Resources Science ›› 2018, Vol. 40 ›› Issue (10): 2099-2109.doi: 10.18402/resci.2018.10.17

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Integrated maize growth monitoring based on gray correlation analysis and remote sense data in the central plain of Hebei Province

Lei WANG1,2(), Pengxin WANG1,2(), Li LI1,2, Lan XUN1,2, Qingling KONG1,2   

  1. 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Received:2017-10-17 Revised:2017-12-03 Online:2018-10-25 Published:2018-10-20

Abstract:

In order to improve maize growth monitoring accuracy, the central plain of Hebei Province (PR China) was chosen as the study area. The gray correlation analysis method was implemented to calculate the weight coefficients of vegetation temperature condition index (VTCI) and leaf area index (LAI) at four maize growth stages (emergence-jointing, jointing-booting, booting-filling, and filling-mature), as well as the weight coefficients of growth at the 4 stages during maize growth and yield formation. Thus, the integrated growth monitoring indices (G) at the 4 stages and main growth stage could be derived. Linear regression models between maize yields and Gs of counties in the years from 2011 to 2015 were established to analyze the precision of maize growth monitoring. The results showed that the weight coefficients of VTCI at the 4 stages were greater than those of LAI, and the joint-booting stage was the highest, the booting-filling stage was followed. The results further indicated that maize growth and yield formation were likely impacted by water stress, especially at the joint-booting and the booting-filling stage. The weight coefficients of maize growth at the 4 stages were similar. It is noted that growth at the filling-mature stage was the highest. It illustrated that maize growth at the filling-mature stage was most closely related to maize production. Coefficients of determination (R2) of the linear regression analysis between Gs and maize yields of counties in 5 cities all passed the significant test at 0. 01 level, and R2 values were greater than those between maize yields and VTCIs or LAIs. The findings of current study demonstrated a high accuracy of the gs derived from gray correlation analysis method. Based on the maize growth monitoring data, this study implies that the best year was 2011, the worst year were 2014 and 2015, and the maize growth in the western plain was better than the eastern part.

Key words: gray correlation analysis, integrated monitoring, leaf area index, maize growth, vegetation temperature condition index, the central plain of Hebei Province