## 1 Introduction  
Terrestrial ecosystems store approximately 283 to 827 GtC [1; 2; 3] and play a major role in the exchange of carbon with the atmosphere [4], hence monitoring and understanding forest productivity are of vital importance to understanding global carbon cycles. A principle variable in the study of land-atmosphere interactions is the gross primary productivity (GPP), which describes the amount of CO\({}_{2}\) sequestered by vegetation per unit area per unit time, before any losses due to growth and maintenance respiration are accounted for. GPP can be modeled following Monteith [5; 6] as the product of incident or absorbed photosynthetically active radiation (PAR) and the light-use efficiency (LUE) which in itself is a function of a wide range of environmental constraints, including temperature, relative humidity, soil moisture, and hydraulic conductivity [7; 8]. Over past decennia, a large number of researchers have investigated the dependency of LUE on environmental drivers using both ground-based monitoring networks, as well as remote-sensing techniques, including orbital and airborne imaging. A significant landmark in the estimation of forest productivity at the stand-level is the eddy-covariance technique that is used to measure fluxes of CO\({}_{2}\) between ecosystem and atmosphere at approximately 10 ha footprints by measuring high-frequency wind-vector components and CO\({}_{2}\)-mixing ratios using sonic anemometers and gas analyzers [9], from which estimates of LUE can be derived. To date, a network of over 500 permanent eddy-covariance installations covers a wide range of different biomes and is used to create dense time series of productivity estimates across naturally occurring environmental gradients. Applicable remote sensing techniques have combined the use of spectral and structural information to assess changes in LUE. For example, [PERSON] _et al._[10] have demonstrated the use of the Photochemical Reflectance Index (PRI) to explain changes in LUE and attributed these changes to the xanthophyll cycle: a series of carotenoid pigment interconversions that serve to dissipate excess light and protect leaves from photo damage [11]. Most of these measurements were taken at the leaf level and with direct sun illumination. [PERSON] _et al._[12] demonstrated the use of shadow fraction to correct PRI observations for bidirectional reflectance effects, enabling stand-level changes in eddy-covariance-derived LUE to be explained with \(R^{2}\) between 0.6 and 0.8, depending on the range of forest types considered. A recent literature review by [PERSON] _et al._[13] showed that PRI accounted for 42% of variation in LUE for a wide range of vegetation types considered by including results from over 80 peer-reviewed publications. Remote sensing studies on interactions between vegetation structure and physiological functioning are, however, limited both in spatial and temporal resolutions and data aggregation over space or time may result in poor understanding of non-linear processes, such as photosynthetic down-regulation; Hence, there is a need to pair airborne and orbital remote sensing observations with dense time-series _in situ_ measurements [13]. Recent trends in consumer electronics and engineering design have provided for the ease of building sensor networks that can be deployed to measure the state and dynamics of our environment _in situ_. With a wide range of consumer electronics produced at incrementally lower cost, new research potential is anticipated to investigate non-linear relationships between vegetation functions and structure and to support and extend laboratory-gained knowledge to the field. [PERSON] _et al._[14] have introduced and demonstrated a low-cost, narrow-band instrument for measuring PRI at the leaf level and discuss the potential to monitor LUE through deployment of networks of sensors. More recently, [PERSON] _et al._[15] used data from a narrow-waveband instrument manufactured by Skye Instruments (Llandrinodd Wells, UK).