Tian Lingwen, Liu Xiangnan, Zhang Biyao, Liu Ming, Wu Ling
School of Information Engineering, China University of Geoscience, Beijing 100083, China.
Int J Environ Res Public Health. 2017 Sep 6;14(9):1018. doi: 10.3390/ijerph14091018.
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 (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 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.
利用遥感技术诊断作物中的重金属胁迫对环境保护和粮食安全具有重要意义。然而，在自然农田生态系统中，各种胁迫因素可能对作物生长产生类似影响，因此难以准确识别重金属胁迫，所以这仍然是一个尚未得到很好解决的科学问题，也是农业遥感领域的一个热点话题。本研究提出了一种利用总体经验模态分解（EEMD）在长时间尺度上获取重金属胁迫信号特征的方法。该方法基于增强型世界粮食研究（WOFOST）模型模拟并与遥感数据同化的叶面积指数（LAI）进行操作。得到了以下结果：（i）使用EEMD通过消除年内和年际分量有效地提取了重金属胁迫信号；（ii）LAI（年际分量与残差之和的一阶导数）能够较好地反映水稻对重金属胁迫的稳定特征响应。LAI在三个生长阶段表现出稳定性，决定系数R²大于0.9，且在6月稳定性最佳。本研究将胁迫效应的光谱特征与时间特征相结合，证实了长期遥感数据在提高作物重金属胁迫识别准确性方面的潜力。
