Abstract
Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.
Highlights
Accurate and timely crop mapping plays a prominent role in food security and economic, political and environmental proposition [1]
The surface reflectance of Green, Red and red-edge band 1 (RE-1) bands is sensitive to the chlorophyll concentration and their temporal information can be used to identify the difference in growing stages among different crops [49,50]
The surface reflectance of shortwave-infrared band 1 (SWIR-1) band is a good indicator for canopy water content which is varied during the different growing stages and significant for croRpemRceolmtaesoStseeinfiSsec.n2as0t. 2i2o00,n210.2,1x2,FxOFROPREPEERERRERVEIEVWIEW
Summary
Accurate and timely crop mapping plays a prominent role in food security and economic, political and environmental proposition [1]. The types and distributions of crops in national and regional scales are crucial for crop area estimation [2,3] and crop yield prediction [4]. Satellite remote sensing has been considered as an advanced technology to obtain crop types and distributions in regional scale since it can provide periodically large-scale observations of ground objects [7]. One is to use the spectral information from a single date satellite imagery during the crop growing season. Crop phenology in the same region can vary among different crop types, i.e., their sowing and harvest dates, their seasonal dynamics and inter-annual growth period are generally different. The unique spectral-temporal features of crops extracted from the time series of remote sensing data have the potential to increase the accuracy of crop classification [13]
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