干旱区生态环境知识资源中心(Arid): Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain

Arid

DOI	10.3390/agronomy13112800
	Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain
	Zhang, Xiaoyong; Guo, Yonglin; Tian, Xiangyu; Bai, Yongqing
通讯作者	Bai, YQ
来源期刊	AGRONOMY-BASEL
EISSN	2073-4395
出版年	2023
卷号	13 期号:11
英文摘要	Northern Slopes of Tianshan Mountain (NSTM) in Xinjiang hold significance as a principal agricultural hub within the region's arid zone. Accurate crop mapping across vast agricultural expanses is fundamental for intelligent crop monitoring and devising sustainable agricultural strategies. Previous studies on multi-temporal crop classification have predominantly focused on single-point pixel temporal features, often neglecting spatial data. In large-scale crop classification tasks, by using spatial information around the pixel, the contextual relationships of the crop can be obtained to reduce possible noise interference. This research introduces a multi-scale, multi-temporal classification framework centered on ConvGRU (convolutional gated recurrent unit). By leveraging the attention mechanism of the Strip Pooling Module (SPM), a multi-scale spatial feature extraction module has been designed. This module accentuates vital spatial and spectral features, enhancing the clarity of crop edges and reducing misclassifications. The temporal information fusion module integration features from various periods to bolster classification precision. Using Sentinel-2 imagery spanning May to October 2022, datasets for cotton, corn, and winter wheat of the NSTM were generated for the framework's training and validation. The results demonstrate an impressive 93.03% accuracy for 10 m resolution crop mapping using 15-day interval, 12-band Sentinel-2 data for the three crops. This method outperforms other mainstream methods like Random Forest (RF), Long Short-Term Memory (LSTM), Transformer, and Temporal Convolutional Neural Network (TempCNN), showcasing a kappa coefficient of 0.9062, 7.52% and 2.42% improvement in Overall Accuracy compared to RF and LSTM, respectively, which demonstrate the potential of our model for large-scale crop classification tasks to enable high-resolution crop mapping on the NSTM.
英文关键词	remote sensing in agriculture crop mapping deep learning multi-temporal neighborhood information spatial temporal neural networks strip pooling module sentinel-2
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:001108162300001
WOS关键词	IDENTIFICATION ; CLASSIFICATION
WOS类目	Agronomy ; Plant Sciences
WOS研究方向	Agriculture ; Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395250
推荐引用方式 GB/T 7714	Zhang, Xiaoyong,Guo, Yonglin,Tian, Xiangyu,et al. Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain[J],2023,13(11).
APA	Zhang, Xiaoyong,Guo, Yonglin,Tian, Xiangyu,&Bai, Yongqing.(2023).Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain.AGRONOMY-BASEL,13(11).
MLA	Zhang, Xiaoyong,et al."Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain".AGRONOMY-BASEL 13.11(2023).