## 4 Discussion
### SIF Retrievals with FIREFLY
#### 4.1.1 Indirect Retrievals from Solar and Oxygen Absorption Features
Figure 19: (**a**) False color RGB FIREFLY images (greens: vegetated targets; purples/pinks: non-vegetated targets); (**b**) NDVI FIREFLY images. Note that NDVI successfully removes the cloud shadows and shading from structure visible in the false color images. The final component of the decision-making process for forming a set of absorption features for an indirect SIF retrieval is the expected noise of the feature, relative to the expected SIF magnitude at the spectral location of the feature. To estimate the expected noise for a feature, it is necessary to have an estimate of radiance for the valley and peak bands, and to understand the relationship between noise and radiance for the instrument. The underlying principles of how noise interacts with indirect SIF retrievals have already been explored here (Figures 3 and 15). To provide a frame of reference for expected target radiances, the FIREFLY in-flight acquisitions used in this study were searched for vegetated and non-vegetated areas with internally similar spectral shapes. For each of these areas, the mean, minimum, maximum and standard deviation of radiance was derived per spectral band. Across eight in-flight scenes, a total of 12,149 non-vegetated pixels and 437,617 vegetated pixels were considered, and the results were used to parameterize forward modelling of uncertainty. Figure 21 displays several examples of absorption features, showing their observed radiances in vegetated pixels from in-flight acquisitions, and demonstrating simulated noise propagation via Monte Carlo simulation. Figure 20: The absorption features identified in simulated FIREFLY spectra are arranged according to their wavelength (resulting in the wavelength axis being not to scale). The proportional depth (left plot) and valley to peak band distance (right plot) for each feature in each spatial pixel is shown. A black point indicates that the absorption feature was not present in that spatial pixel. The spectral regions relevant to SIF retrieval (see Figure 16) are drawn to the right of the plots, and are scaled to match the wavelengths of the absorption features. Figure 21: (**a**) Absorption features (gray lines) shown with a FIREFLY vegetation spectra (black line), and a simulated SIF function (green dashed line, not to scale). (**b**) Radiances from in-flight acquisitions (see Appendix A Table A2) for the valley bands of the features shown as red lines in (**a**). The radiance range (vertical black line), mean (horizontal black line), \(\pm\) one standard deviation (blue lines), and minimum and maximum (red lines) are shown. (**c**) The frequency (left) and coefficient of variation (middle and right) of radiances for Monte Carlo simulation (100,000 samples) of noise distributions centered on the key values of the radiance ranges outlined in green in (**b**). Colors match those of the mean, minimum etc., in (**b**). (**d**) The 95% confidence intervals on the mean for the values outlined in green in (**c**), over the accumulation of samples from the Monte Carlo simulation. Direct retrievals from the valley bands (top row) and indirect retrievals (bottom row) are shown, along with expected SIF radiance (dashed green lines) based on the SIF function in Figure 1.  
In order to make a decision for an absorption feature based on the expected noise, there has to be a metric on which to base the decision. Various metrics and standards were proposed and tested during this study, but in Figure 21, the relationship explored is between the expected 95% confidence interval on the mean of the radiance and the expected SIF in the valley band, based on the simulated SIF function shown in Figure 1. Figure 21 demonstrates several key points. It is immediately clear that even within the limited in-flight acquisitions utilized in this study, the radiance values for individual bands varied considerably both within and between acquisitions.