Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery

Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types-winter garlic, winter canola and winter wheat-was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing.

Automated summary

This paper evaluates the potential of using Sentinel-2 data to map winter crops in the early growth stage, demonstrating that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. With an overall classification accuracy of 96.62%, Sentinel-2 images prove to be an important data source for accurately identifying these winter crops in the field of agricultural remote sensing.