School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Int J Environ Res Public Health. 2019 Jan 26;16(3):350. doi: 10.3390/ijerph16030350.
Previous studies make it possible to use remote sensing techniques to monitor heavy metal stress of rice synchronously and continuously. However, most studies mainly focus on the analysis of rice's visual symptoms and physiological functions rather than temporal information during the growth period, which may reflect significant changes of rice under heavy metal stress. In this paper, an enhanced spatial and temporal adaptive reflectance fusion model was used to generate synthetic Landsat time series. A normalized difference water index and an enhanced vegetation index were employed to build phenological phase space. Then, the ratio of the rice growth rate fluctuation (GRFI Ratio) was constructed for discriminating the different heavy metal stress levels on rice. Results suggested that the trajectories of rice growth in phenological phase space can depict the similarities and differences of rice growth under different heavy metal stress levels. The most common phenological parameters in the phase space cannot accurately discriminate the heavy metal stress level. However, the GRFI Ratio that we proposed outperformed in discriminating different levels of heavy metal stress. This study suggests that this framework of detecting the heavy metal pollution in paddy filed based on phenological phase space and temporal profile analysis is promising.
先前的研究使得利用遥感技术同步和连续监测水稻重金属胁迫成为可能。然而，大多数研究主要集中于分析水稻的视觉症状和生理功能，而不是生长期间的时间信息，这可能反映了水稻在重金属胁迫下的显著变化。在本文中，使用增强的时空自适应反射融合模型来生成合成的 Landsat 时间序列。归一化差异水指数和增强植被指数被用来构建物候相位空间。然后，构建了水稻生长率波动比（GRFI Ratio），用于区分水稻不同重金属胁迫水平。结果表明，物候相位空间中水稻生长的轨迹可以描述不同重金属胁迫水平下水稻生长的相似性和差异性。在相位空间中最常见的物候参数不能准确区分重金属胁迫水平。然而，我们提出的 GRFI Ratio 在区分不同水平的重金属胁迫方面表现更好。本研究表明，基于物候相位空间和时间剖面分析来检测稻田重金属污染的这种框架是有前景的。
