# Status of Phenological Research Using Sentinel-2 Data: A Review
## 2 Phenology of Forests
### Vegetation Indices for Phenological Research in Woody Species  
The use of phenological information in the form of time series of vegetation indices has been successfully implemented by [PERSON] et al. (2019) [67]. The authors used monthly Sentinel-2 NDVI data and random forest models with hierarchical logic to discriminate coastal vegetation into dense and sparse vegetation of herbaceous and woody types. The trajectory of the NDVI values were found to possess critical phenological information at key stages that helped distinguish vegetation classes. The variability of NDVI values during the year, particularly its annual mean and its value in summer, helped discriminate dense woody vegetation, such as forests and shrubs (which have a high NDVI through the year), from other vegetation classes that experience greater changes in their greenness (or NDVI) through the year. Nevertheless, although NDVI has been reported to be highly correlated with vegetation greenness and biomass, it performs poorly in identifying parts of the canopy, such as flowers, which are mostly non-green [73; 74]. This issue was addressed by [PERSON] et al. (2019) [73], who tested NDVI and a bloom index based on the visible bands to identify flowering in almond trees. They used multiscale time series data from sensors including a unmanned aerial vehicle (UAV), aerial photographs from ceres imaging, and satellite images from PlanetScope, Sentinel-2 and Landsat-8 with spatial resolutions ranging from 2 cm (UAV) to 30 m (Landsat-8). This study took advantage of the fact that the reflectance of the almond flowers was much higher in the visible spectrum (especially in the red and blue bands) in comparison to green leaves and soil. The authors developed an enhanced bloom index (EBI) from the red, green and blue bands to temporally characterize the flowering of plants including the start, peak and end of blooming, which was completely missed by the NDVI. The higher spatial resolution Sentinel-2-based EBI could identify flowers with more than twice the accuracy of Landsat-8 and had the highest correlation (r = 0.69) with the very high resolution ceres data (although this was available for only a single date). PlanetScope and Sentinel-2 were both found to be spatially and temporally suitable to capture the trajectory of almond flower blooming and its critical stages through the use of EBI. Although it is not surprising that the very high-resolution ceres dataset provides the maximum capacity in detecting blooming signals early in the season, it would be interesting to see if the flowers and other parts of the vegetation can be unmixed from coarser resolution data as mentioned in [PERSON] et al. (2020) [75], who reported the capability of the visible bands and red-edge bands in discriminating the photosynthetic and non-photosynthetic fractions. Several studies have used spectral mixture analyses to identify photosynthetic and non-photosynthetic fractions [76; 77; 78] and vegetation indices to estimate concentration of plant pigments [79; 80; 81]. However, similar efforts are lacking to discriminate flowers from other parts of the plant and existing techniques have been applied mostly to high-resolution aerial or hyperspectral data [82; 83; 84; 85; 86]. Notably, the EBI developed by [PERSON] et al. (2019) [73] was found to perform poorly in discriminating non-white flowers, and it may be that other band combinations are more suitable for different coloured petals. Foliar nitrogen (N) varies between forest phenophases [87; 88] and has been previously estimated using expensive hyperspectral sensors and commercially available multispectral images at high spatial resolution. With the advent of Sentinel-2, it is now possible to compare N estimation accuracies across different phenological stages much more easily and cheaply. [PERSON] et al.