## 1 Introduction  
Phenology is the study of recurring life cycle stages, and their timing and relationship with environmental factors [1; 2]. Phenology controls the seasonality of ecosystem functions and plant feedbacks to climate through diverse processes, such as changes in the surface albedo and the exchange of CO\({}_{2}\) between atmosphere and biosphere [3; 4; 5; 6]. Despite its importance, phenology is not always well described in Earth system models [7; 8; 9], in particular, the environmental factors controlling phenology are still uncertain [6; 10]. Therefore, additional efforts to monitor and model plant phenology are needed to improve the representation of phenology in Earth system models [6; 11]. Conventional visual monitoring of phenology dates back to 705 CE [12], and still plays an important role in evaluating the impacts of climate change on ecosystems [13; 14; 15; 16]. However, conventional monitoring requires substantial field work, which limits spatial and temporal representativeness [17]. Near-surface remote sensing is becoming a more frequently used tool to monitor vegetation phenology at the ecosystem scale. In recent years, installation of commercial digital cameras for phenology monitoring (i.e., PhenoCam) has proliferated throughout diverse biomes and continents [18; 19; 20; 21; 22; 23], which has led to the consolidation of national and continental monitoring networks [24; 25; 26; 27; 28]. The use of PhenoCam consistently reduces manual labor, guarantees time series of high temporal resolution, and creates a permanent data record from which visual interpretation and qualification can be made at any later point in time [29]. The proximity to the target ecosystem allows the cameras to track phenological transition dates (PTDs), such as leaf emergence, leaf discoloration, senescence, and green up and senescence of vegetation with high temporal resolution [23; 30], as well as monitor the different plant types within the camera's field of view (FOV; [24]). Nowadays, the increasing number of sites with digital cameras co-located with ecosystem-atmosphere CO\({}_{2}\) flux measurements collected using the eddy covariance (EC) technique are contributing to understanding the relationship between phenology of structure and function of ecosystems [28; 31; 32]. Green chromatic coordinates (GCC) is the most commonly used vegetation index (VI) extracted from PhenoCam, due to the requirement of only three visible spectral bands for computation, and it is used to represent plant development throughout the season [33]. PhenoCam, with an additional near-infrared (NIR) band which is more sensitive to vegetation structural change than visible bands, has increased the use of the PhenoCam-based normalized difference vegetation index (CamNDVI) for the same purpose [29; 34; 35]. Both GCC and CamNDVI are considered plausible indexes to bridge satellite and ground-based observations of phenology [24]. For instance, GCC and CamNDVI have been shown to be effective (though not always consistent) tools for describing greenness variation of individual plant species and ecosystems in a variety of plant functional types [29; 36], and for evaluating and linking remote sensing phenology products [18; 37; 38; 39] with ground observations [40]. Recently, [PERSON] et al. [41] introduced a new vegetation index called near-infrared reflectance of vegetation index (NIRv), designed to mitigate the mixed pixel problem (determining the fraction of vegetated land surface and reconstructing the signal attributable to vegetation) to better represent photosynthesis of ecosystems. A strong correlation between satellite-based NIRv and gross primary productivity (GPP) at global scale was observed, which outperforms the correlation between NDVI and GPP [40]. As such, it would be interesting to know if this new index could provide an advantage in tracking seasonal GPP and phenology compared to the widely used CamNDVI and GCC at ecosystem-scale.