Journal of Resources and Ecology ›› 2022, Vol. 13 ›› Issue (2): 196-209.DOI: 10.5814/j.issn.1674-764x.2022.02.003

• Ecosystem and Climate Change • Previous Articles     Next Articles

Spatio-temporal Analysis of Changes in Winter Wheat Cold Damage with Meteorological Elements in Huang-Huai-Hai Plain from 2011 to 2020

ZHENG Xintong1,2(), XIE Chuanjie1,*(), HE Wei1,2, LIU Gaohuan1   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-02-05 Accepted:2021-06-30 Online:2022-03-30 Published:2022-03-09
  • Contact: XIE Chuanjie
  • About author:ZHENG Xintong, E-mail:
  • Supported by:
    The National Key Research and Development Program of China(2017YD0300403)


The Huang-Huai-Hai Plain is one of the typical agri-ecosystems in China, which suffers from cold damage frequently resulting in substantial economic losses. In order to monitor the changes in the occurrence of cold damage in an effective and large-scale manner, and to determine their meteorological causes, this paper collected low temperature data from the agricultural meteorological stations and remote sensing data of MODIS from 2005 to 2015, and constructed a monitoring model of cold damage to winter wheat in Huang-Huai-Hai Plain based on the Logistic regression model. This model was used to analyze the spatio-temporal changes of cold damage of winter wheat in Huang-Huai-Hai Plain from 2011 to 2020, and correlation analysis was performed with the spatio-temporal changes of meteorological factors to ascertain how they affect cold damage. The results show that the harm from cold damage in winter wheat has been gradually decreasing from 2011 to 2020, and the cold damage areas with high probability and high frequency are moving from north to south. The meteorological elements with the greatest impacts on the degree of cold damage from stronger to weaker are heat, precipitation and sunshine duration, whose influence has spatial variability.

Key words: cold damage, meteorological elements, Logistic regression, Pearson correlation analysis