Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

Lingwen Tian, Xiangnan Liu, Biyao Zhang, Ming Liu and Ling Wu
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Lingwen Tian: School of Information Engineering, China University of Geoscience, Beijing 100083, China
Xiangnan Liu: School of Information Engineering, China University of Geoscience, Beijing 100083, China
Biyao Zhang: School of Information Engineering, China University of Geoscience, Beijing 100083, China
Ming Liu: School of Information Engineering, China University of Geoscience, Beijing 100083, China
Ling Wu: School of Information Engineering, China University of Geoscience, Beijing 100083, China

IJERPH, 2017, vol. 14, issue 9, 1-17

Abstract: The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAI df (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAI df showed stability with an R 2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification.

Keywords: heavy metal stress; remote sensing; time series; WOFOST; EEMD; trend component (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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