## 4 Discussion
### SIF Retrievals with FIREFLY
#### 4.1.1 Indirect Retrievals from Solar and Oxygen Absorption Features
This approach provides an approximate integration of the error that would result in SIF retrievals if the minimum FWHM observed for FIREFLY was assumed for all spectral bands and spatial pixels. Targets that should not have contained SIF, determined by normalized difference vegetation index (NDVI) value of 0, had a mean absolute SIF estimate equivalent to 4.3% of target radiance in each band (\(\pm\)2.9, 95% CI), which was also equivalent to 161% of expected SIF, based on the example simulated SIF function shown in Figure 1 (\(\pm\)51.2, 95% CI). This would obviously be an unacceptable level of uncertainty, and confirms that a robust FWHM characterization is essential for FIREFLY to attempt indirect SIF retrievals. The next step towards an indirect retrieval from FIREFLY would be to choose an appropriate subset of the available absorption features from which to estimate SIF. Several factors affect the choice of whether to include a particular absorption feature: the spectral region in which it resides; its proportional depth; the expected radiance, and therefore the expected noise, of its valley and peak bands; and the expected relative magnitude of SIF at its spectral location. It should be noted that these factors are far from independent, and that the selection of appropriate thresholds to make a decision based on each of these factors is likely to be a complex and dynamic process for an operational SIF retrieval product. However, establishing the tools for making such decisions for FIREFLY retrievals was among the aims of this study, and these are demonstrated below. Figure 17: Examples of absorption feature proportional depths (colored lines and valley wavelength labelled) changing over the spatial pixels of FIREFLY as a result of the variation in FWHM. Several regions of the spectral range of FIREFLY (670 nm-780 nm) would require particular assumptions to be met, or additional information to be acquired in order to effectively utilize absorption features in those regions (Figure 16). The regions containing the oxygen A (759 nm-770.5 nm) and oxygen B (686 nm-715 nm) features are subject to the influence of oxygen absorption, and therefore would require atmospheric conditions to be accounted for in each in-flight collection. Likewise, the region influenced by water vapor (\(<\) 747 nm) would need a quantitative representation of atmospheric conditions. Atmospheric modelling for each in-flight acquisition would be the most likely solution for these regions, to which coincident data from other G-LiHT instruments could prove useful. For example, the downwelling radiometer could provide irradiance profiles, and the lidar instruments could provide ranges to estimate the length of the atmospheric column for each ground target (Figure 18). Ultimately, any errors in characterizations of atmosphere would propagate to error in SIF retrievals in these regions. Since even minor sloped influences have been shown to produce relatively large errors in SIF retrievals (Figures 3 and 14), further study into an appropriate atmospheric model, and the accurate retrieval of parameters with G-LiHT data would have to be conducted before these regions could be used in an operational product. Leaf reflectance is expected to have a steep slope in the central region of FIREFLY (756 nm-686 nm). To utilize absorption features in this region effectively, an estimate of the slope would be required. Leaf reflectivity slopes are well-represented in FIREFLY data (Figures 1 and 10), and could be estimated with a ratio of bands similar to the process of NDVI (Figure 19). Using groups of bands for this process could help to mitigate the effects of noise and reduce uncertainty, which is particularly important given the sensitivity of indirect SIF retrievals to sloped influences (Figure 3). It should be noted that a substantial portion of the region with leaf reflectance shape overlaps with the region of water vapor influence, and the region of oxygen influence around the oxygen B feature.