Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

  • Mingwei Z
  • Qingbo Z
  • Zhongxin C
 et al. 
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Abstract

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series. © 2007 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Crop discrimination
  • Fourier transform
  • MODIS
  • NDVI

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Authors

  • Zhang Mingwei

  • Zhou Qingbo

  • Chen Zhongxin

  • Liu Jia

  • Zhou Yong

  • Cai Chongfa

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