Next Article in Journal
Multimorbidity in Chronic Conditions: Public Primary Care Patients in Four Greater Mekong Countries
Previous Article in Journal
Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Int. J. Environ. Res. Public Health 2017, 14(9), 1018; doi:10.3390/ijerph14091018

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

School of Information Engineering, China University of Geoscience, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 15 July 2017 / Revised: 31 August 2017 / Accepted: 3 September 2017 / Published: 6 September 2017
(This article belongs to the Section Environmental Science and Engineering)
View Full-Text   |   Download PDF [3471 KB, uploaded 6 September 2017]   |  

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) LAIdf (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAIdf showed stability with an R2 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. View Full-Text
Keywords: heavy metal stress; remote sensing; time series; WOFOST; EEMD; trend component heavy metal stress; remote sensing; time series; WOFOST; EEMD; trend component
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Tian, L.; Liu, X.; Zhang, B.; Liu, M.; Wu, L. Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition. Int. J. Environ. Res. Public Health 2017, 14, 1018.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top