## 1 Introduction  
Phenology is defined as the \"study of the timing of recurring biological events, the causes of their timing with regard to biotic and abiotic forces, and the interrelation among phases of the same or different species\" [1]. Vegetation phenology specifically addresses processes linked to the plant cycle, such as leaf emergence, flowering, leaf colouration and fall [2]. These are controlled by molecular mechanisms inside the organism and driven by factors such as temperature and photoperiod [3]. The study of plant phenology through on-field observations, although a reliable approach [4], is time-consuming and costly when applied at large spatial scales or over long time series. Moreover, observed phenological data are site specific, often sparsely distributed and measured for a few plant species only and at discrete phenophases [5]. To overcome these issues, the collection of data and information on phenology through remote sensing devices and methodologies has become of great importance over the years as a result of scientific advances in this field of study. Within remote sensing, different types of technologies can be considered for phenological analysis, such as satellites equipped with specific sensors [6; 7; 8] or Phenocam digital cameras [9; 10; 11; 12]. Taking into account satellite images and the different vegetation indices derived from them [13], the use of these data has been widely experimented in the literature in different environments, such as evergreen forests, deciduous forests, croplands and grasslands [14; 15], to assess the vegetation cycles through the extraction of phenological dates of the start, peak or end of the growing season. Approaches to retrieving these relevant phenological dates from vegetation indices have been previously investigated with regard to the smoothing and filtering functions of raw satellite data [16] and date extraction techniques [15]. Furthermore, there has been the development of specific software, such as Phenopix [17], that simplified and automated data processing techniques for phenological studies. In addition to data collected from on-field and remote sensing observations, another important source of information for studying phenology is the gross primary productivity (GPP), i.e., the total amount of CO\({}_{2}\) fixed by plants through vegetation photosynthesis [18; 19; 20], elaborated from eddy covariance measurements at flux tower sites. Since phenology is one of the most important controls of the interannual variability of GPP [21; 22], these measurements have been used in a number of studies as a proxy for vegetation phenology [15; 23]. Unlike phenological on-field observations that are based on the human eye, GPP is focused on photosynthetic phenology, which is a result of both plant canopy development and light use efficiency [24]. By measuring the photosynthetic carbon uptake of the vegetation canopy, dates of start and end of season elaborated from GPP measurements provide indications of when ecosystems switch from a source to a sink of C, and vice versa [25]. Given the importance of these data, GPP measurements are also used to evaluate the efficiency of remote sensing data in estimating ecosystem phenological dates [26; 27]. In the last decades, the study of phenological events in plant communities has become increasingly important to assess the impacts of climate warming on vegetation cycles, with consequences on agricultural, forest and grassland systems [28; 29]. With a specific focus on the latter, monitoring grassland ecosystems, which cover ca. 70% of the total world agricultural area [30], has gained interest due to the large amount of ecosystem services they provide, e.g., erosion protection, water regulation, carbon storage, biodiversity, food for animal production systems and wildlife, aesthetic and recreational functions [31; 32; 33; 34].