserved in the system due its structural characteristics [44].