干旱区生态环境知识资源中心(Arid): Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China

Arid

DOI	10.3390/rs12244052
	Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China
	Yi, Zhiwei; Jia, Li; Chen, Qiting
通讯作者	Chen, QT (corresponding author), Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:24
英文摘要	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.
英文关键词	crop classification sentinel-2 random forest red-edge band short-wave infrared
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000603214400001
WOS关键词	MACHINE LEARNING ALGORITHMS ; TIME-SERIES ; RANDOM FOREST ; LAND-COVER ; FEATURE-SELECTION ; WATER-CONTENT ; MODIS DATA ; NDVI DATA ; VEGETATION ; PERFORMANCE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349160
作者单位	[Yi, Zhiwei; Jia, Li; Chen, Qiting] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Yi, Zhiwei] Univ Chinese Acad Sci, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Yi, Zhiwei,Jia, Li,Chen, Qiting. Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China[J],2020,12(24).
APA	Yi, Zhiwei,Jia, Li,&Chen, Qiting.(2020).Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China.REMOTE SENSING,12(24).
MLA	Yi, Zhiwei,et al."Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China".REMOTE SENSING 12.24(2020).