# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 4 Discussion
### Linear Mixed Models and Our Adjustments to SIF  
The LMM model using SIF\({}_{FR}\)-SZA, the normalization of SIF\({}_{FR}\) values by the cosine of the Solar Zenith Angle (SZA), effectively improved SIF's relationship to environmental data (Table 3, slightly higher marginal R\({}^{2}\) and considerably lower RMSE, REML and BIC). These results contrast with conclusions from previous studies suggesting that SZA normalization could amplify noise if applied to measurements taken from lower intensity emissions (as observed at the Caatinga) and influence perceived sun/shade effects [29]. Furthermore, it is interesting to notice that our study region is under direct influence from the South Atlantic Anomaly (SAA) and it is, therefore, inherently noisy and perhaps a \"worst-case scenario\" for SZA normalization (see PAR normalization in [PERSON] et al. [28]).  
The SIF\({}_{FR}\)-Prod adjustment, based on the classic productivity equation of [PERSON] and [PERSON] [62; 63], already shown in a preliminary study to increase the correlation between GOME-2 SIF and FLUXNET GPP across diverse vegetation types [66], yielded significant improvements on LMM goodness-of-fit in relation to standard SIF (Table 3). However, due to the substantial increase it caused in the effect size of the Ecoregion factor on tested LMMs (Table 2) and, considering that this effect encapsulates part of the structural variability between the different ecoregions, we conjecture that SIF\({}_{FR}\)-Prod may be \"double-counting\" the influence of emitting vegetation as it is calculated by multiplying SIF by NDVI and both variables are strongly influenced by vegetation structure and biochemistry. This is also suggested by the fact that although the SIF\({}_{FR}\)-Prod LMM had the highest coefficients of determination, it had relatively worse quality than what was observed with the SIF\({}_{FR}\)-SZA LMM (Table 3, RMSE, REML and BIC are higher for SIF\({}_{FR}\)-Prod than for the SIF\({}_{FR}\)-SZA LMM).  
The spectral adjustment here called SIF\({}_{FR}\)-Yield, consisting on dividing the observed GOME-2 SIF by corresponding NDVI, had the largest effect on SIF among the tested modifications (Tables 2 and 3). The observed decrease in goodness-of-fit of the SIF\({}_{FR}\)-Yield LMM in relation to models using SIF\({}_{FR}\), \(d\)SIF\({}_{FR}\), SIF\({}_{FR}\)-SZA and SIF\({}_{FR}\)-Prod is in agreement with the hypothesis that SIF\({}_{FR}\)-Yield normalizes SIF data according to biochemistry and structure as sampled by NDVI, removing much of these influences from SIF data in the proportion to which NDVI is able to capture them. Because in our LMMs the Ecoregion factor encapsulates all unmodelled differences between the sampled vegetation at the different ecoregions, the drop in relative effect of that factor in relation to environmental factors Temperature and Soil Moisture in Yield-normalized models (Table 2) shows that much of these unmodelled differences concern biochemical and structural influences on GOME-2 SIF data. Certainly NDVI would not be an adequate proxy to biochemistry- and structure-related effects on SIF emissions for all types of vegetation but, it is worth noting that NDVI at the Caatinga is not subject to strong saturation effects and it seldom rises above 0.7 in the majority of the region (Figure 9). We suggest that Yield-normalization is necessary for interpreting fluorescence measurements as our results show that SIF-Yield adds another perspective on SIF measurements (Figure 12), separating the long-term effects of phenology, biochemistry and structure (related to photosynthetic capacity) from the transient photosynthetic rate-related effects (related to photosynthetic performance).  
Therefore, our findings support previous suggestions that SIF has potential to improve our understanding of plant responses to drought [30; 31; 32] and exemplify in a case study the premise that SIF can be more informative when combined with vegetation indices.