Journal of Integrative Agriculture ›› 2017, Vol. 16 ›› Issue (02): 348-359.DOI: 10.1016/S2095-3119(15)61304-1

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  • 收稿日期:2015-10-23 出版日期:2017-02-20 发布日期:2017-01-31

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

TAO Jian-bin1, 2, WU Wen-bin1, 2, ZHOU Yong2, WANG Yu2, JIANG Yan2   

  1. 1 Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China

    2 Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, P.R.China

  • Received:2015-10-23 Online:2017-02-20 Published:2017-01-31
  • Contact: WU Wen-bin, E-mail: wuwenbin@caas.cn
  • About author:TAO Jian-bin, E-mail: taojb@mail.ccnu.edu.cn
  • Supported by:

    This work was supported by the open research fund of the Key Laboratory of Agri-informatics, Ministry of Agriculture and the fund of Outstanding Agricultural Researcher, Ministry of Agriculture, China.

Abstract: By employing the unique phenological feature of winter wheat extracted from peak before winter (PBW) and the advantages of moderate resolution imaging spectroradiometer (MODIS) data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data.  First, a phenological window, PBW was drawn from time-series MODIS data.  Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information.  Finally, a regression model was built to model the relationship of the phenological feature and the sample data.  The amount of information of the PBW was evaluated and compared with that of the main peak (MP).  The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data.  These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale.  Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies.  This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Key words: time-series MODIS data, phenological feature, peak before wintering\ winter wheat mapping