# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 4 Discussion  
### SIF Responses to Environmental Variation  
Fluorescence sampled from Caatinga vegetation seems to vary in similar amplitude as the environmental indicators at the different ecoregions (Figures 4 and 5). The sensitivity of GOME-2 SIF to environmental variation can also be established by observing the results of our Linear Mixed  
Figure 11: Relative Change of mean 2012 values in relation to mean 2007–2011 values for all variables. Thick lines within boxes are medians. Precip is Precipitation Rate; SM is Soil Moisture.; dLST is daytime Land Surface Temperature; EVI is MODIS-MAIAC Enhanced Vegetation Index; NDVI is MODIS-MAIAC Normalized Difference Vegetation Index; \(\text{SIF}_{FR}\)-Prod is the product of \(\text{SIF}_{FR}\) by NDVI; \(d\text{SIF}_{FR}\) is the daily average of SIF based on a clear sky PAR proxy; \(\text{SIF}_{FR}\)-SZA is the quotient of \(\text{SIF}_{FR}\) by the cosine of the Sun’s Zenith Angle (SZA); \(\text{SIF}_{FR}\) is Sun-Induced Fluorescence at the far-red peak; \(\text{SIF}_{FR}\) is Sun-Induced Fluorescence at the red peak; \(\text{SIF}_{FR}\)-Yield is the quotient of \(\text{SIF}_{FR}\) by NDVI; \(\text{SIF}_{R/FR}\) is the ratio between \(\text{SIF}_{R}\) and \(\text{SIF}_{FR}\). Colors separate the variable types: environmental, spectral vegetation indices and SIF-related. 2012 period \(n=12\) while 2007–2011 period \(n=59\), per variable.  
Models (Table 2) and by comparing the relative change observed at the 2012 drought among the various variables (Figure 11) and between NDVI, \(\text{SIF}_{FR}\) and \(\text{SIF}_{FR}\)-Yield at each ecoregion (Figure 12). The variables \(\text{SIF}_{FR}\)-Prod, \(d\text{SIF}_{FR}\), \(\text{SIF}_{FR}\)-SZA and standard \(\text{SIF}_{FR}\) presented a median drop of more than 40% during the year of 2012 in relation to the period from 2007 to 2011 (Figure 11). While these variables include the phenological influences of biochemistry and structure on SIF radiative transfer, \(\text{SIF}_{FR}\)-Yield has had most of these influences removed but it also dropped by a median value of over 25% in 2012. Considering that both MODIS EVI and NDVI corrected through the MAIAC algorithm have a finer spatial resolution than GOME-2 SIF, it is interesting to see that \(\text{SIF}_{FR}\)-Yield mean value still dropped by a higher margin than that of the sampled SVIs (Figure 11).  
Figure 12: Ecoregion-specific relative change on NDVI, \(\text{SIF}_{FR}\)-Yield and \(\text{SIF}_{FR}\) observed on the year 2012 (panel _a_), and on the period from 2013 to 2017 (panel _b_), in relation to the period from 2007 to 2011. Eoregions are represented by letters, corresponding to Figure 1: (a) Campo Maior; (b) Northern Sertaneja Depression; (c) Aarripe-Ibiopaba Complex; (d) Borborema Plateau; (e) Major Southern Sertaneja Depression; (f) Sao Francisco Dunes; (g) Raso da Catarina; (h) Minor Southern Sertaneja Depression; (i) Chapada Diamantina. \(n=12\) for each variable at each ecoregion.  
Results from our study show that the interpretation of chlorophyll fluorescence emissions is complex but very informative. As this study demonstrates and as it has been argued in recent reviews [3; 11], the main factors to consider for the interpretation of SIF seem to be the resolution and temporal aspect of measurements and the characteristics of the vegetation being measured. Due to the nature of our study and our choice of GOME-2 SIF data, temporal influences in our results could not be controlled and thus, could not be directly tested. Phenology however, consisting of temporal variation on biochemical and structural characteristics of vegetation, has an apparently strong influence on SIF measured at the Caatinga as we will now discuss.