## 6 Discussion
### The Case Studies for Tower-Mounted Sensor Measurement Protocols  
This demonstration began with four cases, analysing a set of simulated noise-free set-up configurations in order to distinguish between errors derived from the SIF retrieval technique and errors caused by O\({}_{2}\) absorption effects: (1) using high-spectral resolution data and assuming the knowledge of surface reflectance; (2) compensating O\({}_{2}\) effects using the 3 FLD technique; (3) compensating O\({}_{2}\) effects using the SFM strategy; and (4) adapting an airborne atmospheric correction scheme and coupling it with the SFM. According to case (1) (Section 4.1), the need to correct for oxygen absorption effects was clearly demonstrated. It was shown that correcting the acquired radiance for the upward oxygen transmittance is critical, as is acquiring solar irradiance at the tower-mounted sensor level instead of at TOC, which negatively impacts the estimated SIFIn this case, we made use of high spectral resolution data and SIF was derived by simply isolating \(F\) from Equation (3) according to the different experimental configuration set-up considered. In this respect, no extra ISRF convolutions were applied in this example. In case (2) (Section 4.2), the effect of compensating the 3 FLD method for oxygen absorption was assessed. We demonstrated that this technique leads to high errors even in those set-up experimental configurations where the oxygen upward transmittance is corrected (set-up 1 and set-up 3). Many of those large errors were associated with negative SIF retrieval estimates, explaining the reason for this common problem in field studies. Compared to experimental results reported by [PERSON] et al. (2010), oxygen correction in the O\({}_{2}\)-A region when applying the 3 FLD method for a 20 m sensor height with a SR of 0.4 nm, impacts the estimated SIF around \(\sim\)5% of its value. However, in our simulated study the analogous example (Figure 8, set-up 1, SR of 0.4 nm) would lead to errors in retrieved SIF of around 20% when comparing estimated SIF at sensor with SIF at TOC level. Although the significant difference reported in relative errors (from 5% to 20%), in absolute terms estimated errors in both studies are consistent, leading to a slight difference of \(\sim\)0.1 mW/m\({}^{2}\)/sr/nm due to the contrasting SIF values at O\({}_{2}\)-A used in [PERSON] et al. (2 mW/m\({}^{2}\)/sr/nm) and in this study (1 mW/m\({}^{2}\)/sr/nm).  
With regards to case (3) (Section 4.3), the compensation of the oxygen transmittance effects in the SFM delivered interesting results. As expected, more accurate SIF estimations were found in experimental set-up 1 and set-up 3, where upward oxygen transmittance is corrected. However, for all the configurations evaluated, our results indicated that estimated SIFs outside the deepest region of the O\({}_{2}\)-A band were biased. The observed shift in the slope of scatter plots shown in Figure 11, which affects all set-up configurations is attributed to the ISRF convolution of each of the individual terms that compose Equation (11). Thus, case (3) becomes an interesting strategy when focusing on quickly deriving a SIF value corresponding to the deepest region of the O\({}_{2}\)-A band, and therefore avoiding the use of any RTM. In this case, we can avoid the use of any atmospheric RTM by modelling the oxygen transmittance using the HITRAN database or the empirical expression developed by [PERSON] (2011) and presented in Equation (9). Alternatives to RTMs mentioned here allow the computation of the oxygen transmittance while taking into account its dependency on temperature and pressure conditions.