# Status of Phenological Research Using Sentinel-2 Data: A Review
## 4 Phenology of Grasslands  
Monitoring Seasonal Change in Grassland to Determine Management Practices, Invasion and Biomass Production  
Grasslands provide valuable ecosystem services, and account for more than a third of all agricultural land on earth [55,109]. Characterising phenology of grasslands is particularly challenging since they are sensitive to both management practices (i.e., frequent grazing and cutting events) and weather [110]. [PERSON] (2013) [55] noted that frequent observations are needed to capture changesassociated with management practices in grasslands, such as grazing and mowing. In his study, undertaken before Sentinel-2 was launched, Hyperion data were used to simulate observations from Sentinel-2, MODIS and VIIRS. Various vegetation indices and bands were used to study pigments (NDVI, carotenoid reflectance index (CRI), anthocyanin reflectance index (ARI), red-green ratio (RGR)), water content (normalised difference infra-red index (NDII)), soil and senescence (SWIR, PSRI) and biomass (soil adjusted total vegetation index (SATVI)). This study revealed that most of the simulated Sentinel-2 indices except ARI were highly correlated to MODIS and VIIRS with strong linear relationships. VIIRS and MODIS both lack a band at 700 nm that is critical for calculating ARI, and hence this had to be substituted with the closest available band, which might explain its poor correlation with Sentinel-2-based ARI data. The vegetation indices were used to predict grassland cover and nominal vegetation status (with respect to disturbances, i.e., degraded or pristine grasslands). The Sentinel-2 constellation, with its high spatial and temporal resolutions, provides optimum conditions for detecting and quantifying mowing events, although its utility can be impeded by cloud cover given how quickly the grass regrows after mowing and also that long gaps in data can lead to failure in detecting disturbances. To overcome this problem, [PERSON] et al. (2020) [PERSON] et al. (2020) presented a multi-sensor approach by using harmonised Landsat and Sentinel (HLS) data to map the frequency and timing of mowing events, with the underlying assumption that vegetation growth follows a climate-driven phenological trajectory and large deviations from the normal trajectory are a result of removal of biomass. They used NDVI data derived from 10-day HLS composites for detecting up to five mowing events per season. On evaluating the detection algorithm, they found that the HLS data allow for identification of mowing frequency and the timing of the detected mowing event with a high degree of inter-parcel variability. Pixels with only one mowing event were reported to have a higher probability of omission errors, since the application of cloud masks could potentially filter out valid observations in the time series data. HLS data were also used by [PERSON] et al. (2020) [PERSON] et al. (2020) to map invasive annual grass species, which can be a major contributor to ecosystem degradation, and, therefore, it is imperative to monitor and map them for better management of the environment. Previously, invasive species were studied using MODIS and AVHRR to quantify their spatio-temporal variations (Pixels, 2020); however, the spatial resolutions of these sensors posed a drawback. The results showed better agreements for phenological metrics calculated from the expedited weekly MODIS (eMODIS) and integrated HLS data (with correlations of 0.55, 0.81 and 0.91 for SOS, peak of season and EOS timings, respectively). Furthermore, this study showed that the regression tree model using both spatio-temporal dynamics was better (correlation of 0.91 with eMODIS) than the strictly temporal information-based per-pixel approach (correlation of 0.87 with eMODIS) in constructing time series data. The invasive species maps were obtained at an enhanced resolution of 30 m, as compared to the much coarser resolution of 250 m for eMODIS data, and are better suited for decision makers on the ground.