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Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China

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  • Xiaolei Wang

    (The School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450000, China
    Joint Laboratory of Eco-Meteorology, Chinese Academy of Meteorological Sciences, Zhengzhou University, Zhengzhou 450000, China)

  • Mei Hou

    (The School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450000, China)

  • Shouhai Shi

    (The School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450000, China)

  • Zirong Hu

    (The School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450000, China)

  • Chuanxin Yin

    (The School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450000, China)

  • Lei Xu

    (National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China)

Abstract

As a major world crop, the accurate spatial distribution of winter wheat is important for improving planting strategy and ensuring food security. Due to big data management and processing requirements, winter wheat mapping based on remote-sensing data cannot ensure a good balance between the spatial scale and map details. This study proposes a rapid and robust phenology-based method named “enhanced time-weighted dynamic time warping” (E-TWDTW), based on the Google Earth Engine, to map winter wheat in a finer spatial resolution, and efficiently complete the map of winter wheat at a 10-m resolution in Henan Province, China. The overall accuracy and Kappa coefficient of the resulting map are 97.98% and 0.9469, respectively, demonstrating its great applicability for winter wheat mapping. This research indicates that the proposed approach is effective for mapping large-scale planting patterns. Furthermore, based on comparative experiments, the E-TWDTW method has shown excellent robustness across lower quantities of training data and early season extraction ability. Therefore, it can provide early data preparation for winter wheat planting management in the early stage.

Suggested Citation

  • Xiaolei Wang & Mei Hou & Shouhai Shi & Zirong Hu & Chuanxin Yin & Lei Xu, 2023. "Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1490-:d:1033735
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    References listed on IDEAS

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    1. Jing Tang & Xiaoyong Zhang & Zhengchao Chen & Yongqing Bai, 2022. "Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    2. Zhiwen Yang & Hebing Zhang & Xiaoxuan Lyu & Weibing Du, 2022. "Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
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