# Status of Phenological Research Using Sentinel-2 Data: A Review
## 6 Current Developments in Using Sentinel-2 for Phenological Research
### Gap Filling Techniques for Phenological Research Using Sentinel-2  
The issue of cloud cover that limits the number of usable optical images is a well-known challenge for phenology studies and affects all land covers. Studies have used different methods to overcome the limitations of frequent and/or long gaps (caused by cloud cover and snow) in optical time series data. Most notable among the gap-filling methods are the use of temporal information only or seasonal means [30; 145; 146], use of both spatial and temporal information [147; 148], and other techniques in the temporal domain, such as Fourier transformation or harmonic analysis of time series data [149; 150]. Data gaps in one sensor time series have also been filled using data from other sensors; for example Sentinel-2 series have been used in combination with Landsat 8 to produce a denser time series [61; 70]. [PERSON] et al. (2018) [21] analysed a time series of Sentinel-2 NDVI data and concluded that both the frequency and the timing of data gaps resulting from cloud cover are important in ensuring correct estimation of LSP metrics. For example, the presence of above-normal cloud cover in the spring season in slow-growing salt marshes of the Netherlands had less impact on the estimated LSP as compared to a rapidly greening-up agricultural plot. [PERSON] et al. (2018) [65] modelled time series NDVI data using a double logistic function and filled temporal regions with long gaps using a \"shape prior\", or in simple terms the averaged climatological shape (analogous to seasonal means of a time series). The authors reported an improvement in the SOS and EOS calculated from the use of shape priors for filling gaps in time series; this reduced the root mean square error (RMSE) between Sentinel-2 and daily MODIS-based LSP by more than half. [PERSON] et al. (2017) [57] reiterated the high consistency between Sentinel-2 and Landsat-8 bands with respect to their spatial and temporal characteristics, which could be used to map crops at higher resolutions. They found the NDVI and normalised difference water index (NDWI) pairs (from S2 and L-8) to be significantly correlated with each other, with negligible bias and systematic errors in geometric positioning of pixels. Differences observed between the indices were primarily due to the temporal mismatch in image acquisition, but those could be managed using a linear bias correction. This study thus revealed the immense scope in the conjunctive use of Sentinel-2 and Landsat-8 data to increase the temporal sampling of data and overcome the issue of gaps in optical time series data for phenological studies. In another study, [PERSON] et al. (2018) [151] found that there were insufficient cloud-free images from the high spatial resolution Sentinel-2 sensors to identify the presence of cotton rot, and while there were enough clear MODIS images, its resolution of 250 m was inadequate. They fused both datasets to overcome their individual shortcomings and were able to simulate high fidelity Sentinel-2 data at much higher temporal frequencies. It was observed that the phenological curves of healthy and infected cotton differed significantly, and the value of peak NDVI, productivity, SOS and length of season were all reduced in the rot-affected cotton crop. [PERSON] et al. (2020) [152] used a non-parametric regression technique to extract spatio-temporal phenological information from crop fields using Sentinel-2 NDVI to detect fallow lands and cropped pixels. In this study, accuracies in resulting maps were partly attributable to the higher temporal resolution that aided in obtaining multiple cloud-free images per month. Additionally, data from multiple sensors, such as VIIRS, Landsat and Sentinel-2, have been successfully used to fill gaps in data from the use of single sensors [53; 61; 153]. By contrast, temporal compositing of data covering key stages of crop growth has also been suggested to mitigate the need for very high frequency time series data to distinguish crops [92].