## 1 Introduction  
Vegetation in the Amazon region is highly diverse and contains about one-half of the biomass stored in tropical forests [1; 2]. Tropical forests play an important role in the global carbon cycle by intaking up to \(\sim\)1.2 Pg of carbon (C) per year [3]. In recent decades, the Amazon region was hit by repeated severe drought events in 2005, 2010, and 2015-2016 [4; 5; 6]. Such events have the potential to alter the carbon cycle of the Amazon rainforest through biomass losses from, for example, increased tree mortality, and may thus lead to increased carbon emissions to the atmosphere [5; 6]. For the 2005 and 2010 thoughts, C emissions from the Amazon rainforest were estimated to be \(\sim\)1.6 Pg C and \(\sim\)2.2 Pg C, respectively [5]. During the period from September 2015 to March 2016, the terrestrial water storage in the Amazon basin was lower by one standard deviation (sd) than the 1900-2016 climatological seasonal cycle, and in August 2016, the rainfall remained 10% below the average values, leading to strong reductions in gross primary productivity (GPP; [7]). Such repeated drought events have the potential to turn the forest into a C source [5; 8]. Quantifying the GPP for the Amazon region remains a challenge due to the area's large spatial extent, remote field sites, and difficulties in installing long-term monitoring sites. Only recently have remotely sensed solar-induced chlorophyll fluorescence (SIF) tools become available, which may help overcome spatial and temporal gaps in the model-based up-scaling of GPP fluxes from eddy covariance measurements. SIF refers to the fluorescence radiance (photons) emitted from the chlorophyll molecules during plant photosynthesis. The emitted fluorescence wavelength ranges up to 600-850 nm, and constitutes both photosystems (I and II). This is proposed to be a remote sensing proxy for gross photosynthesis [9] that allows the constraining of carbon balance estimates [10]. The remotely sensed GOME-2 mission's SIF and the GPP measured using a network of carbon flux towers (FLUXNET-GPP) show a high correlation globally (r\({}^{2}\) = 0.74), albeit with regional variations [11]. For the Amazon region, high correlations (Pearson correlation coefficient = 0.80) were found between GOME-2's SIF, and an up-scaled GPP product that extrapolated the eddy covariance estimations using machine learning [7]. Within the Amazonian region, the phenological seasonality in vegetation communities has been attributed to climate factors; 76% of the SIF seasonality of Amazonia is explained by seasonal radiation variation, while 13% and 11% are explained by precipitation and a combination of both [12]. In contrast, when vegetation factors are disentangled from the climate variability of multiple forest types, two-thirds of Amazonian GPP variations are shown to be due to a forest's composition, structure, and nutrients, while just one-third is due to intra-annual climate variability [13]. Such GPP patterns agree with remotely sensed SIF from the GOSAT satellite [13]. The photons take in by the photosystems of plants rely on the incident photosynthetically active radiation (PAR) and the fraction of that radiance absorbed by the leaves (fPAR). The PAR absorbed by leaves is partitioned into photochemistry, heat dissipation (non-photochemical quenching; NPQ), and fluorescence emission (SIF). In general, SIF satellite measurements at the canopy surface result from the absorbed photosynthetically active radiation (APAR; the combination of fPAR and PAR), the fluorescence quantum yield in the band of measurement (\(\Phi_{F}\)), and the probability that photons can escape the canopy (Equation (1); \(\Omega_{C}\), [9]):  
\[SIF=PAR\cdot fPAR\cdot\Phi_{F}\cdot\Omega_{C} \tag{1}\]  
In canopies of temperate forests, APAR has been found to explain 85% of the variance in SIF [14], but the evaluation of SIF can be very challenging for a complex canopy (such as tropical ones; [9]). \(\Phi_{F}\) is genotype-dependent and affected by shading [15] and by nitrogen availability [16].