## 4 Discussion
### SIF Retrievals with FIREFLY
#### 4.1.1 Indirect Retrievals from Solar and Oxygen Absorption Features  
Indirect SIF retrievals are those that utilize baseline knowledge of the relative radiance expected between a pair, or two groups, of bands in the absence of SIF to detect the presence, and quantify the magnitude, of an additive SIF signal. The most common form this approach takes, is to estimate the proportional depth of an absorption feature that is expected to be consistently represented in instrument observations. The main advantage of indirect retrievals is that there is no requirement for any estimation of irradiance conditions, nor of target reflectance, nor of any other influence that would scale the bands equally. The first consideration for attempting indirect SIF retrievals is the availability and properties of the absorption features represented in the acquisitions of an instrument. As described in Section 2.3, absorption feature valleys were identified in simulated FIREFLY spectra for the full spectral range of each spatial pixel, and then paired with spectrally proximal peak bands. The detailed characterization of FWHM in this study was essential to this process, as the expected depth of absorption features changes with the FWHM, and some absorption features even disappear entirely across the FWHM range of FIREFLY. Figure 5 shows examples of absorption feature depth varying with FWHM, and Figure 16 shows the variation in the number of absorption features represented in each FIREFLY spatial pixel. The importance of characterizing FWHM thoroughly, and simulating absorption features accurately, is further emphasized by Figure 17. This figure shows that the proportional depths of absorption features change non-linearly with FWHM across the spatial pixels of FIREFLY. Furthermore, there are some examples of sharp inflections in the proportional depths, resulting from the changing FWHM causing a band to gain or lose the influence of prominent shapes in the underlying high-resolution reference spectra from which the simulated FIREFLY spectra are convolved. Because of these factors, if an instrument has FWHM variation, then it is essential that the baseline  
Figure 16: **(a)** An example FIREFLY vegetation spectra (black line) with a simulated SIF signal (green dashed line, not to scale). Each panel highlights a spectral region that might require different assumptions or supporting information: The top panel shows oxygen B (pink) and oxygen A (red); the middle panel shows sloped leaf reflectance (yellow) and the region where SIF can be expected to be polynomial in shape (green); the bottom panel shows the region subject to the influence of water vapor (blue); and all panels show the narrow regions (gray) not included in any other spectral region. **(b)** The number of solar absorption features found across the spatial dimension of FIREFLY, if each spectral region is excluded (line colors correspond to regions in **(a)**, with red incorporating both oxygen A and oxygen B regions). knowledge of feature proportional depths for indirect retrievals considers each spatial pixel separately, as is achieved here. Even if a real reference target is used, it must be observed separately with each spatial pixel of the instrument, which could be challenging for in-scene reference targets. For in-scene reference targets, the recommendation would be to pre-identify a ground target that could provide relatively homogenous cover across the FIREFLY swath when the instrument is at its typical acquisition altitude. Examples of suitable targets could include a perpendicular runway, empty highway, calm body of water, or large roof. To quantify the importance of the thorough characterization of the FWHM variation of FIREFLY, simulated reference spectra, based on the lowest FWHM in the detector array (0.16 nm), was used as the baseline knowledge of proportional depth for a SIF retrieval from all spatial pixels, across all the in-flight FIREFLY data in this study.