IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1490-d1033735.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1490/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/2/1490/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sara Mastrorosa & Mattia Crespi & Luca Congedo & Michele Munafò, 2023. "Land Consumption Classification Using Sentinel 1 Data: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-25, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1490-:d:1033735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.