## 1 Introduction
However, these assumptions are not met when the atmospheric path length increases, as with a higher mount on towers or with larger solar or view zenith angles (SZA, VZA) [36]. Besides that, since oxygen absorption is proportional to air pressure [37], at the bottom of the atmosphere where pressure is greatest even a few meters distance between the target and the sensor can result in significant errors in the retrieved SIF at TOC [36]. It is worth mentioning that some field campaigns measuring SIF from systems mounted on towers have already incorporated a processing strategy to correct radiances for atmospheric effects [10; 38; 39]. Also, in a recent publication from [PERSON] et al. (2017) [40], transmittance effects on SIF canopy-leaving measurements made with hemispherical sensors were analysed through an effectivetransmittance correction. Nevertheless, a comprehensive analysis that evaluates atmospheric effects on SIF measurements acquired at proximal sensing is still required. Therefore, to complement previous analyses about this topic under a theoretical framework, this paper focuses on assessing:  
* How ignoring atmospheric effects can distinctly impact the success of the technique applied (FLD or SFM) to disentangle SIF from reflected light? * What could be the best strategy to correct proximal sensing data for atmospheric effects? * Is it possible to adapt atmospheric correction strategies used to process airborne data for proximal sensing measurements? Additionally, this paper describes for the first time how to consistently couple formulations applied to correct proximal-sensing data for atmospheric effects with formulations used for retrieving SIF at canopy level. Our approach is similar to that in a previous study developed by [PERSON] et al. (2014) where the coupling between atmospheric and SIF retrieval strategies was addressed at satellite scale. Both FLD and SFM methods are based on the idea that radiance measured at TOC, \(L_{\text{TOC}}\), is composed of the contributions of both the reflected solar irradiance and the emitted SIF. However, this condition is not exactly met when \(L_{\text{TOC}}\) is not measured at TOC but, rather, estimated through inversion of an atmospheric correction scheme ([PERSON] et al., 2014). Radiance acquired at a certain distance from the target, \(L_{sen}\), is provided according to its specific Instrumental Spectral Response Function (ISRF), noted here as \(\langle L_{sen}\rangle\). Even in the most simple scenario when correcting the surface radiance acquired at the top (or some position) on a tower \(\langle L_{sen}\rangle\) for the atmospheric transmittance \(\langle T\rangle\), this would imply dividing two non-smooth functions already convolved to derive \(\langle L_{\text{TOC}}\rangle\). Mathematically, given two functions \(a\) and \(b\), this inequality always exists \(\langle a\rangle\cdot\langle b\rangle\
eq\langle a\cdot b\rangle\), which is especially important when working with high spectral resolution data in absorption regions ([PERSON] et al., 2014). At the same time, it must be considered that atmospheric correction strategies generally assume simplifications in their formulation, e.g., assuming a Lambertian surface reflectance behaviour; or they must deal with the correction of adjacent contributions of light from surrounding areas. Further, when focusing on the oxygen absorption spectral regions, O\({}_{2}\) transmittance dependency on air temperature (\(T\)) and surface pressure (\(p\)) must be considered. In this paper, we aimed to assess to what extent seasonal changes in \(T\) and atmospheric \(p\) would interfere with the retrieved SIF signals, especially over long temporal data series when this dependency is not accounted for as part of the oxygen transmittance correction.