# Status of Phenological Research Using Sentinel-2 Data: A Review
## 3 Phenology of Croplands
### Mapping of Crops Using Time Series Data  
Several crop mapping studies used phenological information in the process of discriminating crop types. Such studies worked on the logic that the spectral response of different crops might be similar on any single day but the separability among crop types is greatly improved in a multi-temporal space [91]. This fact has been utilized by many studies for mapping crop types using Sentinel-2 data. [PERSON] et al. (2019) [92] used Landsat 7-8 and Sentinel-2 images to increase the frequency of data for mapping winter crops. They reported that temporal compositing of minimum and maximum NDVI values at the peak and end of season could mitigate the need for a longer time series of data to discriminate winter crops from forests and spring crops. The improved spatial and temporal resolution of the combined Landsat and Sentinel-2 series successfully identified the winter crops in 96% of the pixels, in comparison to 82% when using MODIS data. Another issue with crop mapping in fragmented landscapes was highlighted by [PERSON] et al. (2020) [93]. Though the models in the study were able to explain 89% of the variation in crop census data, they revealed that Landsat NDVI could not distinguish between cropland and grassland at its native 30-meter spatial resolution. The authors therefore proposed using red-edge bands from Sentinel-2 and the wet season radar backscatter from Sentinel-1 since they have the potential to distinguish crops. Studies [94; 95] have reported the applicability of vegetation indices, such as enhanced vegetation index (EVI), NDVI and land surface water index (LSWI), from the high spatial resolution Sentinel-2 time series data in identifying rice cropped areas with high accuracy. Inclusion of phenological metrics, such as the SOS and EOS dates, in the time series curves from a combined Landsat-7 and 8 and Sentinel-2 dataset further increased the mapping accuracy of rice crop from 78% to 93% [95]. Multi-sensor data from Sentinel-2, Landsat and MODIS were also used in combination by [PERSON] et al. (2018) [96] for a hierarchical classification of groups of crops. The authors reported an increase of almost 40% in the data availability since 2015 (coinciding with the launch of Sentinel-2A). This study revealed that the EVI curvature parameters including the rates of green-up and senescence are the most important predictors in the crop type models, and hence are most affected by the temporal sampling of data. The slow growth and faster decline of pulse crops during winter helped in their separation from cereals that exhibited the opposite pattern. [PERSON] et al. (2018) [97] used multi-temporal data from Sentinel-1, Sentinel-2 and Landsat-8 to map crops during and at the end of the growing season. This study highlighted the issue of cloud contamination in optical (Sentinel-2 and Landsat-8) data, which the authors addressed using radar data from Sentinel-1. An accuracy of 80% in mapping crops was obtained with only Landsat data, which improved to 85% on addition of Sentinel-1 data. Similar improvements were also seen in the early season crop maps from a combination of Sentinel-1 and -2 data, which led to an accuracy of 93.7% when used in combination. Only two cloud-free scenes from Sentinel-2 provided an accuracy of 87% in comparison to an accuracy of 92% from eight Sentinel-1 images covering the same period. [PERSON] et al. (2019) [98] reviewed Sentinel-1 and Sentinel-2 data for mapping agricultural areas, showing that Sentinel-1 data for agricultural applications is limited by complex image data structures and limited bands as compared to optical data. Sentinel-2 was shown to be the most effective to map crops, providing an overall accuracy of 91% in comparison to 86% and 77% accuracies obtained from the use of L8 and S1 data, respectively. A study by [PERSON] et al. (2018) [99] revealed that Sentinel-2 is superior to Landsat-8 for smallholder agri-mapping and crop discrimination in Spain, providing accuracies close to 90% in comparison to 70% when using Landsat data.