# Status of Phenological Research Using Sentinel-2 Data: A Review
## 3 Phenology of Croplands
### Estimation of Crop Yield  
In addition to crop mapping, Sentinel-2 data have also been successfully adapted to predict crop development and yield. A study by [PERSON] et al. (2020) [60] highlighted the advantage offered by the high temporal resolution of Sentinel-1 and -2 that makes them particularly suitable for \"in-season crop forecasting\", as the different phenological events can be reliably captured. In this study, Sentinel-1 data in the VV polarisation mode were used for the analysis since this mode was established to be sensitive to the change of canopy due to the flowering of rape seed. Additionally, it was hypothesised that the normalised difference yellow index (NDYI) based on the blue and green bands was suitable for capturing the increasing yellowness during flowering due to strong absorption in the blue band by the flowers. The high reflectance in the green and red bands is then perceived as yellow (the colour of the flowers). This study revealed that the in situ data pertaining to the onset and end of flowering match with those captured by the Sentinel-1 radar backscatter in the VV mode. As regards to Sentinel-2, the in situ peak flowering date matched with the average peak of the NDVI. It is worthwhile to note that the peak flowering date as captured by Sentinel-1 was delayed by 5 days compared to that captured by Sentinel-2 due to the attenuation of the C band backscatter signal by the flowers. However, a strong correlation was found between the peaks as detected by both Sentinel-1 and Sentinel-2.  
From a review of crop phenology studies based on optical remote sensing data, [PERSON] et al. (2015) [28] envisaged that Sentinel-2 has the potential to mitigate the limitations of existing optical sensors for crop yield estimates. Small farms, which are challenging to study with lower spatial resolution imagery due to the presence of multiple land cover types within a pixel, can now be monitored at the Sentinel-2 scale and with much greater confidence. [PERSON] and [PERSON] (2019) [90] used hyperspectral ground reflectance data along with Landsat-8, Quickbird, Rapid Eye, World View-2 and Sentinel-2 to estimate grain yield and grain nitrogen uptake. It was observed that Sentinel-2 with its narrower bands performed the best for both objectives. The NIR band was found to be best for studying grain yields, while the RE bands were highly correlated to nitrogen-related traits of plants. In a similar study, [PERSON] et al. (2019) [105] studied yield prediction and land suitability for the cassava crop in Indonesia, revealing that maximum correlation can be found between vegetation indices and green-up, and that remote Sentinel-2 data also helped to predict yields up to five months in advance of the harvest season. This study also showed that the yield was highly correlated with vegetation indices such as NDVI, soil-adjusted vegetation index (SAVI) and the inverted red-edge chlorophyll index (IRECI), and to the leaf area index (LAI) and fraction of photosynthetically active radiation (fAPAR). It was reported that the average yield of cassava was approximately 12 and 8 t/ha in the highly and moderately suitable areas for cultivation, respectively. While mapping winter wheat and barley using Sentinel-2 data, [PERSON] et al. (2018) [106] flagged the issue of mixed pixels that arises with low and medium resolution optical data. The authors showed that, for pixels of a single species, NDVI was highly correlated to above-ground biomass, and suggested a decision tree applied to the temporal profiles of NDVI could effectively discriminate wheat cropped areas and thus may help in monitoring crop yields, as mentioned in [107,108].