# Status of Phenological Research Using Sentinel-2 Data: A Review
## 1 Introduction
The different LSP metrics have been linked to various GP observations with varying degrees of success and are particularly sensitive to canopy level changes, such as leaf unfolding and defoliation, rather than to events such as bud-break [36]. Despite their limitations and a lack of direct correspondence to GP, various LSP metrics have nonetheless been able to provide valuable insights into vegetation seasonality and their various controls at much larger scales (both temporal and spatial) [29; 37; 38; 39; 40]. Although optical remote sensing has shown promising results in detecting phenology, many optical sensors pose several shortcomings in terms of spatial and temporal resolution. For example, the longest freely available optical dataset from the Landsat missions has been found to suffer from frequent cloud cover and scan line errors (for Landsat-7) that inhibit its utility to study land surface phenology (LSP) and needs integration with higher resolution datasets to overcome such challenges [41]. [PERSON] et al. (2018)[21] pointed out that, even with the higher spatial resolution and frequent revisit time, the data availability from Landsat-8 is limited due to its single orbit and is not fine enough to capture fast-occurring changes such as cropland green-up. With Landsat data being infrequent (with a repeat cycle of 16 days), requiring high computing resources to process and the challenge of filling gaps in Landsat-7, especially in cloudy conditions, other coarse resolution sensors, such as MODIS, AVHRR, SPOT-VEGFATION and MERIS, gained popularity since high quality datasets were readily available and easier to process [42; 43]. However, many LSP studies conducted using data from these sensors, although providing frequent observations, suffered from mixed pixels and were therefore limited in their applicability over heterogeneous landscapes [44; 45; 46]. Although mixed pixels are unavoidable, a careful handling of pixel heterogeneity may help in quantifying the variability in the estimated LSP [47; 48]. The combined datasets from different Landsat satellites (such as 7, 8 and potentially 9 once it is operational) could provide a nominal 8 day ground coverage [49], and are thus one of the best choices for sampling data at a higher spatial and temporal resolution. However, given the constraints of cost and image acquisition during the commercialisation of Landsat from 1984 to 2008 [43], the freely available, wide-swath coarser resolution sensors, such as AVHRR and MODIS, have been the focus of much interest in the phenological community. To overcome some of the limitations of these lower spatial resolution sensors, newer satellite data from constellations of identical sensors, such as Sentinel-2, RapidEye and Planet (with higher temporal and spatial resolution), could help improve both our estimates and understanding of LSP. The newest European Space Agency Sentinel-2 satellites, available from 2015 (S2A) with the second sensor (S2B) launched in 2017 to double the data acquisition, provide data at much finer spatial (10-20 m) and higher temporal (up to 5 days) resolutions, although currently over a shorter time series than Landsat and MODIS. The reader is referred to the Sentinel-2 mission website for detailed information on the specifications of its sensors [50]. The temporal resolution is one of the biggest advantages of S2 data, with improved sampling of the ground, and overlapping areas (for S2A and 2B orbits) achieving even higher temporal sampling, which is helpful especially in areas where cloud cover may be frequent [51]. It has been observed that, for the same area, Sentinel-2 can sample approximately ten times more pixels when compared to Landsat-8, owing to its better spatial resolution and higher revisit frequency from two sensors (up to four times improved temporal sampling of cloud-free observations), thereby providing improved capability for monitoring vegetation from space [52; 53].