## 4 Discussion
### SIF Retrievals with FIREFLY
#### 4.1.1 Indirect Retrievals from Solar and Oxygen Absorption Features
It is also worth considering the potential of indirect SIF retrievals using just the oxygen absorption features. Since these features typically have extremely low radiances inside their valleys, the best ratios between SIF and noise are expected to be found there. The mean radiance of the oxygen A feature valley across vegetation targets drawn from the in-flight data in this study was 16.88 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) (\(\pm\) 4.195% CI, N = 437,617), which is equivalent to an expected standard deviation of 0.4 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). For context, the amount of SIF found in the oxygen A valley, based on the function shown in Figure 1, would be 1.17 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). The proportional depth of the oxygen A feature was found to be 0.11 (\(\pm\)0.03 95% confidence interval), which would be quite resistant to non-uniform influences (Figure 14). However, the extreme depth of the oxygen A feature arises partly because of the relatively high radiance of the peak band, with a mean of 164.94 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) across the in-flight acquisitions, resulting in an expected standard deviation of 1.17 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). The oxygen B feature is represented in the in-flight vegetation targets with a mean proportional depth of 0.757; a mean valley radiance of 4.26 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\); and mean peak radiance of 7.71 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). These low radiances in both bands give a favorable relationship between the expected standard deviations of 0.21 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) for the valley, and 0.28 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) for the peak, and the estimated SIF at the wavelength of the valley of 1.26 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). These evaluations of indirect retrievals from oxygen features are encouraging, but there would still be considerable uncertainty when the expected noise is propagated through to a simulated SIF retrieval. Therefore, similar recommendations to those previously discussed: to mitigate noise through spectral binning, spatial aggregation, and repeated observations, can be applied to indirect SIF retrievals from the oxygen features. The one exception would be that the narrow FWHM and high resolution spectral sampling of FIREFLY provide representations of the valleys of the oxygen features with several bands each, facilitating spectral binning. The oxygen A feature is typically represented by five bands, and the oxygen B feature is represented by three bands within a 10% total variation according to assessment of the relative depth according to the in-flight vegetation targets. Finally, indirect retrievals from oxygen features, unlike solar absorption features, require atmospheric effects to be accounted for, since the valley bands will be subject to oxygen absorption and the peak bands will not. Therefore, it is necessary to either utilize an atmospheric model parameterized with retrieval conditions, or to utilize an in-scene reference target with equivalent path-length to other targets. Each of these approaches could add uncertainty to SIF retrievals, and warrant further study. However, the high resolution (\(\sim\)3 cm ground resolution at typical acquisition altitude) RGB camera integrated into G-LiHT could help identify vegetation-free reference targets in combination with G-LiHT's other multispectral data [1].