| Summary: | In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIF<sub>fuzzy</sub> and SIF<sub>fuzzy-APAR</sub>. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The <i>HyPlant</i> derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIF<sub>fuzzy</sub>. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIF<sub>fuzzy</sub> was multiplied by APAR (SIF<sub>fuzzy-APAR</sub>). The agreement between the modelled SIF<sub>fuzzy</sub> and actual SIF airborne retrievals expressed by R<sup>2</sup> ranged from 0.38 to 0.69 for SIF<sub>760</sub> and from 0.85 to 0.92 for SIF<sub>687</sub>. The inclusion of APAR improved the R<sup>2</sup> value between SIF<sub>fuzzy-APAR</sub> and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.
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