## 1 Introduction
The PRI was developed from leaf-scale measurements, however, an increasing number of studies have demonstrated the sensitivity of PRI to photosynthetic activity at the canopy and ecosystem scale across different vegetation types [21; 22; 23]. [PERSON] et al. [24] found that the PRI served as a useful indicator of LUE specifically during spring transitions in boreal forest canopies. The Moderate Resolution Imaging Spectrometer (MODIS) satellite sensor has commonly been applied in PRI studies [25; 26; 27; 28; 29; 30; 31; 32]. Although MODIS is not specifically designed for the purpose, it has several narrow ocean bands (10 nm), including a band centered at 531 nm, and a high temporal resolution (1-2 days revisit time globally), which makes the sensor suitable for time series studies of photosynthetic activity and LUE. MODIS-based studies use different versions of the PRI (i.e., different reference bands) since the sensor does not include a band centered at 570 nm, identified as the most useful reference wavelength at the leaf scale by [PERSON] et al. [17]. The optimal wavelength bands vary from site to site [27; 30; 33], which highlights some well-known limitations of using PRI as well as other VIs. When applied at larger scales, the VI signal is affected by various confounding effects, which are not related to the canopy itself [34]. These are the result of bidirectional reflectance distribution function effects caused by varying viewing and illumination geometry [22], canopy structure and background effects (e.g., soil color and shadows) [30] and changing atmospheric conditions [34]. These include compositing methods such as the CV-MVC algorithm used to generate global MODIS NDVI products [35] and temporal smoothing of the data points by applying filters or fitting functions [36; 37; 38]. A new study by [PERSON] et al. [39] shows that these confounding factors can be largely ameliorated by use of standardized geometry and processing approaches. Snow and cloud cover is particularly problematic when observing phenology in boreal regions as the noise arising as a result tends to decrease the VI signal significantly and consequently reduce the temporal resolution of the VI time series. A wide variety of strategies have been developed to minimize the impacts of noise and improve the stability of the time series  
Recent work by [PERSON] and [PERSON] [14] showed that changes in PRI and pigment pool sizes were closely timed with spring photosynthetic activation in conifers measured by gas exchange on the ground. The study was carried out at the leaf and stand scales and the authors encourage further work using MODIS PRI to demonstrate these relationships at larger scales and at multiple sites. The application of MODIS in PRI studies has been hindered by the fact that the narrow bands used are designed for ocean color detection and are therefore not included in standard processing over land regions. [PERSON] et al. [32] and [PERSON] et al. [33] highlight this lack of processed and quality controlled MODIS ocean band data over terrestrial regions as a major hindrance to long-term studies of PRI. However, recently NASA has processed a large dataset, which consists of 16 years of gridded daily surface reflectances in band 1-12 (including the 531 nm band) for North America, South America and Europe. The dataset is atmospherically corrected using a multi-angle implementation of atmospheric correction (MAIAC), which uses time-series and image-based processing to derive aerosol optical thickness and surface reflectance [40; 41]. In comparison with a standard atmospheric correction (65), [PERSON] et al. [42] found that MAIAC-processed data markedly improved the MODIS signal in a PRI-based study of vegetation LUE. The MAIAC dataset thus presents new opportunities for testing the applicability of PRI as an optical indicator of phenological events and exploring long-term trends in vegetation productivity in evergreen ecosystems.