Abstract
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.
Highlights
We can see that normalised difference yellowness index (NDYI) has the potential to distinguish winter canola from other land cover types during the canola flowering stage
This study demonstrates that Sentinel-2 images can be used for early season identification of winter crops due to their high temporal and spatial resolution
This study analysed the spectral characteristics of winter canola, winter garlic and winter wheat as represented in Sentinel-2 images
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Food security is a very important global issue [1]. Detailed data on the spatio-temporal distributions of crop plots are vital for guaranteeing food security [2]. The continuous development of remote sensing technologies, such as classification algorithms and satellite or unmanned aerial vehicle (UAV) imagery, provides many potential solutions for mapping crop types [3,4,5,6,7]. Mapping crop plots in the early growth stage is very helpful for informing decision-making related to food security and other policies [8,9] because such early season crop maps are the basis of crop yield and drought risk predictions. Early season mapping has not received enough attention because the most common approach to the mapping of crop types relies on the relationship between full-season image features and crop type classes
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