## 1 Introduction  
Chlorophyll fluorescence (ChlF) is defined as the light emitted by photosynthetic organisms with peaks at 687 (red ChlF) and 740 nm (far-red ChlF) [1]. The study of ChlF goes further than the estimation of basic structural plant traits like those usually analyzed from conventional multispectral remote sensing [2], e.g., throughout vegetation indices based on red-green-blue (RGB) and near infrared (NIR) reflectance. ChlF occurs in competition with heat to dissipate absorbed radiation not used in the light reactions of photosynthesis. Thus, the variation in the efficiency of one process affects the efficiencies of the others. This link forms the rationale for the use of ChlF to infer the plant physiological status, improving the understanding of the health \(\leftrightarrow\) stress status dynamics of plants in agricultural and environmental studies [3]. Recent studies successfully used ChlF as a proxy for water stress [4], leaf nitrogen [5], nutrient status [6], biomass determination [7], and gross primary production (GPP) [8]. The authors of [9] summarize the main physiological processes, such those related to photo-protection, that might be affected by specific ChlF drivers at ecological and temporal scales. Several options are available for the assessment of ChlF, such as the light induced fluorescence transient (LIFT) active sensing method, which provides accurate ChlF field estimations especially useful in the context of high-throughput phenotyping field experiments [10]. However, this proximal technique is not viable for open field studies, as it is only suitable for close range observations. Large scale monitoring becomes feasible with the passive sensing of solar induced ChlF (SIF) [9] on the basis of hyperspectral techniques using spectroradiometers, e.g., on airborne and orbital platforms [11]. SIF can be quantitatively obtained throughout spectral measurements by the Oxygen-A (O\({}_{2}\)-A, 760 nm) and Oxygen-B (O\({}_{2}\)-B, 687 nm) absorption features of the atmosphere, where the ratio between the ChlF signal and the reflected radiance is higher [12] due to the absorption of the incoming irradiance (\(>\)90% in O\({}_{2}\)-A) [2,9]. SIF is considered the most direct remote sensing signal to infer the actual functional state of the photosynthetic apparatus and its dynamics at leaf, canopy, ecosystem, or even global scale. However, the SIF-photosynthesis relationship is influenced by several factors, including environmental conditions, structural traits, stress effects, and re-absorption processes by chlorophyll [9]. Hence, ancillary information is needed to interpret fluorescence changes and link them to variations in the photosynthetic efficiency. Moreover, an accurate retrieval of SIF is crucial to understand photosynthesis and its dynamics [13]. The three main SIF retrieval methods are: the Fraunhofer Lines Depth (FLD) [14], the spectral fitting methods (SFM) [15], and the singular vector decomposition (SVD) [16]. The relatively low intensity of the ChlF signal (\(<\)1%-5% of the reflected NIR radiation) [2] makes the retrieval of SIF challenging, and numerous studies tried to address this. Satellite missions, for example the Orbiting Carbon Observatory-2 (OCO-2), the Gases Observing Satellite (GOSAT), and the Global Ozone Monitoring Experiment-2 (GOME-2) were used to retrieve SIF for the assessment of physiological parameters like carbon fixation [17; 18]. Other missions such as the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIMACHY), the tropospheric Monitoring Instrument (TROPOMI), and the Exploratory Satellite for Atmospheric CO\({}_{2}\) (TanSat) have been used for similar purposes [19]. Airborne SIF estimations are also being explored by means of the HyPlant sensor: a system composed by two spectroradiometers covering the 400-2500 nm and 670-780 nm spectral ranges [20].