RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2020, Vol. 29 >> Issue (10): 2296-2306.doi: 10.11870/cjlyzyyhj202010018

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Extraction and Dynamic Monitoring of Winter Wheat Area Based on Zoning with EVI Similarity

YANG Huan 1, DENG Fan 1, FU Han-cong 1, XU Nuo 1 , LI Cai-xia 1 , ZHANG Jia-hua 2   

  1. (1. School of Geosciences, Yangtze University, Wuhan 430100, China;
    2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China)
  • Online:2020-10-20 Published:2020-11-18

Abstract: Timely and accurately obtaining the planting area of winter wheat and its spatio-temporal change is very important for improving the accuracy of crop yield estimation and food security ensurance. The phenological period of winter wheat is greatly affected by the geographical conditions and human factors, and the extraction accuracy of planting area in a large area by remote sensing is easily affected by the phenological differences. Using MODIS EVI and Landsat as data sources, the winter wheat planting area was extracted by comparing the reference time series with the time series which were to be classified after zoning the study area with winter wheat EVI similarity and elevation information, and the spatial-temporal change process of winter wheat planting area in Hubei Province from 2009 to 2017 was analyzed. The results show that: (1)The average extraction accuracy of winter wheat area was 95.3% with city-level R 2 greater than 0.90, which was better than the extraction accuracy that of  no-zoning; (2) Winter wheat was widely distributed in the middle and northern part of the study area, especially in Xiangyang County and Zaoyang City; (3) There was an obvious spatial-temporal pattern of expansion to the Northwest and Southeast for the spatial distribution of winter wheat; (4)The planting area of winter wheat showed an overall upward trend with an increase of 4% in 2017 compared with 2009, which increased first and then decreased from 2009 to 2017. The method of crop area extraction based on EVI similarity can improve the accuracy of planting area and provide an effective method for remote sensing monitoring of the spatial-temporal change of winter wheat area in a large area.

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