# Status of Phenological Research Using Sentinel-2 Data: A Review
## 1 Introduction  
Accurate monitoring of vegetation phenology across space and time is imperative for understanding the impact of climate change on plants and animals. Many studies have reported the influence of climate on phenological events, and monitoring of the timing of these events is therefore considered one of the important indicators to study the impacts of climate change [1; 2]. Since different species react to changes in climate differently, shifts in the timing of phenological events have the ability to desynchronize species interactions [3]. Phenology also influences ecosystem productivity, carbon cycling, succession, and migration strategies [4; 5; 6; 7]. In addition, it has been classified as one of the essential biodiversity variables (EBV) and addresses goals 13 (climate action) and 15 (life on land) of the Sustainable Development Goals (SDG), which makes the study of phenological patterns and drivers crucial to plan adaptation and mitigation strategies [8; 9]. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important evidences of trends in plant phenology [2; 10; 11; 12; 13; 14]. Such has been the interest in the field that a two-fold increase has been observedin the published literature on the topic of plant phenology in the last decade (based on Scopus database search results). Traditional approaches to monitoring phenology, also referred to as ground phenology (GP), involve recurring visual observations of phenological events by trained personnel and can be expensive and time consuming. GP of individual plants is recorded in the form of day-of-year observations of breaking of leaf buds, expansion of leaves, appearance of flowers, leaf senescence etc. [15; 16]. In addition to professional networks, many citizen science networks exist across the world that strive to provide high quality observations of phenology on a voluntary basis [17; 18]. The latest developments in technology allow fixed digital cameras (phenocams) to capture several images of vegetation on a daily basis to monitor plant growth from a close range [19; 20]. Although they work on the same principle as satellite observations of phenology, their spatial coverage is, however, limited, and simultaneous monitoring of vegetation phenology across large areas can only be achieved through satellites [21]. Due to the requirement of repeated observations to study land surface phenology (LSP), most satellite-based phenology studies have relied on medium to coarse spatial resolution satellite sensors, such as MODIS, Landsat and AVHRR, or on fusing MODIS and Landsat imagery to improve the temporal and spatial resolution [22; 23; 24; 25]. In contrast to GP observations, the phenophases derived from satellite imagery or LSP represents the timings of the changes observed in the reflectance from the vegetated features aggregated at the pixel scale of the sensor [26; 27]. The phenological information has been modelled (satellite reflectance modelled as function of time, temperature or relative humidity, etc.) and extracted (thresholds, derivatives and moving averages) from time series of satellite reflectance data in various ways, and the resulting phenological metrics are known to be very sensitive to the choice of algorithm [26; 28; 29; 30; 31]. Apart from using the original reflectance values, information from more than one spectral band is often combined to enhance the vegetation properties within a satellite pixel. A list of frequently cited vegetation indices and their algorithms can be found in [32; 33; 34]. LSP metrics are typically estimated as the day of the year corresponding to the start of season (SOS), end of season (EOS) and peak of season (POS), when particular values of thresholds (or rates of change) on the ascending and descending portions, and the peak values on the reflectance or vegetation index (VI) time series are respectively reached [30; 35].