School of Information Engineering, China University of Geosciences, Haidian District, Beijing 100083, China.
96669 Troops, Changping District, Beijing 102208, China.
Sensors (Basel). 2018 Apr 17;18(4):1230. doi: 10.3390/s18041230.
Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of the assimilation framework for continuous monitoring of heavy metal stress in rice. The Harris algorithm was used for the leaf area index (LAI) curves to select the key period for an optimized assimilation. To obtain accurate LAI values, the measured dry weight of rice roots (WRT), which have been proven to be the most stress-sensitive indicator of heavy metal stress, were incorporated into the improved WOFOST model. Finally, the key periods, which contain four dominant time points, were used to select remote sensing images for the RS-WOFOST model for continuous monitoring of heavy metal stress. Compared with the key period which contains all the available remote sensing images, the results showed that the optimal key period can significantly improve the time efficiency of the assimilation framework by shortening the model operation time by more than 50%, while maintaining its accuracy. This result is highly significant when monitoring heavy metals in rice on a large-scale. Furthermore, it can also offer a reference for the timing of field measurements in monitoring heavy metal stress in rice.
准确监测作物中的重金属胁迫对于粮食安全和农业生产至关重要。将遥感图像融入世界粮食研究（WOFOST）模型中提供了一种有效的方法来解决这个问题。在本研究中，我们旨在研究同化框架的关键时期，以实现对水稻中重金属胁迫的连续监测。哈里斯算法用于叶面积指数（LAI）曲线，以选择优化同化的关键时期。为了获得准确的 LAI 值，我们将已被证明是重金属胁迫最敏感指标的水稻根干重（WRT）纳入改进的 WOFOST 模型中。最后，使用包含四个主导时间点的关键时期来选择遥感图像，用于 RS-WOFOST 模型以连续监测重金属胁迫。与包含所有可用遥感图像的关键时期相比，结果表明，最优关键时期可以通过将模型运行时间缩短 50%以上，显著提高同化框架的时间效率，同时保持其准确性。这一结果在大规模监测水稻中的重金属时具有重要意义。此外，它还可以为监测水稻中重金属胁迫时的田间测量时间提供参考。
