## 6 Discussion  
### Ground-Based Validations  
Accurately measuring SIF variations due to changes in the environmental conditions can facilitate its interpretation and constrain SIF-GPP relationships. Recent studies reported a correlation between SIF and GPP by combining GPP data acquired either from Eddy-Covariance (E-C) flux towers or  
Figure 14: Spectral oxygen transmittance variation for the O\({}_{2}\)–A (**a**) and O\({}_{2}\)–B (**b**) absorption bands for the \(T\) and \(p\) conditions registered at 30 m tall tower at the Hyytiäla Forest Field Station in Finland. Figure 15: Variation in radiance units computed as \(L_{sem}\)(DOY)-\(L_{sem}\)(DOY=1) for the O\({}_{2}\)–A (**a**) and the O\({}_{2}\)–B (**b**) absorption regions. The acronym DOY refers to the Day Of Year from 1–365. satellites, with satellite-derived SIF from the Greenhouse Gases Observing Satellite (GOSAT) [47; 48], the Global Ozone Monitoring Mission-2 (GOME-2) [7; 49; 50; 51] and with the Orbiting Carbon Observatory-2 (OCO-2) [8]. However, some concerns regarding the ecophysiological basis between SIF and GPP relationship under varying environmental conditions are still present in the scientific community [52; 53], and particularly for the gap in the mechanistic understanding between SIF-GPP temporal short-term and spatial small-scale mechanisms [54]. In this respect, systematic SIF tower based measurements can play a key role: (1) to validate SIF maps derived at a global scale from satellite; (2) to analyse the impact of vegetation structure on emitted SIF; and (3) to better understand the existing relationship between SIF and other energy fluxes such as the GPP, the ecosystem respiration, latent and sensible heat fluxes, etc., which are typically related products indirectly derived from E-C flux towers. At proximal sensing scale, the idea of linking ground-based remote sensing measurements to ecosystem CO\({}_{2}\) flux data has been addressed in the past by many international initiatives, such as the SPECNET [55], and the European cost actions EUROSPEC ES0930 [5] and the OPTIMIISE-ES1309 ([[http://optimise.des.aber.ac.uk/](http://optimise.des.aber.ac.uk/)]([http://optimise.des.aber.ac.uk/](http://optimise.des.aber.ac.uk/))) networks. These projects have explored the use of proximal passive optical remote sensing data of ecosystems whereby carbon and water vapor fluxes are estimated at research-tower sites by E-C techniques. In particular, these network initiatives have focused on analysing, comparing and standardizing measurement protocols; while also promoting the design, testing, and development of new optical instrumentation. However, when SIF is one of the biophysical parameters under study, additional critical attention must be drawn to processing strategies as well as to instrument and measurement protocols. For instance, while instrument specifications needed to determine some biophysical remote sensing indexes such as the Normalized Difference Vegetation Index (NDVI) [56] or the Enhanced Vegetation Index (EVI) [57], can be easily reached; measuring SIF from passive remote sensing techniques is still quite challenging. In terms of the instrument specifications, high spectral resolution spectrometers with high signal to noise ratio are generally required, but these instruments are more expensive and more difficult to maintain [46]. In terms of data processing, as demonstrated in this paper, atmospheric absorption effects cannot be ignored, even when measuring data at a few meters distance from the TOC target. In this study, we demonstrated the need for compensating O\({}_{2}\) molecular absorption when measuring SIF using proximal remote sensing techniques inside the O\({}_{2}\)-A absorption band. Since oxygen absorption is proportional to surface pressure, even a few meters distance between the TOC target and the sensor strongly impacts the retrieval of the weak fluorescence signal.