## 1 Introduction  
Photosynthesis, the light-driven conversion by plants of atmospheric carbon dioxide (CO\({}_{2}\)) into carbohydrates, is one of the Earth's largest sinks for CO\({}_{2}\), thus playing a key role in determining the global carbon (C) balance [1; 2]. The spatial and temporal dynamics of photosynthetic CO\({}_{2}\) uptake (Gross Primary Productivity, or GPP) can provide key information about the magnitude and variability of the fraction of atmospheric CO\({}_{2}\) that is absorbed by the terrestrial surface. Direct quantification of GPP can be carried out via micrometeorological methods, although this approach measures the Net Ecosystem Exchange (NEE), which is a combination of both GPP and ecosystem respiration, and therefore these processes need to be disentangled. NEE measurement, although being routinely made at hundreds of flux sites around the world, falls short in representing the spatial and temporal variability of these processes globally [3]. Current methods used for estimating GPP include: (1) A combination of eddy covariance, remote sensing and gridded satellite climate products, e.g., Reference [4], (2) satellite metrics of greenness and climate variables through modelling approaches [5], and (3) process-based models integrated into Earth system models [6]. However, in all these methods uncertainties propagate through to the final GPP estimates, and for this reason a need for independent estimates via direct observation still remains. Improving our ability to monitor photosynthetic C uptake globally has become of major interest to climate and earth system modelers and to the remote sensing community, as this has direct implications for the development of climate mitigation scenarios or for determining targets in reduction of fossil fuel emissions (IPCC 2007). Currently, two main approaches exist to estimate photosynthesis directly from remotely sensed measurements. The first is based on measurements of the Photochemical Reflectance Index (PRI), a numerical index that uses the spectral reflectance values measured in a narrow detection band at 531 nm and in a reference band at 570 nm [7]. At short time scales (e.g., seconds, hours), variations in the PRI can be related to the amount of absorbed energy in the leaf that is directed to non-photochemical quenching (NPQ), a process by which plants under stress will dissipate excess absorbed energy as heat [8; 9; 10]. During that process, xanthophyll pigments in the chloroplast undergo reversible or sustained changes through de-epoxidation of violaxanthin into zeaxanthin, resulting in changes in leaf-level reflectance at 531 nm that can be related to canopy-level photosynthetic down-regulation. At longer time scales (e.g., days, months), variations in the PRI become increasingly related to changes in carotenoids and chlorophyll pigment pools [11; 12], which have also been related to canopy photosynthetic light-use efficiency (LUE) [13; 14]. The second approach for estimating photosynthesis from remotely sensed measurements is based on solar-induced chlorophyll fluorescence (SiF): The weak and near-instantaneous emission by chlorophyll molecules of redshifted light (i.e., lower energy relative to absorbed light) during photosynthesis [15]. In actively photosynthesizing plants, the amount SiF changes continuously in response to variations in environmental conditions. While SiF is very small compared to the amount of energy that is reflected, it is possible to indirectly measure SiF through the application of appropriate algorithms such as those based on Fraunhofer Line Discriminator, Spectral fitting, or indirectly by using spectral indices based on the reflectance measured at SiF-related wavelengths [16; 17; 18; 19; 20]. Remote sensing of SiF is complex and challenging; apart from fluctuating in response to environmental conditions, the small amount of SiF also depends on the leaf or canopy chlorophyll content and on the fraction of energy that is directed towards NPQ relative to SiF and photochemistry [15; 21; 22].