# Status of Phenological Research Using Sentinel-2 Data: A Review
## 4 Phenology of Grasslands
### Matching Sentinel-2 with Phenocam Data in Grasslands  
In contrast to satellites, phenocams provide close-range remotely sensed imagery at much higher spatial and temporal resolutions, and do not require atmospheric corrections [114]. Though phenocams currently typically operate in only a few spectral channels (e.g., three visible and one NIR band), they have been successfully used across a wide range of ecosystems, and are reported to provide better agreement with GP observations, more robust estimates of productivity, and an ability to track changes at the canopy level [115; 116; 117; 118; 119]. While their field of view is considerably reduced compared to satellites, multiple phenocam devices have emerged in the last decade, and have been used effectively to track vegetation changes. As with the satellite data, some pre-processing of the time series data is necessary before calculation of the phenological metrics, but gap-filling due to data loss is rarely required, unless the instrument fails, due to the high density of image acquisition [20]. It is important to acknowledge that the images acquired by satellites and phenocams cannot be directly compared with each other due to intrinsic differences in their sensors, fields of view and data format; however the phenological indicators derived from each can be equivalent. [PERSON] et al. (2020) [32] highlighted that, for phenology related research, a gap remains between observations made in the field and those acquired through satellites. Their proposed solution lies in integrating data from phenocams and satellites to minimise this scale gap. The authors used field measurements of soil moisture and meteorological conditions along with phenocam and Sentinel-2 data to study phenology and its relation to the hydrological behaviour of grasslands in an oak-grass savanna system. The green chromatic coordinate (GCC), computed using the red, green and blue bands, though not a direct measurement of chlorophyll content, has been shown to minimise the effect of light on images captured by cameras and to quantify the greenness as captured by phenocams. A high correlation (greater than 0.7) was obtained between the grassland's phenological metrics (i.e., start of season (SOS), end of season (EOS) and peak of season (POS)), calculated from phenocam-based GCC and vegetation indices (VIs) from Sentinel-2, such as NDVI, EVI, SAVI and MERIS terrestrial chlorophyll index (MTCI). It was found that all VIs could estimate EOS with greater reliability than POS, with SOS estimates delayed by around 10 days for both Sentinel-2-based GCC and VIs as compared to that from phenocam-based GCC. Finally, the authors concluded that despite the additional information contained within more sophisticated VIs, NDVI was best suited for such analyses and its values most closely matched those obtained from the terrestrial photography GCC. However, no explanation was given regarding the reasons behind such improved performance of NDVI over other indices. [PERSON] et al. (2019) [120] found a closer correspondence of Sentinel-2-based SOS for grasslands with phenocams in comparison to the one from Landsat-8. The authors found a 50% increase in the frequency of clear observations when using Sentinel-2 versus images available from Landsat-8 during days 90-120, which represents the SOS based on ground observations. In areas with high cloud cover, three to five cloud-free Sentinel-2 images were available as compared to one to two clear images from Landsat-8. In fact, Landsat-8 data were found to be unable to detect annual curves due to a lack of images through the growing season, and hence this sensor alone would be insufficient to extract seasonality for many pixels in the central USA. Within the Sentinel series, [PERSON] et al. (2019) [121] found that the SOS and EOS estimated from the optical Sentinel-2 sensors were closer to those estimated from phenocams and were only delayed by 4 and 10 days, respectively.