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

作     者：TAO Jian-bin WU Wen-bin ZHOU Yong WANG Yu JIANG Yan 

作者机构：Key Laboratory of Agri-informaticsMinistry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental SciencesCentral China Normal University 

出 版 物：《Journal of Integrative Agriculture》 (农业科学学报（英文版）)

年 卷 期：2017年第16卷第2期

页      面：348-359页

核心收录：

学科分类：082804[工学-农业电气化与自动化] 08[工学] 09[农学] 0710[理学-生物学] 0832[工学-食品科学与工程（可授工学、农学学位）] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 0828[工学-农业工程] 0905[农学-畜牧学] 0906[农学-兽医学] 0901[农学-作物学] 0703[理学-化学] 0902[农学-园艺学] 0713[理学-生态学] 

基　　金：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 

主　　题：time-series MODIS data phenological feature peak before wintering winter wheat mapping 

摘      要：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.