# Status of Phenological Research Using Sentinel-2 Data: A Review
## 6 Current Developments in Using Sentinel-2 for Phenological Research  
### Performance of Sentinel-2 Red-Edge Bands in Phenological Research  
In addition to better temporal and spatial resolutions, the red-edge bands on the Sentinel-2 sensors have been considered beneficial in studying urban vegetation phenology by [PERSON] et al. (2020) [48]. The authors showed that, irrespective of the processing methods (double logistic function fitting or Savitzky-Golay smoothing of the time series), the normalised difference red edge (NDRE)-based time series (as compared to NDVI and normalised burn ratio (NBR)) shows greater stability (i.e., smaller standard deviations for pure vegetation pixels at a site) at the start of season and the peak of season. From a review of publications, it has emerged that most of the studies evaluated the temporal trajectory of red-edge bands for detecting plant pigments, discriminating different vegetation classes and estimating productivity. The lack of studies using red-edge bands to estimate different LSP metrics (i.e., SOS, EOS and POS, etc.) is quite evident. The effectiveness of using Sentinel-2 red-edge bands has been tested in various applications, including detection of chlorophyll, tree species classification and biomass estimations in grasslands and crops [128; 129; 130]. The red-edge bands in Sentinel-2 were found to be comparable to MERIS and RapidEye, but band 6 (733-748 nm) is unique since it is situated at the longest wavelengths of the red-edge where the gradient remains high (the comparable band 10 of MERIS at 750-757.5 nm is situated slightly beyond the steep high gradient region). The Sentinel-2 bands 5 and 6 provide improved estimates of red-edge over the other sensors due to their optimal bandwidths and better spatial resolution [54]. The red-edge information from the MERIS based MTCI (operational from 2002-2012) was reported to have a robust relationship with gross primary productivity (GPP), but the data were limited by the coarse spatial resolutions of 300 m [131]. [PERSON] et al. (2019) [33] also highlighted the suitability of red-edge-based vegetation indices from Sentinel-2 over the conventional non-red-edge indices (i.e., NDVI, EVI and the NIR reflectance of terrestrial vegetation (NIR\({}_{\text{v}}\))). In this study chlorophyll red-edge and chlorophyll green indices, both based on red-edge bands, showed high correspondence (coefficient of determination ranging from 0.69 to 0.89) with in situ estimates of grassland GPP. However, it was found that the light use efficiency (LUE)-based GPP from MODIS rather than the red-edge-based vegetation indices (VIs) from Sentinel-2 were better suited for forested landscapes. The authors explained that GPP in forests is mainly driven by incident photosynthetically active radiation (PAR) rather than the chlorophyll content as detected by Sentinel-2 red-edge bands. In a study by [PERSON] et al. (2019) [132], the red-edge bands 5 (705 nm) and 6 (740 nm) on Sentinel-2 were also reported to capture differences in the density of tree canopies. Those red-edge bands in the wet season were particularly important to distinguish bushy grasslands from broadleaf scrubs. Using pre-operational Sentinel-2 data, [PERSON] et al. (2016) [51] showed that crop lands and tree species could be classified with high accuracy. From a random forest classification model, the red-edge (band 5) and SWIR (band 11 at 1610 nm) were found to be the two most important variables in comparison to other Sentinel-2 bands. [PERSON] et al. (2011) [58] used a simulated Sentinel band 4 (665 nm) in combination with the red-edge bands 5, 6 and 7 (705, 740 and 783 nm, respectively) from hyperspectral data to estimate chlorophyll content with improved accuracy. Additionally, this study showed that bands 4 and 5 can be used to calculate green LAI for crops.