# Status of Phenological Research Using Sentinel-2 Data: A Review
## 2 Phenology of Forests  
### Integration of Sentinel-2 Data with Other Imagery  
The Sentinel-2 pair of sensors are becoming increasingly popular for phenological studies of forests due to their enhanced spatial and temporal resolutions that have helped overcome some of the limitations of optical remote sensing. [PERSON] et al. (2017) [60] reported a Sentinel-2-based normalised difference vegetation index (NDVI) to be better correlated to ground-based sensors, as compared to NDVI estimated from MODIS for which green-up dates lagged by 8-14 days relative to Sentinel-2 and in situ measurements. Senescence as calculated from MODIS data also occurred 4-11 days earlier relative to Sentinel-2 and in situ data, with these variations attributed to the mixed pixel effects of the low resolution MODIS images. [PERSON] et al. (2020) [42] used harmonized Landsat-8 and Sentinel-2 (HLS) data to calculate LSP metrics at a spatial resolution of 30 m and temporal resolution of 1-4 days using a \"multi-sensor land surface phenology (MS-LSP)\" algorithm. This product showed strong correlations (r = 0.9) with phenocam data for 50% green-up dates for deciduous broadleaf forests, whereas very weak correlations (r = 0.15) were found for evergreen needle leaf forests. As is the case for other satellite sensors, it was a challenge to estimate evergreen forest phenology due to its very small seasonal amplitudes. Nevertheless, HLS data were able to capture seasonal variations in vegetation phenology at finer spatial resolutions than was possible previously. By contrast, [PERSON] et al. (2020) [61] found that the HLS data still contained insufficient observations for some locations as a result of the irregular temporal sampling of data and gaps due to the presence of snow and clouds in the field of view. Most of the gaps in the data were found to occur in spring (for forests), which limits the ability to detect the early stages of the growing season. The authors filled the gaps in the 30 m HLS with data from the much coarser resolution VIIRS (500 m) using a shape-matching technique, and were able to improve the detail within the time series. The green up, or start of season (SOS), dates estimated from the VIIRS-HLS fused data had a closer agreement, i.e., mean absolute deviation (MAD), of 8 days with the in situ USA-NPN dates than with HLS (MAD of 14 days). [PERSON] et al. (2018) [62] also pointed out that NDVI, SWIR, blue, green and NIR bands in Sentinel-2 and Landsat-8 are highly consistent  
Figure 1: Frequency of publications (obtained from the Scopus database) mentioning “Phenology” and “Sentinel-2” in their abstract, title or keywords are shown as “Total” in black. The subset of the “Total” publications that includes “normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI)” and “Red-edge or short wave infra-red (SWIR)” are shown in red and blue, respectively. The year 2020 includes papers published up to the month of August 2020. (R\({}^{2}>0.90\)) in HLS data. A review of the literature on forest mapping reveals that such studies focused on the use of multi-temporal images representing different periods within the growing season to map broad forest types and their seasonality, rather than focusing on specific phenological stages [63; 64; 65; 66; 67]. The general principle is to use multi-temporal images to increase separability between forest types through temporal variations in seasonal growth curves or the time series curve geometry, as mentioned in [PERSON] et al. (2014) [26]. It is however essential to evaluate and normalise the differences that exist between different sensors, and even between the same sensor, when images are acquired under different viewing and illumination conditions, as is often the case when generating a dense time series of data. This effect can be much greater between multiple Sentinel-2 images than may be seen between Landsat images, due to the larger swath and viewing angle of S2, which permits the more frequent viewing of the same point on the ground surface [68].